Abstract
When information or infectious diseases spread over a network, in many practical cases, one can observe when nodes adopt information or become infected, but the underlying network is hidden. In this paper, we analyze the problem of finding communities of highly interconnected nodes, given only the infection times of nodes. We propose, analyze, and empirically compare several algorithms for this task. The most stable performance, that improves the current state-of-the-art, is obtained by our proposed heuristic approaches, that are agnostic to a particular graph structure and epidemic model.
Original language | English |
---|---|
Title of host publication | The Web Conference 2019 |
Subtitle of host publication | Proceedings of the World Wide Web Conference, WWW 2019 |
Place of Publication | New York |
Publisher | Association for Computing Machinery |
Pages | 3151-3157 |
Number of pages | 7 |
ISBN (Electronic) | 9781450366748 |
ISBN (Print) | 978-1-4503-6674-8 |
DOIs | |
Publication status | Published - 13 May 2019 |
Event | World Wide Web Conference, WWW 2019 - Hyatt Regency San Francisco hotel, San Francisco, United States Duration: 13 May 2019 → 17 May 2019 https://www2019.thewebconf.org/ |
Conference
Conference | World Wide Web Conference, WWW 2019 |
---|---|
Abbreviated title | WWW 2019 |
Country/Territory | United States |
City | San Francisco |
Period | 13/05/19 → 17/05/19 |
Internet address |
Keywords
- Community detection
- Information propagation
- Information cascades
- Network inference
- Likelihood optimization