Data Mining

Going Deep: Graph Convolutional Ladder-shape Networks

Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric Laplacian smoothing operation to aggregate the feature information of one node with that of its neighbors. While they …

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 from two limitations for graph represen-tation learning. First, they exploit non-smoothing node fea-tures which may …

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. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying …

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. Existing attributed network embedding models are designed either in continuous Euclidean spaces which introduce …

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. Finding cluster structures for networked instances and discovering representative features for each cluster represent a …

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 algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph …