3 papers
cs.LG2024
Enhancing Node Representations for Real-World Complex Networks with Topological Augmentation
Xiangyu Zhao, Zehui Li, Mingzhu Shen +3
Graph augmentation methods play a crucial role in improving the performance and enhancing generalisation capabilities in Graph Neural Networks (GNNs). Existing graph augmentation m…
cs.LG2024
Will More Expressive Graph Neural Networks do Better on Generative Tasks?
Xiandong Zou, Xiangyu Zhao, Pietro Liò +1
Graph generation poses a significant challenge as it involves predicting a complete graph with multiple nodes and edges based on simply a given label. This task also carries fundam…
cs.LG2024
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
Zehui Li, Xiangyu Zhao, Mingzhu Shen +3
Graphs are widely used to encapsulate a variety of data formats, but real-world networks often involve complex node relations beyond only being pairwise. While hypergraphs and hier…