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EnglishDiscovering communities can promote the understanding of the structure, function and evolution in various systems. Overlapping community detection in poly-relational networks has gained much more interests in recent years, due to the fact that poly-relational networks and communities with pervasive overlap are prevalent in the real world. A plethora of methods detect communities from the poly-relational network by converting it to mono-relational networks first. Nevertheless, they commonly assume different relations are independent from each other, which is obviously unreal to real-life cases. In this paper, we attempt to relax this strong assumption by introducing a novel co-ranking framework, named MutuRank. It makes full use of the mutual influence between relations and actors to transform the poly-relational network to the mono-relational network. We then present a novel GMM-NK (Gaussian Mixture Model with Neighbor Knowledge) algorithm incorporating the impact from neighbors into the traditional GMM. Experimental results both on synthetic networks and the real-world network have verified the effectiveness of MutuRank and GMM-NK.
| Titolo: | Detecting overlapping communities in poly-relational networks |
| Autori: | |
| Data di pubblicazione: | 2015 |
| Rivista: | |
| Abstract: | Discovering communities can promote the understanding of the structure, function and evolution in various systems. Overlapping community detection in poly-relational networks has gained much more interests in recent years, due to the fact that poly-relational networks and communities with pervasive overlap are prevalent in the real world. A plethora of methods detect communities from the poly-relational network by converting it to mono-relational networks first. Nevertheless, they commonly assume different relations are independent from each other, which is obviously unreal to real-life cases. In this paper, we attempt to relax this strong assumption by introducing a novel co-ranking framework, named MutuRank. It makes full use of the mutual influence between relations and actors to transform the poly-relational network to the mono-relational network. We then present a novel GMM-NK (Gaussian Mixture Model with Neighbor Knowledge) algorithm incorporating the impact from neighbors into the traditional GMM. Experimental results both on synthetic networks and the real-world network have verified the effectiveness of MutuRank and GMM-NK. |
| Handle: | http://hdl.handle.net/11368/2857355 |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1007/s11280-015-0330-8 |
| URL: | http://www.kluweronline.com/issn/1386-145X |
| Appare nelle tipologie: | 1.1 Articolo in Rivista |
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| File | Descrizione | Tipologia | Licenza | |
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| Detecting overlapping communities in poly-relational networks.pdf | pdf versione editoriale definitiva | Documento in Versione Editoriale | Digital Rights Management non definito | Administrator Richiedi una copia |
http://hdl.handle.net/11368/2857355
