Abstract
In large-scale networks, the structure of the underlying network changes frequently, and thus the power iteration method for Personalized PageRank computation cannot deal with this kind of dynamic network efficiently. In this paper, we design a Monte Carlo-based incremental method for Personalized PageRank computation. In a dynamic network, first, we do a random walk starting from each node and save the performed walks into a fingerprint database; second, we update the fingerprint database in a fixed time interval with our proposed update algorithm; finally, when a query is issued by a user, we estimate the Personalized PageRank vector by our proposed approximation algorithm. Experiments on real-world networks show that our method can handle multichanges of the underlying network at a time and is more efficient than related work, so it can be used in real incremental Personalized PageRank-based applications.
Highlights
Large-scale networks are ubiquitous in today’s world, such as World Wide Web, online social networks, and huge search and query-click logs regularly collected and processed by search engines
There are two main methods for computing the PageRank or Personalized PageRank vector: one is power iteration applying the linear algebra proposed by Page et al [1] and the other is the Monte Carlo approximation methods proposed by Litvak [12] and Fogaras and Racz [13]
We study the incremental computation of Personalized PageRank based on the Monte Carlo method; that is, given a graph, we do a random walk of fixed length starting from each node update the performed walks incrementally in fixed time interval, and approximate Personalized PageRank with the performed walks whenever needed
Summary
Large-scale networks are ubiquitous in today’s world, such as World Wide Web, online social networks, and huge search and query-click logs regularly collected and processed by search engines. PageRank [1], which is the stability distribution of a random walk in a Markov chain, has emerged as an effective measure for ranking on large-scale networks. The main idea of random walk on a graph is that starting from a source node, at each step, jump to a random node with a probability ε (usually called the teleport probability), and follow a random outgoing edge from the current with the probability 1 − ε. We study the incremental computation of Personalized PageRank based on the Monte Carlo method; that is, given a graph, we do a random walk of fixed length starting from each node update the performed walks incrementally in fixed time interval, and approximate Personalized PageRank with the performed walks whenever needed. We do some experiments on real world graphs, and the experiments show that our proposed method is more efficient than related researches in estimating Personalized PageRank on dynamic networks
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