Name
Abstract | ||
|---|---|---|
Social media data are often modeled as heterogeneous graphs with multiple types of nodes and edges. We present a discovery algorithm that first chooses a “background” graph based on a user's analytical interest and then automatically discovers subgraphs that are structurally and content-wise distinctly different from the background graph. The technique combines the notion of a group-by operation on a graph and the notion of subjective interestingness, resulting in an automated discovery of interesting subgraphs. Our experiments on a socio-political database show the effectiveness of our technique. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ASONAM49781.2020.9381293 | 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) |
Keywords | DocType | ISSN |
social network,interesting subgraph discovery,subjective interestingness | Conference | 2473-9928 |
ISBN | Citations | PageRank |
978-1-7281-1057-8 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Subhasis Dasgupta | 1 | 17 | 4.72 |
| Amarnath Gupta | 2 | 1311 | 226.69 |