A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions
Graphs are structured data that models complex relations between real-world entities.
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …
Relief: Reinforcement learning empowered graph feature prompt tuning
The advent of the" pre-train, prompt" paradigm has recently extended its generalization
ability and data efficiency to graph representation learning, following its achievements in …
ability and data efficiency to graph representation learning, following its achievements 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 …
Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis
Q Wang, X Sun, H Cheng - arxiv preprint arxiv:2410.01635, 2024 - arxiv.org
In recent years, graph prompting has emerged as a promising research direction, enabling
the learning of additional tokens or subgraphs appended to the original graphs without …
the learning of additional tokens or subgraphs appended to the original graphs without …
Towards Graph Prompt Learning: A Survey and Beyond
Large-scale" pre-train and prompt learning" paradigms have demonstrated remarkable
adaptability, enabling broad applications across diverse domains such as question …
adaptability, enabling broad applications across diverse domains such as question …