scholarly journals An improved attributed graph clustering method for discovering expert role in real-world communities

2017 ◽  
Author(s):  
isma hamid ◽  
Yu Wu ◽  
Qamar Nawaz ◽  
Runqian Zhao
Keyword(s):  
Real World ◽  
Graph Clustering ◽  
Clustering Method ◽  
Attributed Graph

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.

The Optimization Algorithm Based on Vector Space Model to Determine the Influence of Microblogging Text

2014 ◽  
Vol 571-572 ◽  
pp. 278-281 ◽  
Author(s):  
Yu Chen Dong ◽  
Yan Liu ◽  
Jun Yong Luo ◽  
Jin Zhang
Keyword(s):  
Impact Factor ◽  
Vector Space ◽  
Optimization Algorithm ◽  
Real World ◽  
Information Dissemination ◽  
Evaluation Method ◽  
Vector Space Model ◽  
Clustering Method ◽  
Space Model ◽  
Dissemination Process

This article uses the vector space model to express microblogging text in quantity, via the cosine of the angle of text clustering method to calculate the similarity between the text. We can generate alternative microblogging hot cluster by centralized method, by adding the "microblogging impact Factor " into vector text mold of alternative hotspot clusters. In order to simulate the exclusive advantage of "star" in the information dissemination process of the real world, we use the alternative hotspot clusters which already weighted the "microblogging impact factor" to re-evaluate its characteristics of vector, to optimize the hot microblogging discovery algorithm and to make the sorting results more in line with the law of information dissemination, finally to give some suggestions and opinions on hot microblogging evaluation method.

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.

A signal-diffusion-based spectral clustering method for community detection

2019 ◽  
Vol 18 (01) ◽  
pp. 1941019
Author(s):  
Zheng Qiong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Adjacency Matrix ◽  
Spectral Clustering ◽  
Time Complexity ◽  
Detection Method ◽  
Signal Transmission ◽  
Transmission Mechanism ◽  
Clustering Method ◽  
Spectral Clustering Method

As the traditional spectral community detection method uses adjacency matrix for clustering which might cause the problem of accuracy reduction, we proposed a signal-diffusion-based spectral clustering for community detection. This method solves the problem that unfixed total signal as using the signal transmission mechanism, provides optimization of algorithm time complexity, improves the performance of spectral clustering with construction of Laplacian based on signal diffusion. Experiments prove that the method reaches as better performance on real-world network and Lancichinetti–Fortunato–Radicchi (LFR) benchmark.

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