In this paper, we describe a novel data model for online social networks based on hypergraphs. We show how an influence analysis problem can be properly faced leveraging the introduced network structure. In particular, we implemented a bio-inspired maximization algorithm on the top of the hypergraph model, exploiting the concept of influential path. Preliminary experiments using data of several social networks show how our approach obtains very promising results and encourage the research in this direction.
Influence Analysis in Online Social Networks Using Hypergraphs / Amato, Flora; DI LILLO, Francesco; Moscato, Vincenzo; Picariello, Antonio; Sperlì, Giancarlo. - (2017), pp. 501-508. (Intervento presentato al convegno IEEE International Conference on Information Reuse and Integration (IRI 2017) tenutosi a San Diego, CA, USA nel August 4-6, 2017) [10.1109/IRI.2017.72].
Influence Analysis in Online Social Networks Using Hypergraphs
Flora Amato;Francesco di Lillo;Vincenzo Moscato;Antonio Picariello;Giancarlo Sperlì
2017
Abstract
In this paper, we describe a novel data model for online social networks based on hypergraphs. We show how an influence analysis problem can be properly faced leveraging the introduced network structure. In particular, we implemented a bio-inspired maximization algorithm on the top of the hypergraph model, exploiting the concept of influential path. Preliminary experiments using data of several social networks show how our approach obtains very promising results and encourage the research in this direction.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
