Adaptive On-Line Page Importance Computation

Adaptive On-Line Page Importance Computation

Serge Abiteboul

Domaine de Voluceau, 78150 Rocquencourt, France

(also works at Xyleme)

Mihai Preda

Xyleme SA
6 rue Emile Verhaeren, 92210 Saint-Cloud, France

Grgory Cobena

Domaine de Voluceau, 78150 Rocquencourt, France

(also works at Xyleme)


The computation of page importance in a huge dynamic graph has recently attracted a lot of attention because of the web. Page importance, or page rank is defined as the fixpoint of a matrix equation. Previous algorithms compute it off-line and require the use of a lot of extra CPU as well as disk resources (e.g. to store, maintain and read the link matrix). We introduce a new algorithm OPIC that works on-line, and uses much less resources. In particular, it does not require storing the link matrix. It is on-line in that it continuously refines its estimate of page importance while the web/graph is visited. Thus it can be used to focus crawling to the most interesting pages. We prove the correctness of OPIC. We present Adaptive OPIC that also works on-line but adapts dynamically to changes of the web. A variant of this algorithm is now used by Xyleme.
We report on experiments with synthetic data. In particular, we study the convergence and adaptiveness of the algorithms for various scheduling strategies for the pages to visit. We also report on experiments based on crawls of significant portions of the web.


Hyperlink graph analysis and its application to search and ranking.


An automated web agent visits the web, retrieving pages to perform some processing such as indexing, archiving, site checking, etc., [3,11,24]. The robot uses page links in the retrieved pages to discover new pages. All the pages on the web do not have the same importance. For example, Le Louvre homepage is more important that an unknown person's homepage. Page importance information is very valuable. It is used by search engines to display results in the order of page importance [11]. It is also useful for guiding the refreshing and discovery of pages: important pages should be refreshed more often[*] and when crawling for new pages, important pages have to be fetched first [6]. Following some ideas of [16], Page and Brin proposed a notion of page importance based on the link structure of the web [21]. This was then used by Google with a remarkable success. Intuitively, a page is important if there are many important pages pointing to it. This leads to a fixpoint computation by repeatedly multiplying the matrix of links between pages with the vector of the current estimate of page importance until the estimate is stable, i.e., until a fixpoint is reached.
The main issue in this context is the size of the web, billions of pages [
15,23]. Techniques have been developed to compute page importance efficiently, e.g., [12]. The web is crawled and the link matrix computed and stored. A version of the matrix is then frozen and one separate process computes off-line page importance, which may take hours or days for a very large graph. So, the core of the technology for the off-line algorithms is fast sparse matrix multiplication (in particular by extensive use of parallelism). This is a classical area, e.g., [25]. The algorithm we propose computes the importance of pages on-line, with limited resources, while crawling the web. It can be used to focus crawling to the most interesting pages. Moreover, it is fully integrated in the crawling process, which is important since acquiring web pages is the most costly part of the system.
Intuitively speaking, some ``cash'' is initially distributed to each page and each page when it is crawled distributes its current cash equally to all pages it points to. This fact is recorded in the history of the page. The importance of a page is then obtained from the ``credit history'' of the page. The intuition is that the flow of cash through a page is proportional to its importance. It is essential to note that the importance we compute does not assume anything about the selection of pages to visit. If a page ``waits'' for a while before being visited, it accumulates cash and has more to distribute at the next visit. In Section 
1 and 2, we present a formal model and we prove the correctness of the algorithm.
In practice, the situation is more complex. First, the ranking of result pages by a search engine should be based on other factors than page importance. One may use criteria such as the occurrences of the words from the query and their positions. These are typically criteria from information retrieval [
26] that have been used extensively since the first generation of search engines, e.g. [3]. One may also want to bias the ranking of answers based on the interest of users [19,22]. Such interesting aspects are ignored here. On the other hand, we focus on another critical aspect of page importance, the variations of importance when the web changes.
The web changes all the time. With the off-line algorithm, we need to restart a computation. Although techniques can be used to take into account previous computations, several costly iterations over the entire graph have to be performed by the off-line algorithm. We show how to modify the on-line algorithm to adapt to changes. Intuitively, this is achieved by taking into account only a recent window of the history.
Several variants of the adaptive on-line algorithm are presented. A distributed implementation of one of them is actually used by the Xyleme crawlers [
27,28]. The algorithms are described using web terminology. However, the technique is applicable in a larger setting to any graph. Furthermore, we believe that distributed versions of the on-line algorithm could be useful in network applications when a link matrix is distributed between various sites.
We also mention studies that we conducted with librarians from the French national Library to decide if page importance can be used to detect web sites that should be archived. More precisely, we discuss some experiments and we detail how to use our system to support new criteria of importance, such as site-based importance.
An extended abstract of this work was published in [
2]. A short and informal presentation of the algorithm is given there. The formal presentation, the details of the results as well as the discussion of the experiments are new.
The paper is organized as follows. We first present the model and in particular, recall the definition of importance. In Section
2, we introduce the algorithm focusing on static graphs. In Section 3, we consider different crawling strategies and we move to dynamic graphs, i.e., graphs that are continuously updated like the web. The following section deals with implementation and discusses some experiments. The last section is a conclusion.


In this section, we present the formal model. Reading this section is not mandatory for the comprehension of the rest of the paper.

The web as a graph

We view the World Wide Web as a directed graph $G$. The web pages are the vertices. A link from one page to another form a directed edge. We say that a directed graph $G$ is connected if, when directed edges are transformed into non directed edges, the resulting graph is connected in the usual sense. A directed graph $G$ is said to be strongly connected if for all pair of vertices $i,j$ there exists a directed path going from $i$ to $j$ following the directed edges of $G$. A graph is said to be aperiodic if there exists a $k$ such that for all pair of vertices $i,j$ there exists a directed path of length exactly $k$ going from $i$ to $j$ following the directed edges of $G$. Thus aperiodicity implies strong connectedness. When the web graph is not connected, each connected components may be considered separately.

A graph as a matrix

Let $G$ be any directed graph with $n$ vertices. Fix an arbitrary ordering between the vertices. $G$ can be represented as a matrix $L[1..n, 1..n]$ such that:
  • $L$ is nonnegative, i.e. $\forall i, \forall j, L[i,j]\geq0$
  • $L[i,j]>0$ if and only if there is an edge from vertex $i$ to vertex $j$.
There are several natural ways to encode a graph as a matrix, depending on what property is needed afterwards. For instance, Google [21,
19] defines the out-degree $d[i]$ of a page as the number of outgoing links, and set $L[i,j]=1/d[i]$ if there is a link from $i$ to $j$. In [16], Kleinberg proposes to set $K[i,j]=1$ if there is a link from $i$ to $j$, but then sets $L=K^{T}*K$ (where $K^{T}$ is the transpose of matrix $K$).


The basic idea is to define the importance of a page in an inductive way and then compute it using a fixpoint. If the graph contains $n$ nodes, the importance is represented as a vector $\bar x$ in a $n$ dimensional space. We consider three examples, in which the importance is defined inductively by the equation $\bar x_{k+1} = L \bar x_k$:
  • If one decides that a page is important if it is pointed by important pages. Then set $L[i,j]=1$ iff there is an edge between $i$ and $j$.
  • A 'random walk' means that we browse the web by following one link at a time, and all outgoing links of a page have equal probability to be chosen. If one decides that a page importance is the probability to read it during a 'random walk' on the web, then set $L[i,j]=1/d[i]$ iff there is a edge between $i$ and $j$. The random walk probabilities correspond to the Markov chain with generator $L$. This definition of $L$ will result in the definition of importance as in Google Pagerank.
  • If one decides that a page is important if it is pointed by important pages or points to important pages. Then set $L[i,j]=1$ iff there is an edge between $i$ and $j$ or an edge between $j$ and $i$. This is related to the work of Kleinberg.
In all cases, this leads to solving by induction an equation of the type $\bar x_{k+1} = L \bar x_k$ where $L$ is a nonnegative matrix. This may be achieved by iterating over $x_k$. Unfortunately, for obvious modulus reasons, this is very likely to diverge or to converge to zero. Observe that we are only interested in the relative importance of pages, not their absolute importance. This means that only the direction of $x_k$ is relevant, not its norm. Thus it is more reasonable to consider the following induction (equivalent for importance computation), which uses the previous induction step but renormalizes after each step :

\begin{displaymath}\bar x_{k+1} = \frac{L \bar x_k}{\parallel L \bar x_k \parallel}~~~(\dag )\end{displaymath}

Computing the importance of the pages thus corresponds to finding a fixpoint $\bar x$ to $(\dag )$, each $i^{th}$ coordinate of $x$ being the importance of page $i$. By definition, such a fixpoint is an eigenvector of $L$ with a real positive eigenvalue. If $\bar x_0$ is a linear combination of all eigenvector having a real positive eigenvalue then it is easy to see that $(\dag )$ will converge to the eigenspace corresponding to the dominant eigenvalue (i.e. which is maximal). Thus, unless $x_0$ is not general enough (e.g. not zero), the importance corresponds to an eigenvector of $L$ which eigenvalue is a positive real and which modulus is maximal among all other eigenvalue. For each nonnegative matrix $L$, there always exists such an eigenvector (see Perron-Frobenius Theorem 
1.1) but several problems may occur:
  • There might be several solutions. This happens when the vector space corresponding to the maximal eigenvalue has a dimension greater than 1.
  • Even if there is a unique solution, the iteration $(\dag )$ may not converge when the graph does not have some desired properties.
All these cases are completely characterized in the Theorem of Perron-Frobenius that we give next.

Theorem 1.1   Perron-Frobenius [10] Let $L$ be an nonnegative matrix corresponding to a graph $G$.
  • There exists an eigenvalue $r$ which is real positive and which is greater than the modulus of any other eigenvalue.
  • If $G$ is strongly connected then the vector space for $r$ is of dimension 1.
  • If $G$ is aperiodic and $\bar x_0$ general enough then the induction $(\dag )$ converges towards the eigenvector for $r$ of modulus 1[*].

In order to solve the convergence problem, Google [
11] uses the following patch. Recall that $L$ is defined in this case by $L[i,j]=1/d[i]$ iff there is an edge from $i$ to $j$. A new matrix $L'$ is defined such that $L'[i,j]=L[i,j] + \epsilon$ where $\epsilon$ is a small real. Then the fixpoint is computed over $L'$ instead of $L$. Note that $L'$ corresponds to a new graph $G'$ which is $G$ plus a ``small'' edge for any pair $i,j$. Observe that the new graph $G'$ is strongly connected and aperiodic thus the convergence of $(\dag )$ is guaranteed by Theorem 1.1. For each $\epsilon$, this gives an importance vector $\bar x_\epsilon$. It is not hard to prove that when epsilon goes to zero, $\bar x_\epsilon$ converges to an eigenvector of $L$ with a maximal real positive value. Thus, for epsilon small enough, $\bar x_\epsilon$ may be seen as a good approximation of the importance. For some mysterious reason, Google sets $\epsilon$ to $0.2$ [*].

Another way to cope with the problem of convergence is to consider the following convergence suite :

\begin{displaymath}(\dag ') ~~~\bar y_{n+1} = \frac{Ly_n + y_n}{\parallel Ly_n + y_n \parallel}\end{displaymath}

If $r$ is the maximal eigenvalue of a nonnegative matrix $L$ then $r+1$ can be shown to be the maximal eigenvalue of $L+I$. Thus, a solution $\bar y$ of $(\dag ')$ is also a solution of $\dag $. If $L$ is strongly connected then $L+I$ is aperiodic and thus $(\dag ')$ converges towards the importance. If $L$ is not strongly connected there might be several linearly independent eigenvector, but still it is easy to show that $(\dag ')$ converges towards the projection of $\bar x_0$ on the eigenspace corresponding to all solutions.

On the Web

The computation of page importance in a huge dynamic graph has recently attracted a lot of attention because of the web, e.g., [
18,21,19,22,9]. It is a major issue in practice that the web is not strongly connected. For instance, in the bow tie [4] vision of the web, the in nodes do not branch back to the core of the web. Although the same computation makes sense, it would yield a notion of importance without the desired semantics. Intuitively, the random walk will take us out of the core and would be ``trapped'' in pages that do not lead back to the core (the ``rank sink'' according to [21]). So, pages in the core (e.g., the White House homepage) would have a null importance. Hence, enforcing strong connectivity of the graph (by ``patches'') is more important from a semantic point of view than for mathematical reasons. In a similar way to Google, we enforce the strong connectivity of the graph by introducing ''small'' edges. More precisely, in our graph, each node points to a unique virtual page. Conversely, this virtual page points to all other nodes.

Our Algorithm

Our algorithm computes the characteristic vector of $(\dag ')$, and doesn't require any assumption on the graph. In particular, it works for any link matrix $L$ assuming that $L$ can be read line by line. More precisely, for each page $i$ that is read, we use the values $L[i,j]$ where $L[i,j]>0$. For instance, in Google's link matrix, these values corresponds to outgoing links (the pages $j$ pointed by page $i$), which are known at little cost by parsing the HTML file. However, the cost may be higher in some other cases (e.g., when $L[i,j]>0$ represents incoming links, we need to store and read an index of links). In terms of convergences, the different cases are characterized in a similar way as previously, e.g. if $G$ is strongly connected, the solution is unique and independent of the initial vector $x_0$. Previous work is abundant in the area of Markov chains and matrix fixpoint computations, e.g. [
7] or [18]. In most cases, infinite transition matrix are managed by increasing the size of a known matrix block. Some works also consider a changing Web graph, e.g. an incremental computation of approximations of page importance is proposed in [5]. As far as we know, our algorithm is new. In particular:
  • it may start even when a (large) part of the matrix is still unknown,
  • it helps deciding which (new) part of the matrix should be acquired (or updated),
  • it is integrated in the crawling process,
  • it works on-line even while the graph is being updated.
For instance, after crawling $400$ million pages on the web, we have a relatively precise approximation of page importance for over $1$ billion pages, i.e., even of parts of the matrix that we do not know yet. A drawback for our algorithm is that it is strictly tailored to the computational cost model of crawling the Web, and in other cases converges slower than others after reading the same pages.

Static graphs: OPIC

We consider in this section the case of a static graph (no update). We describe the algorithm for Google's link matrix $L$ as defined previously. It can be generalized to work for other link matrices. We present the OPIC algorithm and show its correctness. We briefly discuss the advantages of the technique over the off-line algorithm. We will consider dynamic graphs in the next section.

Informal description

For each page (each node in the graph), we keep two values. We call the first cash. Initially, we distribute some cash to each node, e.g., if there are $n$ nodes, we distribute $1/n$ to each node. While the algorithm runs, the cash of a node records the recent information discovered about the page, more precisely, the sum of the cash obtained by the page since the last time it was crawled. We also record the (credit) history of the page, the sum of the cash obtained by the page since the start of the algorithm until the last time it was crawled. The cash is typically stored in main memory whereas the history may be stored on disk. When a page $i$ is retrieved by the web agent, we know the pages it points to. In other words, we have at no cost the outgoing links information for the retrieved page. We record its cash in the history, i.e., we add it to the history. We also distribute this cash equally between all pages it points to. We reset the cash of the page $i$ to 0. This happens each time we read a page. We will see that this provides enough information to compute the importance of the page as used in standard methods. We will consider in a further section how this may be adapted to handle dynamic graphs.

Detailed description

We use two vectors $C[1..n]$ (the cash) and $H[1..n]$ (the history). The initialization of $C$ has no impact on the result. The history of a page is simply a number. A more detailed history will be needed when we move to an adaptive version of the algorithm. Let us assume that the history $H$ is stored on disk and $C$ is kept in main memory. In order to optimize the computation of $\vert H\vert=\sum_i{H[i]}$, a variable $G$ is introduced so that $G=\vert H\vert$ at each step. The algorithm is as follows:
On-line Page Importance Computation

for each i let C[i] := 1/n ;
for each i let H[i] := 0 ;
let G:=0 ;
do forever
  choose some node i ;
  %% each node is selected
  %% infinitely often

  H[i] += C[i];
  %% single disk access per page

  for each child j of i,
    do C[j] += C[i]/out[i];
  %% Distribution of cash
  %% depends on L

  G += C[i];
  C[i] := 0 ;
At each step, an estimate of any page $k$'s importance is $(H[k]+C[k]) / (G+1)$. Note that the algorithm does not impose any requirement on the order we visit the nodes of the graph as long as each node is visited infinitely often (some minimal fairness). This is essential since crawling policies are often governed by considerations such as robots exclusion, politeness (avoid rapid-firing), page change rate, focused crawling. As long as the cash of children is stored in main memory, no disk access is necessary to update it. At the time we visit a node (we crawl it), the list of its children is available on the document itself and does not require disk access.

Each page has at least one child, thanks to the ``small'' edges that we presented in previous Section (and that points to the virtual page). However, for practical reasons, the cash of the virtual page is not distributed all at once. This issue is in particular related to the discovery of new pages and management of variable sized graphs that we consider later.

Definition 2.1   We note $C_t$ and $H_t$ the values of vectors $C$ and $H$ at the end of the $t$-th step of the algorithm. The vector $C_0$ denotes the value of vector $C$ at initialization (all entries are $1/n$). Let $X_t$ be defined by:

\begin{displaymath}X_t = \frac{H_t}{\vert H_t\vert}\end{displaymath}


\begin{displaymath}\forall j, X_t[j]= \frac{H_t[j]}{(\sum_i H_t[i])} \end{displaymath}

One can prove that:

Theorem 2.1   Assuming the graph is connected, when $t$ goes to infinity, $\vert H_t\vert$ goes to infinity and

\begin{displaymath}\left\vert (L'*X_t)-X_t \right\vert < \frac{1}{\vert H_t\vert} \end{displaymath}

and $\vert X_t\vert=1$. Thus the vector $X_t$ converges to the vector of importance, i.e.,

\begin{displaymath}X_{Importance} = lim_{t\rightarrow +\infty}X_t \end{displaymath}

To prove this theorem, we use the three following lemmas:

Lemma 2.2   The total amount of all cash is constant and equal to the initial value, i.e., for each $t$, \( \sum_{i=1}^{n} C_t[i]=\sum_{i=1}^{n}C_0[i]=1\)

This is obvious by induction since we only distribute each node cash among the children.

Lemma 2.3   After each step $t$, we have for each page $j$,

\begin{displaymath}H_t[j]+C_t[j] = C_0[j] + \sum_{(i~ancestor~of~j)} ( \frac{L[i,j]}{out[i]} *H_t[i] ) \end{displaymath}

The proof by induction is given in appendix. It works by considering two cases: either $j$ is read, or another page is read.

Lemma 2.4   If all pages are infinitely read, $\sum_j{H_t[j]}$ goes to infinity.

For this, we must prove that there is $e>0$ such that starting at any time $t$, $\sum_j{H_t[j]}$ will eventually increase of $e$. Consider $e=1/n$, i.e. $e$ is the average value of cash on all pages. At time $t$, there is a page $j$ having more than $e$ cash. The cash of page $j$ can not decrease until $j$ is read. This page will be read one more time after $t$ because all pages are read infinitely often. Thus, the history of the page will increase of at least $e$ when page $j$ is read, and this will increase $\sum_j{H_t[j]}$. Now, we can prove, as shown in appendix, that:

Lemma 2.5   $\lim_{t\rightarrow +\infty}\vert L'*X_t-X_t\vert=0 $

By Lemma 2.5, $X_t$ go infinitely close to a characteristic vector of $L$ of the dominant characteristic value $r$. This suggests using $X_t=H_t/G$ as an estimate of page importance. We can add $1$ (i.e. $\sum_i C_t[i]$) to the denominator $G$ by using the cash accumulated since last crawl, and thus have (on average) a marginally better estimate. More precisely, one can use for page $j$,

\begin{displaymath}\frac{H_t[j]+C_t[j]}{( \sum_i H_t[i]) + 1} \end{displaymath}

Advantages over the off-line algorithms:

The main advantage of our algorithm is that it allows focused crawling. Because our algorithm is run online and its results are immediately available to the crawler, we use it to focus crawling to the most interesting pages for the users. This is in particular interesting in the context of building a web archive [1], when there are strong requirements (and constraints) on the crawling process. Moreover, since we don't have to store the matrix but only a vector, our algorithm presents the following advantages:
  1. It requires less storage resources than standard algorithms.
  2. It requires less CPU, memory and disk access than standard algorithms.
  3. It is easy to implement.
Our algorithm is also well adapted to ``continuous'' crawl strategies. The reason is that storing and maintaining the link matrix during a ``continuous'' crawl of the Web (when pages are refreshed often) is significantly more expensive than for single ``snapshot'' crawl of the Web (when each page is read only once). Indeed, when information about specific pages has to be read and updated frequently, the number of random disk access may become a limiting factor. In our experiment for instance, the crawler was retrieving hundreds of pages per seconds on each PC (see Section 
4). However, note that the storage of a link matrix may be useful beyond the computation of page importance. For instance, given a page $p$, Google provides the list of pages pointing to it. This means that the matrix (or its transpose) is maintained in some form. Another usage of the link matrix is exhibited in [14].

Crawling Strategies

In this section, we first consider different crawling strategies that impact the convergence of our algorithm. Then, we study how they can be used in the case of a changing graph. Implementations aspects and experiments are considered in the next section.

On convergence

As previously mentioned, the error in our estimate is bounded by $\frac{1}{\vert H_t\vert}$. Let us call

\begin{displaymath}\frac{1}{G_{t}}=\frac{1}{\vert H_t\vert}=1/{\sum_k H_{t}[k]}\end{displaymath}

the error factor, although this is, strictly speaking, not the error (but an upper bound for it). Now, in principle, one could choose a very bad strategy that would very often select pages with very low cash. (The correctness of the algorithm requires that each page is read infinitely many times but does not require the page selection strategy to be smart.) On the other hand, if we choose nodes with very large cash, the error factor decreases faster. To illustrate, consider three page selection strategies:
  1. Random : We choose the next page to crawl randomly with equal probability. (Fairness: for each $t_0$, the probability that a page will be read at some $t>t_0$ goes to 1 when $t$ goes to infinity.)
  2. Greedy : We read next the page with highest cash. This is a greedy way to decrease the value of the error factor. (Fairness: For a strongly connected graph, each page is read infinitely often because it accumulates cash until it is eventually read. See Lemma 6.2 in appendix).
  3. Cycle : We choose some fixed order and use it to cycle around the set of pages. (Fairness is obvious.) We considered this page selection strategy simply to have a comparison with a systematic strategy. Recall that systematic page selection strategies impose undesired constraints on the crawling of pages.

Remark 3.1   (Xyleme ) The strategy for selecting the next page to read used in Xyleme is close to Greedy . It is tailored to optimize our knowledge of the web [20], the interest of clients for some portions of the web, and the refreshing of the most important pages that change often.

To get a feeling of how Random and Greedy progress, let us consider some estimates of the values of the error factor for these two page selection strategies. Suppose that at initialization, the total value of the cash of all pages is $1$ and that there are $n$ pages. Then:
  • Random : The next page to crawl is chosen randomly so its cash is on average $\frac{1}{n}$. Thus, the denominator of the error factor is increased by $\frac{1}{n}$ on average per page.
  • Greedy : A page accumulates cash until it reaches the point where it is read. Let $\alpha$ be the average cash of a page at the time it is read. On average, the cash of the page is $\alpha/2$ if we suppose that cash is accumulated linearly by pages until they are read. This result has been confirmed by experiments. Since the total of the cash is $1$, this shows that $\alpha$ is $2*(1/n)$. Thus the denominator of the error factor is increased by $\frac{2}{n}$ on average per page read. This result has also been confirmed by experiments, the average ``cash'' value of crawled pages converges to $\frac{2}{n}$ after crawling a few thousand pages.
Thus the error factor decreases on average twice faster with Greedy than with Random . We will see with experiments (in Section
4) that, indeed, Greedy converges faster. Moreover, Greedy focuses our resources on the important pages which corresponds to users interest. On these pages, the error factor of greedy Greedy decreases even faster.

A changing graph

Consider now a dynamic graph (the case of the web). Pages come and disappear and edges too. Because of the time it takes to crawl the Web (weeks or months), our knowledge of the graph is not perfect. Page importance is now a moving target and we only hope to stay close to it. It is convenient to think of the variable $G=\vert H\vert$ as the clock. Consider two time instants $t-T,t$ corresponding to $G$ having the value $t-T$ and $t$. Let $H_{t-T,t}[i]$ be the total of cash added to the history of page $i$ between time $t-T$ and $t$, i.e., $H_{t}[i]-H_{t-T}[i]$. Let

\begin{displaymath}\forall j, X_{t,T}[j]=\frac{H_{t-T,t}[j]}{(\sum_i H_{t-T,t}[i])}=\frac{H_{t-T,t}[j]}{T} \end{displaymath}

Because the statement of Theorem 
2.3 does not impose any condition on the initial state of $X_t$, it is obvious that $X_{t,T}$ converges to the vector of importance when $T$ goes to infinity. (Note that on the other hand, for a fixed $T$, when $t$ goes to infinity, $X_{t,T}$ does not converge to the vector of importance.) Using the data gathered between $t-T$ and $t$, comes to ignoring the history before time $t-T$ and starting with the state of the cash at time $t-T$ for initial state. Observe that this state may be not more informative than the very first state with equal distribution of cash. We thus estimate the importance of a page by looking at the history between $t$ (now) and $t-T$. We call the interval $[t-T,t]$ the (time) window. There is a trade-off between precision and adaptability to changes and a critical parameter of the technique is the choice of the size of the window.

The Adaptive OPIC algorithm

We next describe (variants of) an algorithm, namely Adaptive OPIC, that compute(s) page importance based on a time window. In Adaptive OPIC, we have to keep some information about the history in a particular time window. We considered the following window policies:
  • Fixed Window (of size $T$): For every page $i$, we store the value of cash $C_t[i]$ and the global value $G_t$ for all times it was crawled since (now - $T$).
  • Variable Window (of size $k$): For every page $i$, we store the value of cash $C_t[i]$ and the global value $G_t$ for the last $k$ times this page was crawled.
  • Interpolation (of time $T$): For every page $i$, we store only the $G_t$ value when it was last crawled, and an interpolated history $H[i]$ (to be defined) that estimates the cash it got in a time interval of size $T$ before that last crawl.
In the following, we call measure a pair ($C,G$). Note that in Variable Window, we store exactly $k$ measures; and that in Interpolation, we store only one. Note also that in Fixed Window, the number of measures varies from one page to another. In our analysis of Adaptive OPIC, there will be two main dimensions: (i) the page selection strategy that is used (e.g., Greedy or Random ) and (ii) the window policy that is considered (e.g., Fixed Window or Interpolation). Variable Window is the easiest to implement since we have to maintain, for each page, a fixed number of values.

Fixed Window

One must be aware that some pages will be read rarely (e.g., once in several months), whereas others will be read perhaps daily. So there are huge variations in the size of histories. For very large histories, it is interesting to use compression techniques, e.g., to group several consecutive measures into one. On the opposite, we have too few measures for very unimportant pages. This has a negative impact on the speed of convergence of the algorithm. By setting a minimum number of measures per page (say 3), experiments show that we obtain better results. See Section 


It is tailored to use little resources. Indeed, for each page, the history simply consists of two values. This is what we tested on real web data (See Section 4). It is the policy actually used in Xyleme [
27,20,28]. It is based on a fixed time window of size $T$. The algorithm uses for history two vectors $H[1..n],G[1..n]$:
  • $H[i]$ represents the sum of the cash acquired by the page $i$ during a time period $T$ before the last crawl. This value is obtained by interpolation.
  • $G[i]$ is the $G$-time of that last crawl.
Figure 1: Simple Interpolation
When we visit a page and update its history, we estimate the cash that was added to that page in the interval $T$ until that visit. See Figure 1, for an intuition of the interpolation. We know what was added to its cash between time $G[i]$ and $G$, $C[i]$. The interpolation assumes that the page accumulates cash linearly. This has been confirmed by experiments. More precisely, the history is updated as follows:

\begin{displaymath} \begin{array}{lll} H[i]*\frac{T - (G - G[i])}{T} + C[i] &&... ... C[i]*\frac{T}{G - G[i]} && \makebox{otherwise} \end{array} \end{displaymath}

Expanding the graph

When the number of nodes increases, the relative difficulty to assign a cash and a history to new nodes highlights some almost philosophical issues about the importance of pages. Consider the definition of importance based on $(\dag )$. When we crawl new pages, these pages acquire some importance. The importance of previously known pages mechanically decreases in average simply because we crawled more pages. This is true for instance in the random walk model: adding new pages of non-null probability to be read can only decrease the probability of other pages to be read. However, these changes in pages importance seem unfair and are not expected by users of the system. We assign to each new page a default history that corresponds to the importance of recently introduced pages. Experiments confirmed this to be a good estimate. The reason is that important pages are discovered first, whereas new or recently introduced pages are often the least important ones.

Focused crawling and pages discovery

In our system, the scheduling of pages to be read depends mostly on the amount of ``cash'' for each page. The crawling speed gives the total number of pages that we can read for both discovery and refresh. Our page importance architecture allows us to allocate resources between discovery and refresh. For instance, when we want to do more discovery, we proceed as follows: (i) we take some cash from the virtual page and distribute it to pages that were not read yet (ii) we increase the importance of ``small'' edges pointing to the virtual page so that it accumulates more cash. To refresh more pages, we do the opposite. We can also use a similar method to focus the crawl on a subset of interesting pages on the web. For instance, we may use this strategy to focus our crawling on XML pages [
27,20]. In some other applications, we may prefer to quickly detect new pages. For instance, we provide to a press agency a 'copy tracker' that helps detecting copies of their News wires over the web. The problem with News pages is that they often last only a few days. In the OPIC algorithm, we process as follows for each link: pages that are suspected to contain news wires (e.g. because the URL contains ``news'') receive some ``extra'' cash. This cash is taken from the (unique) virtual page so that the total value of cash in the system does not change. Other criteria may be used, for instance we are working on the use of the links semantic, e.g. by analyzing words found close to the HTML link anchor.

Implementation and experiments

We implemented and tested first the standard off-line algorithm for computing page importance, then variants of Adaptive OPIC. We briefly describe some aspects of the implementation. We then report on experiments first on synthetic data, then on a large collection of web pages.

A distributed implementation

Our implementation of the off-line algorithm is standard and will not be discussed here. We implemented a distributed version of Adaptive OPIC that can be parameterized to choose a page selection strategy, a window policy, a window size, etc. Adaptive OPIC runs on a cluster of Linux PCs. The code is in C++. Corba is used for communications between the PCs. Each crawler is in charge of a portion of the pages of the web. The choice of the next page to read by a crawler is performed by a separate module (the Page Scheduler). The split of pages between the various crawlers is made using a hash function $h_{url}$ of the URLs. Each crawler evaluates the importance of pages it is in charge of. Its portion of the cash vector is in main memory, whereas its portion of the history is on disk. The crawler also uses an (in memory) hash table that allows to map a URL handled by this crawler to its identifier (an integer) in the system. Finally, it uses a map from identifiers to URLs. This last map may reside on disk. Each crawler crawls millions of pages per day. The bandwidth was clearly the limiting factor in the experiments. For each page that is crawled, the crawler receives the identifier of a page from the page scheduler and then does the following:
Fetch: It obtains the URL of the page, fetches the page from the web and parses it;
Money transfers: It distributes the current cash of the page to the pages it points to. For each such page, it uses $h_{url}$ to obtain the name of the server in charge of that page. It sends a ``money transfer'' to that server indicating the URL of the page and the amount. This is a buffered network call.
Records: It updates the history of the page and resets its cash to null. Updating the history requires one disk access.
Each crawler also processes the money transfer orders coming from other servers. Communications are asynchronous. It should be observed that for each page crawled, there are only two disk accesses, one to obtain the metadata of the page and one to update the metadata, including the history. Besides that, there are Corba communications (on the local network), and main memory accesses.

Synthetic data

Although we started our experiments with large collection of URLs on the web, synthetic data gave us more flexibility to study various input and output parameters, such as: graph size, graph connectivity, change rates, types of changes, distribution of in-degrees, out-degrees and page importance, importance error, ranking errors.

The graph model

We performed experiments with various synthetic graphs containing dozens of millions of web pages. These experiments showed that the use of very large graphs did not substantially alter the results. For instance, we started with graphs obtained using a Poisson distribution on the average of incoming links, a somewhat simplistic assumption. We then performed experiments with more complex distributions following recent studies of the web graph [
4], e.g., with a power distribution $P(I=n)=1/n^{2.1}$. Results were rather similar to those obtained using a Poisson distribution. In order to also control the distribution of outgoing links and the correlations between them, we tried several graph models in the spirit of [8], but even with significant changes of the graph parameters, the patterns of the results did not change substantially from the simple graph model. So, we then restricted our attention to rather simple graphs of reasonably small size to be able to test extensively, e.g., various page selection strategies, various window sizes, various patterns of changes of the web. In the remaining of this section, we will consider a simple graph model based on the power distribution on incoming edges. Details omitted. The number of nodes is fixed to N = 100 000 nodes.

Impact of the page selection strategy

First, we studied the convergence of OPIC for various page selection strategies. We considered Random , Cycle and Greedy . We compared the values of the estimates at different points in the crawl, after crawling $N$ pages, up to to $10*N$ pages. The error we compute is the mean over the set of pages of the error between the computation of OPIC at this state and the value of the fixpoint. More precisely, we compute the average of the percentage of error:

\begin{displaymath}100*\frac{\sum_j \frac{\vert X[j]-Imp[j]\vert}{Imp[j]}}{N}\end{displaymath}

where $Imp$ is obtained by running the off-line algorithm until a fixpoint is reached (with negligible error).
Figure 2: Convergence of OPIC (on all pages)
Figure: Convergene of OPIC (on all pages)
Consider Figure 2. The error is about the same for Greedy and Cycle . This result was expected since previous studies [
13] show that given a standard cost model, uniform refresh strategies perform as good as focused refresh. As we also expected, Random performs significantly worse. We also compared these, somewhat artificially, to the off-line algorithm. In the off-line, each iteration of the matrix is a computation on $N$ pages, so we count $N$ ``crawled pages'' for each iteration. The off-line algorithm converges almost like Cycle and Greedy . This is not surprising since the crawl of $N$ pages with Cycle corresponds roughly to a biased iteration on the matrix.
Figure 3: Convergence of OPIC (on important pages)
Figure: Convergene of OPIC (on important pages)
Now consider Figure 3. The error is measured now only for the top ten percent pages, the interesting ones in practice. For this set of pages, Greedy (that is tailored to important pages) converges faster than the others including the off-line algorithm. We also studied the variance. It is roughly the same for all page selection strategies, e.g., almost no page had a relative error more than twice the mean error. We also considered alternative error measures. For instance, we considered an error weighted with page importance or the error on the relative importance that has been briefly mentioned. We also considered the error in ordering pages when their importance is used to rank query results. All these various error measures lead to no significant difference in the results.

Impact of the size of the window

Figure 4: Influence of window's sizes
Figure: Influence of window's sizes
As already mentioned, a small window means more reactivity to changes but at the cost of some lack of precision. A series of experiments was conducted to determine how much. To analyze the impact of the size of the window, we use Adaptive OPIC with the Greedy strategy and a Fixed Window of $M$ crawls, i.e., we keep for each page the history since the last $M$ crawls of the page. Similar results were obtained with other variants of the algorithm. Consider Figure 4 ignoring the Interpolation policy for the moment. The change rate is the number of pages that have their in-degree significantly modified (i.e. divided par two or multiplied by two) during the time of crawling $N$ pages, where $N$ is the number of pages on the graph (i.e. the time for ``one'' crawl of the graph). For each change rate the graph is crawled ten times. The figure shows the result for $M$ = 4, 8, 16. The important point to notice is that we can get reasonably close to the fixpoint with rather small windows (e.g., $M=8$ here). As previously mentioned, the trade-off is reactivity to changes versus precision. When the time window becomes too small (e.g., $M=4$ here), the error is more important. This is because each measure for a page gives only a too rough estimate of this page importance, so the error is too large. Such an error may still be acceptable for some applications. Now observe the Interpolation experiment in Figure 
4. First, note that it performs almost as well as large Variable Window (e.g. $M=16$) on graph with few changes. Also, it adapts better to higher change rates (e.g. more than 1 percent). So, let us consider now the comparison of various window policies.

Impact of the window policy

We compared different policies for keeping the history. In this report, we use again the Greedy strategy. Various window policies may require different resources. To be fair, we chose policies that roughly requested similar amount of resources. Typically, we count for storage the number of measures we store. (Recall that a measure consists of a value for $C$ and one for $G$.) The five policies we compared used between 4 and 8 measures, except Interpolation that by definition uses only 1. Figure 5 shows the average number of measures used per page in each case. These measures depend for Fixed Window on the crawling speed which was set here to be $N$ pages per month (the speed was chosen here so that Fixed Window would use about as much resources as the others). We also considered a variant of Fixed Window that forces each page to have a minimum number of measures, namely Improved Fixed Window. We required for the experiment mentioned here a minimum of 3 measures. Note that this resulted for this particular data set in an increase of the average number of measures from $4$ to $6.1$.
Figure 5: Storage resources per time window
Window Type and Size Measures per page
Variable Window 8 measures 8
Fixed Window 8 months 8.4
Improved Fixed Window 4 months 6.1
Interpolation 4 months 1
Figure 6: Influence of window's types
Figure: Influence of window's types
Now consider Figure 6. It shows that for a similar number of measures, Variable Window performs better than Fixed Window. The problem with Fixed Window is that very few measures are stored for unimportant pages and the convergence is very slow because of errors on such pages. On the other hand, the Improved Fixed Window policy yields significantly better results. The improvement comes indeed from more reliability for unimportant pages. The most noticeable result about the use of windows is that the algorithm with the Interpolation policy outperforms the other variants while consuming less resources. Indeed, the error introduced by the interpolation is negligible. Furthermore, the interpolation seems to avoid some ``noise'' introduced when an old measure is added (or removed) in Adaptive OPIC. In some sense, the interpolation acts as a filter on the sequence of measures. Of course the convergence of all variants of the adaptive algorithms depends on the time window that is used. The excellent behavior of Interpolation convinced us to adopt it for our experiments with crawls of the web. This is considered next.

Web data

We performed the web experiments using the crawlers of Xyleme [
28]. The crawl used the page selection strategy of Xyleme that has been previously mentioned and is related to Greedy . The history was managed using the Interpolation policy. During the test, the number of PCs varied from 2 to 8. Each PC had little disk space and less than 1.5Gb of main memory. Some reasonable estimate of page importance for the most important pages was obtained in a few days, as important pages are read more frequently and discovered sooner than others. The experiments lasted for several months. We discovered one billion URLs; only 400 millions of them were actually read. Note that because of the way we discover pages, these are 400 million relatively important pages. Moreover, we could give reasonable importance estimates even on pages that were never read. This experiment was sufficient (with limited human checking of the results) to conclude that the algorithm could be used in a production environment. Typically, for all practical uses of importance we considered (such as ranking query results or scheduling page refresh), the precision brought by the algorithm is rapidly sufficient. An advantage of the algorithm is also that it rapidly detects the new important pages, so they can be read sooner. A main issue was the selection of the size of the time window. We first fixed it too small which resulted in undesired variations in the importance of some pages. We then used a too large window and the reactivity to changes was too limited. Finally, the window was set to 3 months. This value depends on the crawling speed, which in our case was limited by the network bandwidth. Our performance analysis also showed that using our system (Xyleme crawler and Adaptive OPIC), it is possible to, for instance, crawl and compute page importance (as well as maintain this knowledge) for a graph of up to 2 billions pages with only 4 PCs equipped each with 4Gb of main memory and a small disk. In the context of Web Archiving [1], we also conducted experiments to decide if our measures of page importance could be used to select pages of interest for the French national Library. We selected thousand web sites, and $8$ different professional librarians ranked each site in order to decide which sites should be archived (on a 1 to 4 scale). We defined the reference value for each site based on the average of these rankings. Finally, we defined the 'score' of a librarian as the number of sites in which his rank was identical to the reference. The scores of librarians ranged from 60 to 80 percent, and the score of our page importance measures was 65 percent. This means that our measure based only on page importance was as good as a professional librarian, although not as good as the best ones. We are currently working on using other criteria [1] to improve the ``automatic'' librarian.

Other Improvements

During our experiments, we found out that the semantics of links in dynamic pages is (often) not as good as in pages fully written by authors. Links written by authors usually points to more relevant pages. On the other hand, most links in dynamic pages often consist in other (similar) queries to the same database. For instance, forum archives or catalog pages often contain many links that are used to browse through classification. Similarly, we found out that ``internal'' links (links that point to a page on the same web site) are less useful to discover other relevant pages than ``external'' links (links to a page on some other web site). To solve both problems, we are currently working on a notion of site-based importance [
1] that consider links between web-sites instead of links between web-pages. We are currently experimenting our algorithm with this new notion of importance per site.


We proposed a simple algorithm to implement with limited resources a realistic computation of page importance over a graph as large as the web. We demonstrated both the correctness and usability of the technique. Our algorithm can be used to improve the efficiency of crawling systems since it allows to focus on-line the resources to important pages. It can also be biased to take into account specific fields of interest for the users [1]. More experiments on real data are clearly needed. It would be in particular interesting to test the variants of Adaptive OPIC with web data. However, such tests are quite expensive. To understand more deeply the algorithms, more experiments are being conducted with synthetic data. We are experimenting with various variants of Adaptive OPIC. We believe that better importance estimates can be obtained and are working on that. One issue is the tuning of the algorithms and in particular, the choice of (adaptable) time windows. We are also continuing our experiments on changing graphs and in particular on the estimate of the derivative of the importance. We finally want to analyze more in-depth the impact of various specific graph patterns as done in [
17] for the off-line algorithm. We are also working on a precise mathematical analysis of the convergence speed of the various algorithms. The hope is that this analysis will provide us with bounds of the error of the importance, and will also guide us in fixing the size of windows and evaluating the changes in importance. We are also improving the management of newly discovered pages. The algorithm presented here computes page importance that depends on the entire graph by looking at one page at a time independently of the order of visiting the pages. It would be interesting to find other properties of graph nodes that can be computed similarly.


We want to thank Luc Segoufin, Laurent Mignet and Tova Milo for discussions on the present work.


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Appendix: Proof of correctness

Lemma 5.1   (see lemma 2.3) After each step $t$, we have for each page $j$,

\begin{displaymath}H_t[j]+C_t[j] = C_0[j] + \sum_{(i~ancestor~of~j)} ( \frac{L[i,j]}{out[i]} *H_t[i] ) \end{displaymath}

The proof is by induction. Clearly, the lemma is true at time $t=0$. Suppose it is true at time $t$ for each element $j$. Consider element $j$ at step $t+1$. At step $t+1$ a page $k$ is crawled. Two cases may occur: If $j=k$, then the right term doesn't change: $\forall i, i\neq j, H_{t+1}[i]=H_t[i]$. The left term value doesn't change either, the cash is added to $H$ and then set to zero. So $H_{t+1}[j]+C_{t+1}[j]=H_t[j]+C_t[j]$, and the equation is true at $t+1$. If $j\neq k$. Then $C_{t+1}[j]$ increases by $C_t[k]*\frac{L[i,j]}{out[i]}$. So

\begin{displaymath} H_{t+1}[j]+C_{t+1}[j]= \end{displaymath}

\begin{displaymath} C_0[j] + \sum_{(i~ancestor~of~j)} ( \frac{L[i,j]}{out[i]} *H_t[i] ) +C_t[k]*\frac{L[k,j]}{out[k]} \end{displaymath}

Now \(\forall i, i\neq k, H_{t+1}[i]=H_t[i]\), and also \(H_{t+1}[k]=H_t[k]+C_t[k]\), and this shows the result.

Lemma 5.2   Consider a strongly connected graph. $c$ in the cash of any node $i$ eventually leads to $c/n^n$ in the cash of $j$. For each $j$, $H_t[j]$ goes to infinity.

Each node splits the value by at most $n$, because it can't have more than $n$ different links. We suppose that the graph is strongly connected, so there is a path from $i$ to $j$, and it is no longer than $n$. Let's note $P_1...P_k$ the pages for this path. We suppose that every page is crawled an infinite number of times. So we eventually will crawl $P_1$, then eventually $P_2$, ... until $P_k$. Thus we will eventually have distributed at least $c/n^n$ in the cash of $j$. Consider any moment $t$, some node contains at least $1/n$ cash (because $\sum_{i}C_t[i]=1$). Thus, it will eventually increase the cash of $j$ (thus eventually its history) by $1/n^n$. Thus $H_t[j]$ goes to infinity.

Lemma 5.3   $\lim_{t\rightarrow +\infty}\vert L'*X_t-X_t\vert=0 $

By definition of $X_t$, for each $i$,


Then, by Lemma 2.3,

\begin{displaymath}H_t[j]+C_t[j] = C_0[j] + \sum_{(i~ancestor~of~j)} ( \frac{L[i,j]}{out[i]} *H_t[i] ) \end{displaymath}

Let us look at the $j$th coordinate of $\vert L'*X_t-X_t\vert$:

\begin{displaymath}\left\vert \frac{(L'*H_t-H_t)[j]}{\sum_k H_t[k]}\right\vert =... ..._0[j]}{\sum_k H_t[k]}\right\vert \leq \frac{1}{\sum_k H_t[k]} \end{displaymath}

Its limit is 0 because, when $t$ goes to infinity, $\sum_j{H_t[j]}$ goes to infinity (by lemma 2.4) and $C_0[j]$, $C_t[j]$ are bounded by 1.

Theorem 5.4   The limit of $X_t$ is $X_{Importance}$, i.e.,

\begin{displaymath}lim_{t\rightarrow +\infty}X_t=X_{Importance} \end{displaymath}

By the previous result,

\begin{displaymath}\lim_{t\rightarrow +\infty}\vert(L'-1)*X_t\vert=0 \end{displaymath}

where 1 is the identity matrix (1 in the diagonal and 0 elsewhere). Consider now the decomposition of $X_t=S_t+D_t$ where $S_t$ is in $Ker (L'-1)$ (the kernel of matrix $L'-1$), and $D_t$ in the corresponding orthogonal space where the restriction of $L'-1$ is invertible. Because $S_t$ is in $Ker (L'-1)$, we have \( \forall t, L'*X_t-X_t=L'*D_t-D_t \) and so

\begin{displaymath}\lim_{t\rightarrow +\infty}\vert(L'-1)*D_t\vert=0 \end{displaymath}

We can now restrict to the orthogonal space of $Ker (L'-1)$, in which $L'-1$ has an inverse called $H$. The matrix multiplication being continuous, we can multiply to the left by $H$, which is constant, and thus

\begin{displaymath}\lim_{t\rightarrow +\infty}\vert D_t\vert=0 \end{displaymath}

Now if we use the fact that there is a single fixpoint solution for $L'$, that mean that $Ker (L'-1)$ is of dimension $1$ and that

\begin{displaymath}\forall t, X_t=\alpha_t*X_{Importance}+D_t \end{displaymath}

where $\alpha_t$ is a scalar. Now because $\vert D_t\vert$ converges to zero, and $\vert X_t\vert=\vert X_{Importance}\vert=1$, we have:

\begin{displaymath}lim_{t\rightarrow +\infty}X_t=X_{Importance} \end{displaymath}


... often[*]
Google [11] seems to use such a strategy for refreshing pages; Xyleme [28] does.
... 1[*]
Note that the converse is true in the sense that if the graph is not aperiodic it is always possible to find an $x_0$ such that $(\dag)$ does not converge
Greater values of $\epsilon$ increase the convergence speed

Gregory Cobena, 2003-02-25, INRIA (Domaine de Voluceau, Rocquencourt BP105, 78153 Le Chesnay), France

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