Graph Neural Networks

Deep Neighbor-aware Embedding for Node Clustering in Attributed Graphs
Node clustering aims to partition the vertices in a graph into multiple groups or communities. Existing studies have mostly focused on …
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Cyclic label propagation for graph semi-supervised learning
Graph neural networks (GNNs) have emerged as effective approaches for graph analysis, especially in the scenario of semi-supervised …
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OpenWGL: Open-World Graph Learning for Unseen Class Node Classification
Graph learning, such as node classification, is typically carried out in a closed-world setting. A number of nodes are labeled, and the …
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Temporal Network Embedding for Link Prediction via VAE joint Attention Mechanism
Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different …
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Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation …
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Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification
Recently, Graph Convolutional Network (GCN) has progressed significantly and gained increasing attention in hyperspectral image (HSI) …
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Heterogeneous Graph Attention Network for Small and Medium-Sized Enterprises Bankruptcy Prediction
Credit assessment for Small and Medium-sized Enterprises (SMEs) is of great interest to financial institutions such as commercial banks …
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A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities …
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Graph Learning: A Survey
Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a wide …
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Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in …
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