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Simple and Efficient Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNNs) deliver the powerful capability to embed rich structural and semantic information of a …
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GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
Most existing machine learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn …
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Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
Continuous-time dynamic graphs naturally abstract many real-world systems, such as social and transactional networks. While the …
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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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