Distributed Algorithms for Computation of Centrality Measures in Complex Networks

This paper is concerned with distributed computation of several commonly used centrality measures in complex networks. In particular, we propose deterministic algorithms, which converge in finite time, for the distributed computation of the degree, closeness and betweenness centrality measures in di...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 62; no. 5; pp. 2080 - 2094
Main Authors: Keyou You, Tempo, Roberto, Li Qiu
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Atmospheric measurements
Centrality measures
Complex networks
Complex systems
Convergence
convergence properties
Distributed algorithms
distributed computation
Eigenvectors
Graph theory
Graphs
Internet
Networks
randomized algorithms
Search algorithms
Spamming
Unsolicited electronic mail
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Summary:This paper is concerned with distributed computation of several commonly used centrality measures in complex networks. In particular, we propose deterministic algorithms, which converge in finite time, for the distributed computation of the degree, closeness and betweenness centrality measures in directed graphs. Regarding eigenvector centrality, we consider the PageRank problem as its typical variant, and design distributed randomized algorithms to compute PageRank for both fixed and time-varying graphs. A key feature of the proposed algorithms is that they do not require to know the network size, which can be simultaneously estimated at every node, and that they are clock-free. To address the PageRank problem of time-varying graphs, we introduce the concept of persistent graph, which eliminates the effect of spamming nodes. Moreover, we prove that these algorithms converge almost surely and in the sense of L^p. Finally, the effectiveness of the proposed algorithms is illustrated via extensive simulations using a classical benchmark.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2016.2604373