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Graph embedding clustering: Graph attention auto-encoder with cluster-specificity distribution
Neural Networks, 2021Towards exploring the topological structure of data, numerous graph embedding clustering methods have been developed in recent years, none of them takes into account the cluster-specificity distribution of the nodes representations, resulting in suboptimal clustering performance.
Huiling Xu +4 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Due to the wide existence of unlabeled graph-structured data (e.g. molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations ...
Man-Sheng Chen +4 more
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Due to the wide existence of unlabeled graph-structured data (e.g. molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations ...
Man-Sheng Chen +4 more
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2018
The goal of the thesis is the study of graphs emphasizing on laplacian matrices and spectral clustering. The first chapter constitutes an introduction to graphs. In the second chapter we introduce laplacian matrices, while in the third chapter we represent some of the most ubiquitous spectral clustering algorithms.
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The goal of the thesis is the study of graphs emphasizing on laplacian matrices and spectral clustering. The first chapter constitutes an introduction to graphs. In the second chapter we introduce laplacian matrices, while in the third chapter we represent some of the most ubiquitous spectral clustering algorithms.
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Detecting alternative graph clusterings
Physical Review E, 2012The problem of graph clustering or community detection has enjoyed a lot of attention in complex networks literature. A quality function, modularity, quantifies the strength of clustering and on maximization yields sensible partitions. However, in most real world networks, there are an exponentially large number of near-optimal partitions with some ...
Supreet, Mandala +2 more
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Multiview Consensus Graph Clustering
IEEE Transactions on Image Processing, 2019A graph is usually formed to reveal the relationship between data points and graph structure is encoded by the affinity matrix. Most graph-based multiview clustering methods use predefined affinity matrices and the clustering performance highly depends on the quality of graph.
Kun Zhan +3 more
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Computer Science Review, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Clustering with -regular graphs
Pattern Recognition, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kim, JK, Choi, S
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Graph Deep Clustering using Cluster Graph Conventional
2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2022Amal Shaheen +2 more
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Simple Contrastive Graph Clustering
IEEE Transactions on Neural Networks and Learning SystemsContrastive learning has recently attracted plenty of attention in deep graph clustering due to its promising performance. However, complicated data augmentations and time-consuming graph convolutional operations undermine the efficiency of these methods.
Yue Liu +7 more
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2012 IEEE 32nd International Conference on Distributed Computing Systems, 2012
In this paper, we propose techniques for clustering large-scale "streaming" graphs where the updates to a graph are given in form of a stream of vertex or edge additions and deletions. Our algorithm handles such updates in an online and incremental manner and it can be easily parallel zed.
Ahmed Eldawy +2 more
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In this paper, we propose techniques for clustering large-scale "streaming" graphs where the updates to a graph are given in form of a stream of vertex or edge additions and deletions. Our algorithm handles such updates in an online and incremental manner and it can be easily parallel zed.
Ahmed Eldawy +2 more
openaire +1 more source