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 alpha-centrality, 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.

Distributed Estimation of Node Centrality with Application to Agreement Problems in Social Networks

Oliva G.;
2019-01-01

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 alpha-centrality, 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.
2019
978-1-5386-1395-5
File in questo prodotto:
File Dimensione Formato  
Distributed_Estimation_of_Node_Centrality_with_Application_to_Agreement_Problems_in_Social_Networks.pdf

non disponibili

Licenza: Non specificato
Dimensione 300.6 kB
Formato Adobe PDF
300.6 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/74507
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? ND
social impact