The investigation of community structures in networks is a task of great importance in many disciplines, namely physics, sociology, biology and computer science, where systems are often represented as graphs. One of the challenges is to find local communities in a graph from a local view- point, in the absence of the access to global information, and to reproduce the subjective hierarchical vision for each vertex. In this paper, we present the improvement of an information dynamics algorithm in which the la- bel propagation of nodes is based on the Markovian flow of information in the network under cognitive-inspired constraints. We introduced two more complex heuristics that allow to detect the hierarchical community struc- ture of the networks from a source vertex or a community, adopting fixed values of model’s parameters. Experimental results show that the proposed methods are efficient and well-behaved in both the real-world and synthetic networks.

Hierarchical Community Structure in Complex (Social) Networks / E. Massaro;F. Bagnoli. - STAMPA. - 7:(2014), pp. 379-393. (Intervento presentato al convegno Summer Solstice 2013 International Conference on Discrete Models of Complex Systems tenutosi a Warszawa, Poland nel June 27–29, 2013) [10.5506/APhysPolBSupp.7.379].

Hierarchical Community Structure in Complex (Social) Networks

MASSARO, EMANUELE;BAGNOLI, FRANCO
2014

Abstract

The investigation of community structures in networks is a task of great importance in many disciplines, namely physics, sociology, biology and computer science, where systems are often represented as graphs. One of the challenges is to find local communities in a graph from a local view- point, in the absence of the access to global information, and to reproduce the subjective hierarchical vision for each vertex. In this paper, we present the improvement of an information dynamics algorithm in which the la- bel propagation of nodes is based on the Markovian flow of information in the network under cognitive-inspired constraints. We introduced two more complex heuristics that allow to detect the hierarchical community struc- ture of the networks from a source vertex or a community, adopting fixed values of model’s parameters. Experimental results show that the proposed methods are efficient and well-behaved in both the real-world and synthetic networks.
2014
Summer Solstice 2013 International Conference on Discrete Models of Complex Systems
Summer Solstice 2013 International Conference on Discrete Models of Complex Systems
Warszawa, Poland
June 27–29, 2013
E. Massaro;F. Bagnoli
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/871121
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