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Found 5 papers, 2 papers with code
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective
Guided by the bound, we design a GCL framework named InfoAdv with enhanced generalization ability, which jointly optimizes the generalization metric and InfoMax to strike the right balance between pretext task fitting and the generalization ability on downstream tasks.
Towards Powerful Graph Neural Networks: Diversity Matters
For a target node, diverse sampling offers it diverse neighborhoods, i. e., rooted sub-graphs, and the representation of target node is finally obtained via aggregating the representation of diverse neighborhoods obtained using any GNN model.
Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning
Graph convolutional networks gain remarkable success in semi-supervised learning on graph structured data.
ANAE: Learning Node Context Representation for Attributed Network Embedding
In this paper, we address attributed network embedding from a novel perspective, i. e., learning node context representation for each node via modeling its attributed local subgraph.
DeepHawkes: Bridging the gap between prediction and understanding of information cascades
In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade.
