Cluster analysis on dynamic graphs - Wang, Yujing (Nanyang Technological University, 2024)
In the era of big data, massive amount of graph data are generated from various domains like citation networks, biological systems, and social networks, leading to the need of effective analysis techniques. Graph clustering (methods for identifying closely connected groups within datasets) has become increasingly popular. Using neural networks has demonstrated potential for achieving effective clustering results. However, dynamic graphs pose unique challenges compared to static ones. Challenges include handling multiple interactions between nodes, evolving node features and cluster structures over time, and the absence of suitable evaluation metrics.