A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges
Graph-structured data exhibits universality and widespread applicability across diverse
domains, such as social network analysis, biochemistry, financial fraud detection, and …
domains, such as social network analysis, biochemistry, financial fraud detection, and …
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …
be connected, has been commonly believed to be the main reason for the superiority of …
Maui: Black-Box Edge Privacy Attack on Graph Neural Networks
Graphs are ubiquitous data structures with nodes representing objects and edges
representing relationships between them. Graph Neural Networks (GNNs) have recently …
representing relationships between them. Graph Neural Networks (GNNs) have recently …
A Privacy-Preserving Graph Neural Network for Network Intrusion Detection
With the ever-growing attention on communication security, machine learning-based
network intrusion detection system (NIDS) is widely utilized to meet different security …
network intrusion detection system (NIDS) is widely utilized to meet different security …