Paper Info
Title
Distributed Estimation Of Node Centrality With Application To Agreement Problems In Social Networks
Abstract
Measures of node centrality that describe the importance of a node within a network are crucial for understanding the behavior of social networks and graphs. In this paper, we address the problem of distributed node centrality identification. In particular, we focus our attention on alphacentrality, which can be seen as a generalization of eigenvector centrality, particularly suitable for graphs with asymmetric interactions. In this setting, our contribution is twofold: first we derive a distributed protocol where agents can locally compute their alpha-centrality index by means of local interactions; then we propose a novel consensus-algorithm running in parallel to the alpha-centrality estimator, which converges towards a weighted average of the initial conditions, where the weights are dictated by the alpha-centrality vector. The proposed algorithm finds application in social networks, where agreement protocols typically place more value on experts and influencers than on the rest of users. Simulations results are provided to corroborate the theoretical findings.
Year
DOI
Venue
2018
10.1109/CDC.2018.8619765
2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
Field
DocType
ISSN
Graph,Mathematical optimization,Social network,Eigenvector centrality,Computer science,Centrality,Weighted arithmetic mean,Estimator,Influencer marketing
Conference
0743-1546
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Eduardo Montijano121422.27
gabriele oliva212021.23
Andrea Gasparri344741.42