Relational Learning

Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning
Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge …
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Going Deep: Graph Convolutional Ladder-shape Networks
Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric …
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GSSNN: Graph Smoothing Splines Neural Networks
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in many graph data analysis tasks. However, they still suffer …
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Reinforcement Learning based Meta-path Discovery in Large-scale Heterogeneous Information Networks
Meta-paths are important tools for a wide variety of data mining and network analysis tasks in Heterogeneous Information Networks …
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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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