Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-57186-7_17 | GREEN, PERVASIVE, AND CLOUD COMPUTING (GPC 2017) |
Field | DocType | Volume |
Social media,Computer science,Constraint graph,Hypergraph,Influence analysis,Artificial intelligence,Analytics,Data model,Maximization,Machine learning,Network structure | Conference | 10232 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
30 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Flora Amato | 1 | 458 | 66.48 |
| Vincenzo Moscato | 2 | 519 | 64.03 |
| Antonio Picariello | 3 | 858 | 87.40 |
| Giancarlo Sperli | 4 | 86 | 19.40 |