Articolo in rivista, 2016, ENG, 10.1007/s13278-016-0397-y
Rossetti G.; Guidotti R.; Miliou I.; Pedreschi D.; Giannotti F.
CNR-ISTI, Pisa, Italy; Department of Computer Science, University of Pisa, Pisa, Italy; Department of Computer Science, University of Pisa, Pisa, Italy; Department of Computer Science, University of Pisa, Pisa, Italy; CNR-ISTI, Pisa, Italy
Due to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intracommunity and inter-community link prediction. Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.
Social Network Analysis and Mining 6 (1)
Link prediction, Community discovery detection
Rossetti Giulio, Giannotti Fosca
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 366877
Year: 2016
Type: Articolo in rivista
Creation: 2017-02-13 09:48:43.000
Last update: 2021-04-07 10:14:36.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1007/s13278-016-0397-y
URL: http://link.springer.com/article/10.1007/s13278-016-0397-y
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:366877
DOI: 10.1007/s13278-016-0397-y
ISI Web of Science (WOS): 000384325600003
Scopus: 2-s2.0-84988884776