Relevant bibliographies by topics / Deep graph clustering

Academic literature on the topic 'Deep graph clustering'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Deep graph clustering"

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Zhang, Xiaoran, Xuanting Xie, and Zhao Kang. "Graph Learning for Attributed Graph Clustering." Mathematics 10, no. 24 (December 19, 2022): 4834. http://dx.doi.org/10.3390/math10244834.

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Due to the explosive growth of graph data, attributed graph clustering has received increasing attention recently. Although deep neural networks based graph clustering methods have achieved impressive performance, the huge amount of training parameters make them time-consuming and memory- intensive. Moreover, real-world graphs are often noisy or incomplete and are not optimal for the clustering task. To solve these problems, we design a graph learning framework for the attributed graph clustering task in this study. We firstly develop a shallow model for learning a fine-grained graph from smoothed data, which sufficiently exploits both node attributes and topology information. A regularizer is also designed to flexibly explore the high-order information hidden in the data. To further reduce the computation complexity, we then propose a linear method with respect to node number n, where a smaller graph is learned based on importance sampling strategy to select m(m≪n) anchors. Extensive experiments on six benchmark datasets demonstrate that our proposed methods are not only effective but also more efficient than state-of-the-art techniques. In particular, our method surpasses many recent deep learning approaches.
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Tu, Wenxuan, Sihang Zhou, Xinwang Liu, Xifeng Guo, Zhiping Cai, En Zhu, and Jieren Cheng. "Deep Fusion Clustering Network." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9978–87. http://dx.doi.org/10.1609/aaai.v35i11.17198.

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Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structure information for clustering performance enhancement. However, we observe that existing literature 1) lacks a dynamic fusion mechanism to selectively integrate and refine the information of graph structure and node attributes for consensus representation learning; 2) fails to extract information from both sides for robust target distribution (i.e., “groundtruth” soft labels) generation. To tackle the above issues, we propose a Deep Fusion Clustering Network (DFCN). Specifically, 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 for consensus representation learning. Also, a reliable target distribution generation measure and a triplet self-supervision strategy, which facilitate cross-modality information exploitation, are designed for network training. Extensive experiments on six benchmark datasets have demonstrated that the proposed DFCN consistently outperforms the state-of-the-art deep clustering methods.
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Li, Xunkai, Youpeng Hu, Yaoqi Sun, Ji Hu, Jiyong Zhang, and Meixia Qu. "A Deep Graph Structured Clustering Network." IEEE Access 8 (2020): 161727–38. http://dx.doi.org/10.1109/access.2020.3020192.

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Ma, Guixiang, Nesreen K. Ahmed, Theodore L. Willke, and Philip S. Yu. "Deep graph similarity learning: a survey." Data Mining and Knowledge Discovery 35, no. 3 (March 24, 2021): 688–725. http://dx.doi.org/10.1007/s10618-020-00733-5.

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AbstractIn many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep graph similarity learning. We propose a systematic taxonomy for the methods and applications. Finally, we discuss the challenges and future directions for this problem.
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Liao, Huifa, Jie Hu, Tianrui Li, Shengdong Du, and Bo Peng. "Deep linear graph attention model for attributed graph clustering." Knowledge-Based Systems 246 (June 2022): 108665. http://dx.doi.org/10.1016/j.knosys.2022.108665.

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Liu, Yue, Wenxuan Tu, Sihang Zhou, Xinwang Liu, Linxuan Song, Xihong Yang, and En Zhu. "Deep Graph Clustering via Dual Correlation Reduction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7603–11. http://dx.doi.org/10.1609/aaai.v36i7.20726.

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Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. However, we observe that, in the process of node encoding, existing methods suffer from representation collapse which tends to map all data into the same representation. Consequently, the discriminative capability of the node representation is limited, leading to unsatisfied clustering performance. To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner. Specifically, in our method, we first design a siamese network to encode samples. Then by forcing the cross-view sample correlation matrix and cross-view feature correlation matrix to approximate two identity matrices, respectively, we reduce the information correlation in the dual-level, thus improving the discriminative capability of the resulting features. Moreover, in order to alleviate representation collapse caused by over-smoothing in GCN, we introduce a propagation regularization term to enable the network to gain long-distance information with the shallow network structure. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of the proposed DCRN against the existing state-of-the-art methods. The code of DCRN is available at https://github.com/yueliu1999/DCRN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github.
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Zhao, Yulin, Xunkai Li, Yinlin Zhu, Jin Li, Shuo Wang, and Bin Jiang. "A Scalable Deep Network for Graph Clustering via Personalized PageRank." Applied Sciences 12, no. 11 (May 29, 2022): 5502. http://dx.doi.org/10.3390/app12115502.

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Recently, many models based on the combination of graph convolutional networks and deep learning have attracted extensive attention for their superior performance in graph clustering tasks. However, the existing models have the following limitations: (1) Existing models are limited by the calculation method of graph convolution, and their computational cost will increase exponentially as the graph scale grows. (2) Stacking too many convolutional layers causes the over-smoothing issue and neglects the local graph structure. (3) Expanding the range of the neighborhood and the model depth together is difficult due to the orthogonal relationship between them. Inspired by personalized pagerank and auto-encoder, we conduct the node-wise graph clustering task in the undirected simple graph as the research direction and propose a Scalable Deep Network (SDN) for graph clustering via personalized pagerank. Specifically, we utilize the combination of multi-layer perceptrons and linear propagation layer based on personalized pagerank as the backbone network (i.e., the Quasi-GNN module) and employ a DNN module for auto-encoder to learn different dimensions embeddings. After that, SDN combines the two embeddings correspondingly; then, it utilizes a dual self-supervised module to constrain the training of the embedding and clustering process. Our proposed Quasi-GNN module reduces the computational costs of traditional GNN models in a decoupled approach and solves the orthogonal relationship between the model depth and the neighborhood range. Meanwhile, it also alleviates the degraded clustering effect caused by the over-smoothing issue. We conducted experiments on five widely used graph datasets. The experimental results demonstrate that our model achieves state-of-the-art performance.
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Qi, Chao, Jianming Zhang, Hongjie Jia, Qirong Mao, Liangjun Wang, and Heping Song. "Deep face clustering using residual graph convolutional network." Knowledge-Based Systems 211 (January 2021): 106561. http://dx.doi.org/10.1016/j.knosys.2020.106561.

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Qin, Shan, Ting Jiang, Sheng Wu, Ning Wang, and Xinran Zhao. "Graph Convolution-Based Deep Clustering for Speech Separation." IEEE Access 8 (2020): 82571–80. http://dx.doi.org/10.1109/access.2020.2989833.

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Hu, Ruiqi, Shirui Pan, Guodong Long, Qinghua Lu, Liming Zhu, and Jing Jiang. "Going Deep: Graph Convolutional Ladder-Shape Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2838–45. http://dx.doi.org/10.1609/aaai.v34i03.5673.

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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 have achieved great success in semi-supervised node classification on graphs, current approaches suffer from the over-smoothing problem when the depth of the neural networks increases, which always leads to a noticeable degradation of performance. To solve this problem, we present graph convolutional ladder-shape networks (GCLN), a novel graph neural network architecture that transmits messages from shallow layers to deeper layers to overcome the over-smoothing problem and dramatically extend the scale of the neural networks with improved performance. We have validated the effectiveness of proposed GCLN at a node-wise level with a semi-supervised task (node classification) and an unsupervised task (node clustering), and at a graph-wise level with graph classification by applying a differentiable pooling operation. The proposed GCLN outperforms original GCNs, deep GCNs and other state-of-the-art GCN-based models for all three tasks, which were designed from various perspectives on six real-world benchmark data sets.
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Dissertations / Theses on the topic "Deep graph clustering"

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Kilinc, Ismail Ozsel. "Graph-based Latent Embedding, Annotation and Representation Learning in Neural Networks for Semi-supervised and Unsupervised Settings." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7415.

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Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and unsupervised learning problems. Specifically, we propose a novel regularization technique called Graph-based Activity Regularization (GAR) and a novel output layer modification called Auto-clustering Output Layer (ACOL) which can be used separately or collaboratively to develop scalable and efficient learning frameworks for semi-supervised and unsupervised settings. First, singularly using the GAR technique, we develop a framework providing an effective and scalable graph-based solution for semi-supervised settings in which there exists a large number of observations but a small subset with ground-truth labels. The proposed approach is natural for the classification framework on neural networks as it requires no additional task calculating the reconstruction error (as in autoencoder based methods) or implementing zero-sum game mechanism (as in adversarial training based methods). We demonstrate that GAR effectively and accurately propagates the available labels to unlabeled examples. Our results show comparable performance with state-of-the-art generative approaches for this setting using an easier-to-train framework. Second, we explore a different type of semi-supervised setting where a coarse level of labeling is available for all the observations but the model has to learn a fine, deeper level of latent annotations for each one. Problems in this setting are likely to be encountered in many domains such as text categorization, protein function prediction, image classification as well as in exploratory scientific studies such as medical and genomics research. We consider this setting as simultaneously performed supervised classification (per the available coarse labels) and unsupervised clustering (within each one of the coarse labels) and propose a novel framework combining GAR with ACOL, which enables the network to perform concurrent classification and clustering. We demonstrate how the coarse label supervision impacts performance and the classification task actually helps propagate useful clustering information between sub-classes. Comparative tests on the most popular image datasets rigorously demonstrate the effectiveness and competitiveness of the proposed approach. The third and final setup builds on the prior framework to unlock fully unsupervised learning where we propose to substitute real, yet unavailable, parent- class information with pseudo class labels. In this novel unsupervised clustering approach the network can exploit hidden information indirectly introduced through a pseudo classification objective. We train an ACOL network through this pseudo supervision together with unsupervised objective based on GAR and ultimately obtain a k-means friendly latent representation. Furthermore, we demonstrate how the chosen transformation type impacts performance and helps propagate the latent information that is useful in revealing unknown clusters. Our results show state-of-the-art performance for unsupervised clustering tasks on MNIST, SVHN and USPS datasets with the highest accuracies reported to date in the literature.
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Alise, Dario Fioravante. "Algoritmo di "Label Propagation" per il clustering di documenti testuali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14388/.

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Negli ultimi anni del secolo scorso l’avvento di Internet ha permesso di avere a disposizione innumerevoli quantità di testi consultabili online, provenienti sia da libri e riviste, sia da nuove forme di comunicazione della rete quali email, forum, newsgroup e chat. 
Le soluzioni adottate nel settore del Text Mining (d’ora in poi abbreviato in TM), che è l’estensione del Data Mining rivolto a dati testuali non strutturati, si basano su fondamenti informatici, statistici e linguistici e sono in linea di principio applicabili a documenti di qualsiasi dimensione.
Con l’avvento dei Social Networks la quantità e la dimensione dei dati testuali da analizzare è cresciuta in maniera sub-esponenziale e benché le tecniche disponibili rimangono comunque valide e applicabili, negli ultimi quattro/cinque anni la ricerca si è concentrata su una tecnica emergente, chiamata semantic hashing, che consente di mappare documenti di qualunque tipo in stringhe binarie.
Sfruttando questa nuova branca di ricerca, lo scopo principale di questa tesi è di definire, progettare ed implementare un algoritmo di clustering che prendendo in input questi dati binari sia in grado di etichettare tali dati in maniera più precisa ed in tempi minori rispetto a quanto fanno gli altri approcci presenti in letteratura.
Dopo una descrizione di quelle che sono le principali tecniche di TM, seguirà una trattazione relativa all’hashing semantico e alle basi teoriche su cui questo si fonda per poi introdurre l’algoritmo adoperato per fare clustering, presentandone lo schema architetturale di funzionamento e la relativa implementazione. 
Infine saranno comparati e analizzati i risultati dell’esecuzione dell’algoritmo, chiamato d’ora in poi Label Propagation (abbreviato in LP), con quelli ottenuti con tecniche standard.
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Book chapters on the topic "Deep graph clustering"

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Li, Sun, Zihan Wang, Yong Li, Yang Yu, Wenbo Li, Hongliang Liu, Rong Song, and Lei Zhu. "Deep Structured Graph Clustering Network." In Data Mining and Big Data, 223–39. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-9297-1_17.

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Demir, Uğur, Mohammed Amine Gharsallaoui, and Islem Rekik. "Clustering-Based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates." In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, 109–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60365-6_11.

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Chowdhary, Chiranji Lal. "Simple Linear Iterative Clustering (SLIC) and Graph Theory-Based Image Segmentation." In Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security, 157–70. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3299-7.ch010.

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With the extensive application of deep acquisition devices, it has become more feasible to access deep data. The accuracy of image segmentation can be improved by depth data as an additional feature. The current research interests in simple linear iterative clustering (SLIC) are because it is a simple and efficient superpixel segmentation method, and it is initially applied for optical images. This mainly comprises three operation steps (i.e., initialization, local k-means clustering, and postprocessing). A scheme to develop the image over-segmentation task is introduced in this chapter. It considers the pixels of an image with simple linear iterative clustering and graph theory-based algorithm. In this regard, the main contribution is to provide a method for extracting superpixels with greater adherence to the edges of the regions. The experimental tests will consider biomedical grayscales. The robustness and effectiveness will be verified by quantitative and qualitative results.
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Bakhshandegan Moghaddam, Farshad, Carsten Draschner, Jens Lehmann, and Hajira Jabeen. "Literal2Feature: An Automatic Scalable RDF Graph Feature Extractor." In Studies on the Semantic Web. IOS Press, 2021. http://dx.doi.org/10.3233/ssw210036.

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The last decades have witnessed significant advancements in terms of data generation, management, and maintenance. This has resulted in vast amounts of data becoming available in a variety of forms and formats including RDF. As RDF data is represented as a graph structure, applying machine learning algorithms to extract valuable knowledge and insights from them is not straightforward, especially when the size of the data is enormous. Although Knowledge Graph Embedding models (KGEs) convert the RDF graphs to low-dimensional vector spaces, these vectors often lack the explainability. On the contrary, in this paper, we introduce a generic, distributed, and scalable software framework that is capable of transforming large RDF data into an explainable feature matrix. This matrix can be exploited in many standard machine learning algorithms. Our approach, by exploiting semantic web and big data technologies, is able to extract a variety of existing features by deep traversing a given large RDF graph. The proposed framework is open-source, well-documented, and fully integrated into the active community project Semantic Analytics Stack (SANSA). The experiments on real-world use cases disclose that the extracted features can be successfully used in machine learning tasks like classification and clustering.
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Saeed, Soobia, Habibullah Bin Haroon, Mehmood Naqvi, Noor Zaman Jhanjhi, Muneer Ahmad, and Loveleen Gaur. "A Systematic Mapping Study of Low-Grade Tumor of Brain Cancer and CSF Fluid Detecting Approaches and Parameters." In Approaches and Applications of Deep Learning in Virtual Medical Care, 236–59. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8929-8.ch010.

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Low-grade tumor or CSF fluid, the symptoms of brain tumor and CSF liquid, usually require image segmentation to evaluate tumor detection in brain images. This research uses systematic literature review (SLR) process for analysis of the different segmentation approach for detecting the low-grade tumor and CSF fluid presence in the brain. This research work investigated how to evaluate and detect the tumor and CSF fluid, improve segmentation method to detect tumor through graph cut hidden markov model of k-mean clustering algorithm (GCHMkC) techniques and parameters, extract the missing values in k-NN algorithm through correlation matrix of hybrid k-NN algorithm with time lag and discrete fourier transformation (DFT) techniques and parameters, and convert the non-linear data into linear transformation using LE-LPP and time complexity techniques and parameters.
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Serafini, Luciano, Artur d’Avila Garcez, Samy Badreddine, Ivan Donadello, Michael Spranger, and Federico Bianchi. "Chapter 17. Logic Tensor Networks: Theory and Applications." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210498.

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The recent availability of large-scale data combining multiple data modalities has opened various research and commercial opportunities in Artificial Intelligence (AI). Machine Learning (ML) has achieved important results in this area mostly by adopting a sub-symbolic distributed representation. It is generally accepted now that such purely sub-symbolic approaches can be data inefficient and struggle at extrapolation and reasoning. By contrast, symbolic AI is based on rich, high-level representations ideally based on human-readable symbols. Despite being more explainable and having success at reasoning, symbolic AI usually struggles when faced with incomplete knowledge or inaccurate, large data sets and combinatorial knowledge. Neurosymbolic AI attempts to benefit from the strengths of both approaches combining reasoning with complex representation of knowledge and efficient learning from multiple data modalities. Hence, neurosymbolic AI seeks to ground rich knowledge into efficient sub-symbolic representations and to explain sub-symbolic representations and deep learning by offering high-level symbolic descriptions for such learning systems. Logic Tensor Networks (LTN) are a neurosymbolic AI system for querying, learning and reasoning with rich data and abstract knowledge. LTN introduces Real Logic, a fully differentiable first-order language with concrete semantics such that every symbolic expression has an interpretation that is grounded onto real numbers in the domain. In particular, LTN converts Real Logic formulas into computational graphs that enable gradient-based optimization. This chapter presents the LTN framework and illustrates its use on knowledge completion tasks to ground the relational predicates (symbols) into a concrete interpretation (vectors and tensors). It then investigates the use of LTN on semi-supervised learning, learning of embeddings and reasoning. LTN has been applied recently to many important AI tasks, including semantic image interpretation, ontology learning and reasoning, and reinforcement learning, which use LTN for supervised classification, data clustering, semi-supervised learning, embedding learning, reasoning and query answering. The chapter presents some of the main recent applications of LTN before analyzing results in the context of related work and discussing the next steps for neurosymbolic AI and LTN-based AI models.
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Conference papers on the topic "Deep graph clustering"

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Wang, Chun, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, and Chengqi Zhang. "Attributed Graph Clustering: A Deep Attentional Embedding Approach." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/509.

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Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.
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Shaheen, Amal, Nabil Hewahi, and Riadh Ksantini. "Graph Deep Clustering using Cluster Graph Conventional." In 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). IEEE, 2022. http://dx.doi.org/10.1109/3ict56508.2022.9990743.

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Fatemi, Bahareh, Soheila Molaei, Hadi Zare, and Hadi Veisi. "Attributed Graph Clustering via Deep Adaptive Graph Maximization." In 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE, 2020. http://dx.doi.org/10.1109/iccke50421.2020.9303694.

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Lin, Zhiping, and Zhao Kang. "Graph Filter-based Multi-view Attributed Graph Clustering." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/375.

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Graph clustering has become an important research topic due to the proliferation of graph data. However, existing methods suffer from two major drawbacks. On the one hand, most methods can not simultaneously exploit attribute and graph structure information. On the other hand, most methods are incapable of handling multi-view data which contain sets of different features and graphs. In this paper, we propose a novel Multi-view Attributed Graph Clustering (MvAGC) method, which is simple yet effective. Firstly, a graph filter is applied to features to obtain a smooth representation without the need of learning the parameters of neural networks. Secondly, a novel strategy is designed to select a few anchor points, so as to reduce the computation complexity. Thirdly, a new regularizer is developed to explore high-order neighborhood information. Our extensive experiments indicate that our method works surprisingly well with respect to state-of-the-art deep neural network methods. The source code is available at https://github.com/sckangz/MvAGC.
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Hu, Pengwei, Keith C. C. Chan, and Tiantian He. "Deep Graph Clustering in Social Network." In the 26th International Conference. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3041021.3051158.

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Luo, Dongsheng, Jingchao Ni, Suhang Wang, Yuchen Bian, Xiong Yu, and Xiang Zhang. "Deep Multi-Graph Clustering via Attentive Cross-Graph Association." In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3336191.3371806.

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Tao, Zhiqiang, Hongfu Liu, Jun Li, Zhaowen Wang, and Yun Fu. "Adversarial Graph Embedding for Ensemble Clustering." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/494.

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Ensemble clustering generally integrates basic partitions into a consensus one through a graph partitioning method, which, however, has two limitations: 1) it neglects to reuse original features; 2) obtaining consensus partition with learnable graph representations is still under-explored. In this paper, we propose a novel Adversarial Graph Auto-Encoders (AGAE) model to incorporate ensemble clustering into a deep graph embedding process. Specifically, graph convolutional network is adopted as probabilistic encoder to jointly integrate the information from feature content and consensus graph, and a simple inner product layer is used as decoder to reconstruct graph with the encoded latent variables (i.e., embedding representations). Moreover, we develop an adversarial regularizer to guide the network training with an adaptive partition-dependent prior. Experiments on eight real-world datasets are presented to show the effectiveness of AGAE over several state-of-the-art deep embedding and ensemble clustering methods.
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Zhang, Xianchao, Jie Mu, Han Liu, and Xiaotong Zhang. "Graphnet: Graph Clustering with Deep Neural Networks." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413809.

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Chen, Zitai, Chuan Chen, Zong Zhang, Zibin Zheng, and Qingsong Zou. "Variational Graph Embedding and Clustering with Laplacian Eigenmaps." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/297.

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As a fundamental machine learning problem, graph clustering has facilitated various real-world applications, and tremendous efforts had been devoted to it in the past few decades. However, most of the existing methods like spectral clustering suffer from the sparsity, scalability, robustness and handling high dimensional raw information in clustering. To address this issue, we propose a deep probabilistic model, called Variational Graph Embedding and Clustering with Laplacian Eigenmaps (VGECLE), which learns node embeddings and assigns node clusters simultaneously. It represents each node as a Gaussian distribution to disentangle the true embedding position and the uncertainty from the graph. With a Mixture of Gaussian (MoG) prior, VGECLE is capable of learning an interpretable clustering by the variational inference and generative process. In order to learn the pairwise relationships better, we propose a Teacher-Student mechanism encouraging node to learn a better Gaussian from its instant neighbors in the stochastic gradient descent (SGD) training fashion. By optimizing the graph embedding and the graph clustering problem as a whole, our model can fully take the advantages in their correlation. To our best knowledge, we are the first to tackle graph clustering in a deep probabilistic viewpoint. We perform extensive experiments on both synthetic and real-world networks to corroborate the effectiveness and efficiency of the proposed framework.
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Wang, Zhengyi, Zhongkai Hao, Ziqiao Wang, Hang Su, and Jun Zhu. "Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/108.

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While deep neural networks have achieved great success in graph analysis, recent work has shown that they are vulnerable to adversarial attacks. Compared with adversarial attacks on image classification, performing adversarial attacks on graphs is more challenging because of the discrete and non-differential nature of the adjacent matrix for a graph. In this work, we propose Cluster Attack --- a Graph Injection Attack (GIA) on node classification, which injects fake nodes into the original graph to degenerate the performance of graph neural networks (GNNs) on certain victim nodes while affecting the other nodes as little as possible. We demonstrate that a GIA problem can be equivalently formulated as a graph clustering problem; thus, the discrete optimization problem of the adjacency matrix can be solved in the context of graph clustering. In particular, we propose to measure the similarity between victim nodes by a metric of Adversarial Vulnerability, which is related to how the victim nodes will be affected by the injected fake node, and to cluster the victim nodes accordingly. Our attack is performed in a practical and unnoticeable query-based black-box manner with only a few nodes on the graphs that can be accessed. Theoretical analysis and extensive experiments demonstrate the effectiveness of our method by fooling the node classifiers with only a small number of queries.
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