5 papers
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…
Data Augmentation on Graphs: A Technical Survey
Jiajun Zhou, Chenxuan Xie, Shengbo Gong +4
In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in com…
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
Chunxu Zhang, Guodong Long, Tianyi Zhou +3
Federated recommendation system usually trains a global model on the server without direct access to users' private data on their own devices. However, this separation of the recom…
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…
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…