graph neural networks

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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Contrastive Graph Similarity Networks
Graph similarity learning is a significant and fundamental issue in the theory and analysis of graphs, which has been applied in a …
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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Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and …
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Projective Ranking-based GNN Evasion Attacks
Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. However, GNNs are at risk of adversarial …
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Reinforced, Incremental and Cross-lingual Event Detection From Social Messages
Detecting hot social events (e.g. political scandal, momentous meetings, natural hazards, etc.) from social messages iscrucial as it …
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Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks …
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Graph self-supervised learning: A survey
Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised …
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