4 research outputs found
Monte Carlo Methods for Top-k Personalized PageRank Lists and Name Disambiguation
We study a problem of quick detection of top-k Personalized PageRank lists.
This problem has a number of important applications such as finding local cuts
in large graphs, estimation of similarity distance and name disambiguation. In
particular, we apply our results to construct efficient algorithms for the
person name disambiguation problem. We argue that when finding top-k
Personalized PageRank lists two observations are important. Firstly, it is
crucial that we detect fast the top-k most important neighbours of a node,
while the exact order in the top-k list as well as the exact values of PageRank
are by far not so crucial. Secondly, a little number of wrong elements in top-k
lists do not really degrade the quality of top-k lists, but it can lead to
significant computational saving. Based on these two key observations we
propose Monte Carlo methods for fast detection of top-k Personalized PageRank
lists. We provide performance evaluation of the proposed methods and supply
stopping criteria. Then, we apply the methods to the person name disambiguation
problem. The developed algorithm for the person name disambiguation problem has
achieved the second place in the WePS 2010 competition
On the Localization of the Personalized PageRank of Complex Networks
In this paper new results on personalized PageRank are shown. We consider
directed graphs that may contain dangling nodes. The main result presented
gives an analytical characterization of all the possible values of the
personalized PageRank for any node.We use this result to give a theoretical
justification of a recent model that uses the personalized PageRank to classify
users of Social Networks Sites. We introduce new concepts concerning
competitivity and leadership in complex networks. We also present some
theoretical techniques to locate leaders and competitors which are valid for
any personalization vector and by using only information related to the
adjacency matrix of the graph and the distribution of its dangling nodes
FrogWild! -- Fast PageRank Approximations on Graph Engines
We propose FrogWild, a novel algorithm for fast approximation of high
PageRank vertices, geared towards reducing network costs of running traditional
PageRank algorithms. Our algorithm can be seen as a quantized version of power
iteration that performs multiple parallel random walks over a directed graph.
One important innovation is that we introduce a modification to the GraphLab
framework that only partially synchronizes mirror vertices. This partial
synchronization vastly reduces the network traffic generated by traditional
PageRank algorithms, thus greatly reducing the per-iteration cost of PageRank.
On the other hand, this partial synchronization also creates dependencies
between the random walks used to estimate PageRank. Our main theoretical
innovation is the analysis of the correlations introduced by this partial
synchronization process and a bound establishing that our approximation is
close to the true PageRank vector.
We implement our algorithm in GraphLab and compare it against the default
PageRank implementation. We show that our algorithm is very fast, performing
each iteration in less than one second on the Twitter graph and can be up to 7x
faster compared to the standard GraphLab PageRank implementation