Name
Abstract | ||
|---|---|---|
Social networks can be used to model social interactions between individuals. In many circumstances, not all interactions between individuals are observed. In such cases, a social network is constructed with the data that has been observed, as this is the best one can do. Recent research has attempted to predict future links in a social network, though this has proven a very challenging task. Rather than predicting future links, we propose an inference method for recovering the links in a social network that already exist but that have not been observed. In addition, our approach automatically identifies groups of individuals that form tight-knit communities and models the intra and inter-community interactions. At this higher level of abstraction and for a social network built frommobile phone calls, our method is able to accurately identify a subset of 10% of all community pairs where about 50% of the pairs have had unobserved communication between them, an improvement of about four times over a subset of the same size with randomly chosen pairs. To the best of our knowledge, this is the first method that infers links that exist but are unobservable in a phone call-based social network. In addition, we perform the inference at the community level, where the discovery of unobserved inter-community communication can provide further insight into the organizational structure of the social network andcan identify social groups that may share common interests. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CSE.2009.197 | CSE (4) |
Keywords | Field | DocType |
higher level,social network,future link,large social networks,frommobile phone call,social group,form tight-knit community,inference method,social interaction,inferring unobservable inter-community links,community level,community pair,social groups,social networks,organizational structure,data mining,feature extraction,artificial neural networks,logistics | Dynamic network analysis,Social group,Organizational network analysis,Social network,Organizational structure,Computer science,Inference,Computer network,Phone,Artificial intelligence,Unobservable,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.34 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heath Hohwald | 1 | 115 | 6.75 |
| Manuel Cebrian | 2 | 196 | 12.40 |
| Arturo Canales | 3 | 9 | 0.81 |
| Rubén Lara | 4 | 543 | 36.35 |
| Nuria Oliver | 5 | 4368 | 357.22 |