Name
Title | ||
|---|---|---|
Predicting Small Group Accretion in Social Networks: A topology based incremental approach |
Abstract | ||
|---|---|---|
Small Group evolution has been of central importance in social sciences and also in the industry for understanding dynamics of team formation. While most of research works studying groups deal at a macro level with evolution of arbitrary size communities, in this paper we restrict ourselves to studying evolution of small group (size ≤ 20) which is governed by contrasting sociological phenomenon. Given a previous history of group collaboration between a set of actors, we address the problem of predicting likely future group collaborations. Unfortunately, predicting groups requires choosing from (n r) possibilities (where r is group size and n is total number of actors), which becomes computationally intractable as group size increases. However, our statistical analysis of a real world dataset has shown that two processes: an external actor joining an existing group (incremental accretion (IA)) or collaborating with a subset of actors of an exiting group (subgroup accretion (SA)), are largely responsible for future group formation. This helps to drastically reduce the (n r) possibilities. We therefore, model the attachment of a group for different actors outside this group. In this paper, we have built three topology based prediction models to study these phenomena. The performance of these models is evaluated using extensive experiments over DBLP dataset. Our prediction results shows that the proposed models are significantly useful for future group predictions both for IA and SA. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2808797.2808914 | Advances in Social Network Analysis and Mining |
Keywords | Field | DocType |
Social Networks, Higher Order Link Prediction, Group Evolution, Hypergraphs, Hypergraph Evolution | Accretion (meteorology),Topology,Social network,Computer science,Constraint graph,Network topology,Artificial intelligence,Predictive modelling,Phenomenon,Macro,restrict,Machine learning | Journal |
Volume | Citations | PageRank |
abs/1507.03183 | 1 | 0.40 |
References | Authors | |
21 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ankit Sharma | 1 | 22 | 6.81 |
| Rui Kuang | 2 | 484 | 31.16 |
| Jaideep Srivastava | 3 | 5845 | 871.63 |
| Xiaodong Feng | 4 | 1 | 0.40 |
| Kartik Singhal | 5 | 1 | 0.40 |