scholarly journals I-Louvain: An Attributed Graph Clustering Method

2015 ◽  
pp. 181-192 ◽  
Author(s):  
David Combe ◽  
Christine Largeron ◽  
Mathias Géry ◽  
Előd Egyed-Zsigmond
Keyword(s):  
Graph Clustering ◽  
Clustering Method ◽  
Attributed Graph

An Efficient Method for Attributed Graph Clustering

2014 ◽  
Vol 36 (8) ◽  
pp. 1704-1713 ◽  
Author(s):  
Ye WU ◽  
Zhi-Nong ZHONG ◽  
Wei XIONG ◽  
Luo CHEN ◽  
Ning JING
Keyword(s):  
Efficient Method ◽  
Graph Clustering ◽  
Attributed Graph

scholarly journals Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 4215-4222
Author(s):  
Binyuan Hui ◽  
Pengfei Zhu ◽  
Qinghua Hu
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Graph Clustering ◽  
Gaussian Mixture ◽  
Complex Data ◽  
Convolutional Networks ◽  
Attributed Graph ◽  
Node Classification

Graph convolutional networks (GCN) have achieved promising performance in attributed graph clustering and semi-supervised node classification because it is capable of modeling complex graphical structure, and jointly learning both features and relations of nodes. Inspired by the success of unsupervised learning in the training of deep models, we wonder whether graph-based unsupervised learning can collaboratively boost the performance of semi-supervised learning. In this paper, we propose a multi-task graph learning model, called collaborative graph convolutional networks (CGCN). CGCN is composed of an attributed graph clustering network and a semi-supervised node classification network. As Gaussian mixture models can effectively discover the inherent complex data distributions, a new end to end attributed graph clustering network is designed by combining variational graph auto-encoder with Gaussian mixture models (GMM-VGAE) rather than the classic k-means. If the pseudo-label of an unlabeled sample assigned by GMM-VGAE is consistent with the prediction of the semi-supervised GCN, it is selected to further boost the performance of semi-supervised learning with the help of the pseudo-labels. Extensive experiments on benchmark graph datasets validate the superiority of our proposed GMM-VGAE compared with the state-of-the-art attributed graph clustering networks. The performance of node classification is greatly improved by our proposed CGCN, which verifies graph-based unsupervised learning can be well exploited to enhance the performance of semi-supervised learning.

scholarly journals Attributed Graph Clustering via Adaptive Graph Convolution

2019 ◽  
Author(s):  
Xiaotong Zhang ◽  
Han Liu ◽  
Qimai Li ◽  
Xiao-Ming Wu
Keyword(s):  
State Of The Art ◽  
Cluster Structure ◽  
Graph Clustering ◽  
Convolutional Networks ◽  
Global Cluster ◽  
Attributed Graph ◽  
Benchmark Datasets ◽  
Node Attributes ◽  
Content Information ◽  
Convolution Method

Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content information, and several recent methods based on it have achieved promising clustering performance on some real attributed networks. However, there is limited understanding of how graph convolution affects clustering performance and how to properly use it to optimize performance for different graphs. Existing methods essentially use graph convolution of a fixed and low order that only takes into account neighbours within a few hops of each node, which underutilizes node relations and ignores the diversity of graphs. In this paper, we propose an adaptive graph convolution method for attributed graph clustering that exploits high-order graph convolution to capture global cluster structure and adaptively selects the appropriate order for different graphs. We establish the validity of our method by theoretical analysis and extensive experiments on benchmark datasets. Empirical results show that our method compares favourably with state-of-the-art methods.

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