graph neural networks

Learning Graph Embedding With Adversarial Training Methods
Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph …
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Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning
Graph neural nets are emerging tools to represent network nodes for classification. However, existing approaches typically suffer from …
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Relation Structure-Aware Heterogeneous Graph Neural Network
Heterogeneous graphs with different types of nodes and edges are ubiquitous and have immense value in many applications. Existing works …
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Attributed Graph Clustering: A Deep Attentional Embedding Approach
Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on …
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Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. …
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