Fast PageRank Computation via a Sparse Linear System

Corso, Gianna M. Del; Gullì, Antonio; Romani, Francesco

doi:10.1080/15427951.2005.10129108

Cited by 81 publications

(52 citation statements)

References 15 publications

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“…The first step in these approaches is to compute the pairwise node affinity matrices in each graph and then determine the distance between these matrices. There are several approaches for determining node affinities in a graph, such as Pagerank and various extensions of random walks [7]. Another recent approach in this category is called Delta connectivity, which can be used for the purpose of anomaly detection.…”

Section: Graph-based Anomaly Detectionmentioning

confidence: 99%

Node Re-Ordering as a Means of Anomaly Detection in Time-Evolving Graphs

Rashidi

Kan

Bailey

et al. 2016

Machine Learning and Knowledge Discovery in Databases

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Abstract. Anomaly detection is a vital task for maintaining and improving any dynamic system. In this paper, we address the problem of anomaly detection in time-evolving graphs, where graphs are a natural representation for data in many types of applications. A key challenge in this context is how to process large volumes of streaming graphs. We propose a pre-processing step before running any further analysis on the data, where we permute the rows and columns of the adjacency matrix. This pre-processing step expedites graph mining techniques such as anomaly detection, PageRank, or graph coloring. In this paper, we focus on detecting anomalies in a sequence of graphs based on rank correlations of the reordered nodes. The merits of our approach lie in its simplicity and resilience to challenges such as unsupervised input, large volumes and high velocities of data. We evaluate the scalability and accuracy of our method on real graphs, where our method facilitates graph processing while producing more deterministic orderings. We show that the proposed approach is capable of revealing anomalies in a more efficient manner based on node rankings. Furthermore, our method can produce visual representations of graphs that are useful for graph compression.

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Section: Graph-based Anomaly Detectionmentioning

confidence: 99%

Node Re-Ordering as a Means of Anomaly Detection in Time-Evolving Graphs

Rashidi

Kan

Bailey

et al. 2016

Machine Learning and Knowledge Discovery in Databases

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“…Methods for accelerating PageRank computations have been considered in [22,9]; we might need to adapt them to our situation. Additionally, we might need methods to accelerate the computation of the accumulated endorsement matrix Q.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Endorsement deduction and ranking in social networks

Pérez-Rosés

Sebé

Ribó

2016

Computer Communications

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Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements.

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“…In order to accelerate the convergence of power iteration, Borgs et al [14] propose a sublinear time algorithm for PageRank computations. Because the graphs in real applications are usually sparse, del Corso et al [15] and Fujiwara et al [16] all apply sparse matrices to speed up the PageRank computation. Though these algorithms can accelerate the Personalized PageRank computation, they cannot be used in dynamic networks.…”

Section: Related Workmentioning

confidence: 99%

Monte Carlo Based Personalized PageRank on Dynamic Networks

Jun-chao

Chen

Song³

et al. 2013

International Journal of Distributed Sensor Networks

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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.

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Fast PageRank Computation via a Sparse Linear System

Cited by 81 publications

References 15 publications

Node Re-Ordering as a Means of Anomaly Detection in Time-Evolving Graphs

Node Re-Ordering as a Means of Anomaly Detection in Time-Evolving Graphs

Endorsement deduction and ranking in social networks

Monte Carlo Based Personalized PageRank on Dynamic Networks

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