Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multihop reasoning over the information found along other paths connecting a pair of entities. However, multi-hop …

Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric Laplacian smoothing operation to aggregate the feature information of one node with that of its neighbors. While they …

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in many graph data analysis tasks. However, they still suffer from two limitations for graph represen-tation learning. First, they exploit non-smoothing node fea-tures which may …

Meta-paths are important tools for a wide variety of data mining and network analysis tasks in Heterogeneous Information Networks (HINs), due to their flexibility and interpretability to capture the complex semantic relation among objects. To date, …

Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying …

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