Graph neural networks (GNNs) based methods have achieved impressive performance on the node clustering task. However, they are designed on the homophilic assumption of the graph, and clustering on the heterophilic graph is overlooked. Hence, clustering on real-world graphs with various levels of homophily poses a new challenge to the graph research community. For example, previous works either preserve structural information by ignoring the impact of heterophily in the graph structure or only focus on node-level consistency by ignoring class-level consistency.
This project invites bachelor's and master's students to explore heterophily in multi-relational graphs (multiplex networks). Specifically, the goal is to construct the multi-hop relationships of selected layer(s) - relation type(s) - between every node to improve homophily and reduce the impact of heterophily in the graph structure. The initial idea is outlined, and students will be responsible for conducting experiments on published approaches and evaluating them on real-world datasets.
Ideal candidates should have a foundation in machine learning principles, proficiency in Python, and basic knowledge of graph neural networks or deep learning.
[1] Yudi Huang, Ci Nie, Hongqing He, Yujie Mo, Yonghua Zhu, Guoqiu Wen, and Xiaofeng Zhu. Multiplex Graph Representation Learning with Homophily and Consistency. AAAI 2025.
[2] Erlin Pan and Zhao Kang. Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering. ICML 2023.
[3] Sitao Luan et al. The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges. ArXiv 2024.
Supervisors: Ylli Sadikaj, Claudia Plant
Contact: Ylli Sadikaj
