Data Mining

Going Deep: Graph Convolutional Ladder-shape Networks
Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric …
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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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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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CFOND: consensus factorization for co-clustering networked data
Networked data are common in domains where instances are characterized by both feature values and inter-dependency relationships. …
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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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