Attention-driven Graph Clustering Network
- URL: http://arxiv.org/abs/2108.05499v1
- Date: Thu, 12 Aug 2021 02:30:38 GMT
- Title: Attention-driven Graph Clustering Network
- Authors: Zhihao Peng, Hui Liu, Yuheng Jia, Junhui Hou
- Abstract summary: We propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN)
AGCN exploits a heterogeneous-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature.
AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion.
- Score: 49.040136530379094
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The combination of the traditional convolutional network (i.e., an
auto-encoder) and the graph convolutional network has attracted much attention
in clustering, in which the auto-encoder extracts the node attribute feature
and the graph convolutional network captures the topological graph feature.
However, the existing works (i) lack a flexible combination mechanism to
adaptively fuse those two kinds of features for learning the discriminative
representation and (ii) overlook the multi-scale information embedded at
different layers for subsequent cluster assignment, leading to inferior
clustering results. To this end, we propose a novel deep clustering method
named Attention-driven Graph Clustering Network (AGCN). Specifically, AGCN
exploits a heterogeneity-wise fusion module to dynamically fuse the node
attribute feature and the topological graph feature. Moreover, AGCN develops a
scale-wise fusion module to adaptively aggregate the multi-scale features
embedded at different layers. Based on a unified optimization framework, AGCN
can jointly perform feature learning and cluster assignment in an unsupervised
fashion. Compared with the existing deep clustering methods, our method is more
flexible and effective since it comprehensively considers the numerous and
discriminative information embedded in the network and directly produces the
clustering results. Extensive quantitative and qualitative results on commonly
used benchmark datasets validate that our AGCN consistently outperforms
state-of-the-art methods.
Related papers
- A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation [14.327262299413789]
We develop ANCKA as a versatile attributed network clustering framework, capable of attributed graph clustering (AGC), attributed multiplex graph clustering (AMGC), and attributed hypergraph clustering (AHC)
We have conducted extensive experiments to compare our methods with 19 competitors on 8 attributed hypergraphs, 16 competitors on 6 attributed graphs, and 16 competitors on 3 attributed multiplex graphs, all demonstrating the superb clustering quality and efficiency of our methods.
arXiv Detail & Related papers (2024-08-10T06:59:51Z) - Reinforcement Graph Clustering with Unknown Cluster Number [91.4861135742095]
We propose a new deep graph clustering method termed Reinforcement Graph Clustering.
In our proposed method, cluster number determination and unsupervised representation learning are unified into a uniform framework.
In order to conduct feedback actions, the clustering-oriented reward function is proposed to enhance the cohesion of the same clusters and separate the different clusters.
arXiv Detail & Related papers (2023-08-13T18:12:28Z) - Deep Embedded Clustering with Distribution Consistency Preservation for
Attributed Networks [15.895606627146291]
In this study, we propose an end-to-end deep embedded clustering model for attributed networks.
It utilizes graph autoencoder and node attribute autoencoder to respectively learn node representations and cluster assignments.
The proposed model achieves significantly better or competitive performance compared with the state-of-the-art methods.
arXiv Detail & Related papers (2022-05-28T02:35:34Z) - Multilayer Graph Contrastive Clustering Network [14.864683908759327]
We propose a generic and effective autoencoder framework for multilayer graph clustering named Multilayer Graph Contrastive Clustering Network (MGCCN)
MGCCN consists of three modules: (1)Attention mechanism is applied to better capture the relevance between nodes and neighbors for better node embeddings; (2) To better explore the consistent information in different networks, a contrastive fusion strategy is introduced; and (3)MGCCN employs a self-supervised component that iteratively strengthens the node embedding and clustering.
arXiv Detail & Related papers (2021-12-28T07:21:13Z) - Deep Attention-guided Graph Clustering with Dual Self-supervision [49.040136530379094]
We propose a novel method, namely deep attention-guided graph clustering with dual self-supervision (DAGC)
We develop a dual self-supervision solution consisting of a soft self-supervision strategy with a triplet Kullback-Leibler divergence loss and a hard self-supervision strategy with a pseudo supervision loss.
Our method consistently outperforms state-of-the-art methods on six benchmark datasets.
arXiv Detail & Related papers (2021-11-10T06:53:03Z) - Self-supervised Contrastive Attributed Graph Clustering [110.52694943592974]
We propose a novel attributed graph clustering network, namely Self-supervised Contrastive Attributed Graph Clustering (SCAGC)
In SCAGC, by leveraging inaccurate clustering labels, a self-supervised contrastive loss, are designed for node representation learning.
For the OOS nodes, SCAGC can directly calculate their clustering labels.
arXiv Detail & Related papers (2021-10-15T03:25:28Z) - Learning Hierarchical Graph Neural Networks for Image Clustering [81.5841862489509]
We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities.
Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.
arXiv Detail & Related papers (2021-07-03T01:28:42Z) - CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional
Network for Clustering [51.62959830761789]
We propose a cross-attention based deep clustering framework, named Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN)
CaEGCN contains four main modules: cross-attention fusion, Content Auto-encoder, Graph Convolutional Auto-encoder and self-supervised model.
Experimental results on different types of datasets prove the superiority and robustness of the proposed CaEGCN.
arXiv Detail & Related papers (2021-01-18T05:21:59Z) - Deep Fusion Clustering Network [38.540761683389135]
We propose a Deep Fusion Clustering Network (DFCN) for deep clustering.
In our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder.
Experiments on six benchmark datasets have demonstrated that the proposed DFCN consistently outperforms the state-of-the-art deep clustering methods.
arXiv Detail & Related papers (2020-12-15T09:37:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.