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Pseudo-Riemannian Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally …
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Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
Graph contrastive learning (GCL) alleviates the heavy reliance on label information for graph representation learning (GRL) via …
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A Dynamic Variational Framework for Open-World Node Classification in Structured Sequences
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How Far are We from Robust Long Abstractive Summarization?
Abstractive summarization has made tremendous progress in recent years. In this work, we perform fine-grained human annotations to …
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Multi-Relational Graph Neural Architecture Search with Fine-grained Message Passing
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Unifying Graph Contrastive Learning with Flexible Contextual Scopes
Graph contrastive learning (GCL) has recently emerged as an effective learning paradigm to alleviate the reliance on labelling …
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Ultrahyperbolic Knowledge Graph Embeddings
Recent knowledge graph (KG) embeddings have been advanced by the hyperbolic geometry due to its superior capability for representing …
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CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic …
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Multi-Graph Fusion Networks for Urban Region Embedding
Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the …
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Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the …
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