Name
Title | ||
|---|---|---|
Temporal Smoothness Framework: Analyzing and Exploring Evolutionary Transition Behavior in Dynamic Networks |
Abstract | ||
|---|---|---|
Real-world systems from a variety of domains, ranging from physics to medicine, can naturally be modelled as dynamic networks. Dynamic community detection is regarded as a fundamental tool to probe into the mechanisms of networks. Here, we describe a framework for tracking the network evolution over time, where each community is characterized by a series of transition events, which is one of the most influential evolutionary patterns in dynamic networks. The framework is used to motivate a temporal smoothness strategy for efficiently identifying dynamic communities and exploring the transition behavior of networks from community-level and node-level. Evaluations on two synthetic and real-world datasets containing embedded transition events demonstrate that the framework can successfully discover dynamic communities and analyze the transition behavior of networks. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICTAI52525.2021.00190 | 2021 IEEE 33RD INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2021) |
Keywords | DocType | ISSN |
Dynamic community detection, Transition behavior, Temporal smoothness | Conference | 1082-3409 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Limengzi Yuan | 1 | 0 | 0.34 |
| Qifeng Zhu | 2 | 0 | 0.34 |
| Yuchen Zheng | 3 | 0 | 0.34 |
| Wutong Dong | 4 | 0 | 0.34 |
| Yuxian Ke | 5 | 0 | 0.34 |
| Zhigang Li | 6 | 0 | 0.34 |