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 …
A Survey on Self-Supervised Pre-Training of Graph Foundation Models: A Knowledge-Based Perspective
Graph self-supervised learning is now a go-to method for pre-training graph foundation
models, including graph neural networks, graph transformers, and more recent large …
models, including graph neural networks, graph transformers, and more recent large …
Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Broad Physical Dynamics Learning
Incorporating Euclidean symmetries (eg rotation equivariance) as inductive biases into
graph neural networks has improved their generalization ability and data efficiency in …
graph neural networks has improved their generalization ability and data efficiency in …
ProCom: A Few-shot Targeted Community Detection Algorithm
Targeted community detection aims to distinguish a particular type of community in the
network. This is an important task with a lot of real-world applications, eg, identifying fraud …
network. This is an important task with a lot of real-world applications, eg, identifying fraud …
ARC: A Generalist Graph Anomaly Detector with In-Context Learning
Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the
majority within a graph, has garnered significant attention. However, current GAD methods …
majority within a graph, has garnered significant attention. However, current GAD methods …
Temporal Enhanced Multimodal Graph Neural Networks for Fake News Detection
Fake news detection is of crucial importance and has received great attention. However, the
existing fake news detection methods rarely consider the news release time, which limits the …
existing fake news detection methods rarely consider the news release time, which limits the …
Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
Incorporating Euclidean symmetries (eg rotation equivariance) as inductive biases into
graph neural networks has improved their generalization ability and data efficiency in …
graph neural networks has improved their generalization ability and data efficiency in …
ProG: A Graph Prompt Learning Benchmark
Artificial general intelligence on graphs has shown significant advancements across various
applications, yet the traditional'Pre-train & Fine-tune'paradigm faces inefficiencies and …
applications, yet the traditional'Pre-train & Fine-tune'paradigm faces inefficiencies and …
Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models
Given the ubiquity of graph data, it is intriguing to ask: Is it possible to train a graph
foundation model on a broad range of graph data across diverse domains? A major hurdle …
foundation model on a broad range of graph data across diverse domains? A major hurdle …
AnyGraph: Graph Foundation Model in the Wild
L Xia, C Huang - arXiv preprint arXiv:2408.10700, 2024 - arxiv.org
The growing ubiquity of relational data structured as graphs has underscored the need for
graph learning models with exceptional generalization capabilities. However, current …
graph learning models with exceptional generalization capabilities. However, current …