In this paper, inspired by hypergraph-based approaches, we propose a novel data model for social media networks: it allows to represent in a simple way all the different kinds of relationships that are typical of these environments (among multimedia contents, among users and multimedia content and among users themselves) and to enable several kinds of analytics and applications. From the other hand, we have tested several influence maximization algorithms leveraging the introduced network structure in order to show the advantages to consider also “user-to-multimedia” relationships (in addition to the “user-to-user” ones) in the influence analysis problem. Preliminary experiments using data of several social media networks shows how our approach obtains very promising results.
Influence Maximization in Social Media Networks Using Hypergraphs / Amato, Flora; Moscato, Vincenzo; Picariello, Antonio; Sperlì, Giancarlo. - 10232:(2017), pp. 207-221. (Intervento presentato al convegno 12th International Conference on Green, Pervasive and Cloud Computing, GPC 2017 tenutosi a Cetara (SA), Italy nel 11 - 14 May, 2017) [10.1007/978-3-319-57186-7_17].
Influence Maximization in Social Media Networks Using Hypergraphs
Flora Amato;Vincenzo Moscato;Antonio Picariello;Giancarlo Sperlì
2017
Abstract
In this paper, inspired by hypergraph-based approaches, we propose a novel data model for social media networks: it allows to represent in a simple way all the different kinds of relationships that are typical of these environments (among multimedia contents, among users and multimedia content and among users themselves) and to enable several kinds of analytics and applications. From the other hand, we have tested several influence maximization algorithms leveraging the introduced network structure in order to show the advantages to consider also “user-to-multimedia” relationships (in addition to the “user-to-user” ones) in the influence analysis problem. Preliminary experiments using data of several social media networks shows how our approach obtains very promising results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.