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GSSNN: Graph Smoothing Splines Neural Networks
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in many graph data analysis tasks. However, they still suffer …
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Reinforcement Learning based Meta-path Discovery in Large-scale Heterogeneous Information Networks
Meta-paths are important tools for a wide variety of data mining and network analysis tasks in Heterogeneous Information Networks …
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An Effective and Explainable Deep Fusion Network for Affect Recognition Using Physiological Signals
Affective computing is an emerging research area which provides insights on human’s mental state through human-machine interaction. …
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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