Stacked autoencoder-based community detection method via an ensemble clustering framework

Xu, Rongbin; Yan, Chen; Wang, Xinmei; Hu, Jianxiong; Xie, Ying

doi:10.1016/j.ins.2020.03.090

Cited by 41 publications

(9 citation statements)

References 32 publications

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“…Community Detection Method via Ensemble Clustering (CDMEC) [103] combines sparse AEs with a transfer learning model to discover more valuable information from local network structures. To this end, CDMEC constructs four similarity matrices and employs transfer learning to share local information via AEs' parameters.…”

Section: B Sparse Ae-based Community Detectionmentioning

confidence: 99%

A Comprehensive Survey on Community Detection With Deep Learning

Xue

Liu

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

213

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A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in network analysis. Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages in handling high dimensional network data. Hence, a comprehensive overview of community detection's latest progress through deep learning is timely to both academics and practitioners. This survey devises and proposes a new taxonomy covering different categories of the state-of-theart methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep sparse filtering. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders. The survey also summarizes the popular benchmark data sets, model evaluation metrics, and open-source implementations to address experimentation settings. We then discuss the practical applications of community detection in various domains and point to implementation scenarios. Finally, we outline future directions by suggesting challenging topics in this fast-growing deep learning field.

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Section: B Sparse Ae-based Community Detectionmentioning

confidence: 99%

A Comprehensive Survey on Community Detection With Deep Learning

Xue

Liu

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

213

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“…The model was trained to obtain a low dimensional representation of a CN, then the communities is discovered with the k-mean clustering algorithm. More recently, Xu et al [71] proposed a new method for CD using DL techniques. The method uses four similarity matrices of CNs based on modularity, diagonal, transition operations, called B, D, T, M. All these matrices are fed to the model as source and target.…”

Section: A Stacked Autoencoder-based Community Detectionmentioning

confidence: 99%

“…[ The performance of the aforementioned methods [69][70][71][72] was evaluated on small-sized CNs, and they achieved good results in terms of accuracy and effectiveness. However, these methods suffer from efficiency issues and dealing with small CNs.…”

Section: Table II Summary Of Dl-based Cd Studies (Autoencoders)mentioning

confidence: 99%

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

et al. 2021

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Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. Meta-Heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, Parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature; however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions. INDEX TERMSCommunity detection; Deep learning; Complex networks; Meta-heuristic algorithms; Parallel computing.

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“…Xu et al [34] presented the Community Detection Method via Ensemble Clustering (CDMEC). This framework combines transfer learning and a stacked auto-encoder to produce a feature representation of complex networks in low dimension.…”

Section: Ae-based Community Detection Strategiesmentioning

confidence: 99%

A Survey on the Recent Advances of Deep Community Detection

2021

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In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteristics that were likely to interfere with the existing members. Today, things have changed dramatically. Social networking platforms are not restricted to forming similar user profiles: The vast amounts of data produced every day have given opportunities to predict and suggest relationships, behaviors, and everyday activities like shopping, food, traveling destinations, etc. Every day, vast data amounts are generated by the famous social networks such as Facebook, Twitter, Instagram, and so on. For example, Facebook alone generates 4 petabytes of data per day. The analysis of such data is of high importance to many aspects like recommendation systems, businesses, health organizations, etc. The community detection problem received considerable attention a long time ago. Communities are represented as clusters of an entire network. Most of the community detection techniques are based on graph structures. In this paper, we present the recent advances of deep learning techniques for community detection. We describe the most recent strategies presented in this field, and we provide some general discussion and some future trends.

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Stacked autoencoder-based community detection method via an ensemble clustering framework

Cited by 41 publications

References 32 publications

A Comprehensive Survey on Community Detection With Deep Learning

A Comprehensive Survey on Community Detection With Deep Learning

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

A Survey on the Recent Advances of Deep Community Detection

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