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Domain-Adversarial Graph Neural Networks for Text Classification
Text classification, in cross-domain setting, is a challenging task. On the one hand, data from other domains are often useful to …
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Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning
Graph neural nets are emerging tools to represent network nodes for classification. However, existing approaches typically suffer from …
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Relation Structure-Aware Heterogeneous Graph Neural Network
Heterogeneous graphs with different types of nodes and edges are ubiquitous and have immense value in many applications. Existing works …
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Attributed Graph Clustering: A Deep Attentional Embedding Approach
Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on …
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Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. …
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Low-Bit Quantization for Attributed Network Representation Learning
Attributed network embedding plays an important role in transferring network data into compact vectors for effective network analysis. …
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Detecting Suicidal Ideation with Data Protection in Online Communities
Recent advances in Artificial Intelligence empower proactive social services that use virtual intelligent agents to automatically …
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Label Embedding with Partial Heterogeneous Contexts
Label embedding plays an important role in many real-world applications. To enhance the label relatedness captured by the embeddings, …
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Active discriminative network representation learning
Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination …
Adversarially regularized graph autoencoder for graph embedding
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding …
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