Name
Title | ||
|---|---|---|
Multi-view community detection with heterogeneous information from social media data. |
Abstract | ||
|---|---|---|
•Heterogeneous information views in social media are combined for community detection.•Experimental evaluation showed the benefits of integrating diverse sources.•Each source had a particular effect on the quality of the detected communities.•The nature of social interactions affect the relevance of the information sources.•Symmetrisation strategies also showed differentiated effects on community quality. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2018.02.023 | Neurocomputing |
Keywords | Field | DocType |
Community detection,Social networks,Multi-view learning,Social graph,Community structure | Data science,Social relation,Social network,Social media,Artificial intelligence,Information aggregation,Semantics,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
289 | C | 0925-2312 |
Citations | PageRank | References |
1 | 0.35 | 31 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonela Tommasel | 1 | 25 | 8.87 |
| Daniela Godoy | 2 | 502 | 38.22 |