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Graph Contrastive Learning for Tracking Dynamic Communities in Temporal Networks
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Xiaoke Ma
IEEE Transactions on Emerging Topics in Computational Intelligence, Volume: 8, Issue: 5, Pages: 3422 - 3435
Swansea University Author:
Xianghua Xie
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DOI (Published version): 10.1109/tetci.2024.3386844
Abstract
Temporal networks are ubiquitous because complex systems in nature and society are evolving, and tracking dynamic communities is critical for revealing the mechanism of systems. Moreover, current algorithms utilize temporal smoothness framework to balance clustering accuracy at current time and clus...
| Published in: | IEEE Transactions on Emerging Topics in Computational Intelligence |
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| ISSN: | 2471-285X |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2024
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa69415 |
| Abstract: |
Temporal networks are ubiquitous because complex systems in nature and society are evolving, and tracking dynamic communities is critical for revealing the mechanism of systems. Moreover, current algorithms utilize temporal smoothness framework to balance clustering accuracy at current time and clustering drift at historical time, which are criticized for failing to characterize the temporality of networks and determine its importance. To overcome these problems, we propose a novel algorithm by joining Non-negative matrix factorization and Contrastive learning for Dynamic Community detection (jNCDC). Specifically, jNCDC learns the features of vertices by projecting successive snapshots into a shared subspace to learn the low-dimensional representation of vertices with matrix factorization. Subsequently, it constructs an evolution graph to explicitly measure relations of vertices by representing vertices at current time with features at historical time, paving a way to characterize the dynamics of networks at the vertex-level. Finally, graph contrastive learning utilizes the roles of vertices to select positive and negative samples to further improve the quality of features. These procedures are seamlessly integrated into an overall objective function, and optimization rules are deduced. To the best of our knowledge, jNCDC is the first graph contrastive learning for dynamic community detection, that provides an alternative for the current temporal smoothness framework. Experimental results demonstrate that jNCDC is superior to the state-of-the-art approaches in terms of accuracy. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
This work was supported in part by the National Natural Science Foundation of China under Grant 62272361 and in part by the Shaanxi Natural Science Funds for Distinguished Young Scholar Program under Grant 2022JC-38. |
| Issue: |
5 |
| Start Page: |
3422 |
| End Page: |
3435 |