Network Structural Transformation-Based Community Detection with Autoencoder

Geng, Xiurui; Lu, Hanqing; Sun, Jun

doi:10.3390/sym12060944

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Then, a sparse autoencoder was performed to find an effective and low-dimensional feature space of CNs. Continuing in the same direction, a CD method based on a denoising autoencoder was developed by Geng et al in [65]. A probability transfer matrix T of a CN was first computed.…”

Section: A Stacked Autoencoder-based Community Detectionmentioning

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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Section: A Stacked Autoencoder-based Community Detectionmentioning

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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“…Game Theory [31] models the process of community formation as a game by representing each vertex with a playing actor, which makes predictions about behaviors of the actor in an interdependent scenario. Network Structure Transformation [32] engages a denoising autoencoder to nonlinearly map the probability transfer matrix, which is calculated from the network adjacency matrix, into a new subspace. Network vertices are then clustered via k-means clustering to obtain communities.…”

Section: Deep Learning Transformationmentioning

confidence: 99%

An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Huang

Jiang

et al. 2021

Symmetry

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Overlapping clustering is a fundamental and widely studied subject that identifies all densely connected groups of vertices and separates them from other vertices in complex networks. However, most conventional algorithms extract modules directly from the whole large-scale graph using various heuristics, resulting in either high time consumption or low accuracy. To address this issue, we develop an overlapping community detection approach in Ego-Splitting networks using symmetric Nonnegative Matrix Factorization (ESNMF). It primarily divides the whole network into many sub-graphs under the premise of preserving the clustering property, then extracts the well-connected sub-sub-graph round each community seed as prior information to supplement symmetric adjacent matrix, and finally identifies precise communities via nonnegative matrix factorization in each sub-network. Experiments on both synthetic and real-world networks of publicly available datasets demonstrate that the proposed approach outperforms the state-of-the-art methods for community detection in large-scale networks.

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“…According to Equation ( 7), α = 1 is a special value. According to Equation (14), is in the range of (0,1). Thus, we select α in the range of [0.8, 1.5] and in the range of [0.1, 0.9], and the step is 0.1.…”

Section: Parameter Selectionmentioning

confidence: 99%

“…The connections between the nodes in the community are very close, while the connections between the communities are relatively sparse [13]. The purpose of community discovery (or community detection [14]) is to mine community structures in a complex network. Community discovery can reveal the universal features of a complex network and help in understanding its topology accurately, which provides guidance for the use and transformation of the network and promotes the practical application of the network.…”

Section: Introductionmentioning

confidence: 99%

Overlapping Community Discovery Method Based on Two Expansions of Seeds

et al. 2020

Symmetry

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The real world can be characterized as a complex network sto in symmetric matrix. Community discovery (or community detection) can effectively reveal the common features of network groups. The communities are overlapping since, in fact, one thing often belongs to multiple categories. Hence, overlapping community discovery has become a new research hotspot. Since the results of the existing community discovery algorithms are not robust enough, this paper proposes an effective algorithm, named Two Expansions of Seeds (TES). TES adopts the topological feature of network nodes to find the local maximum nodes as the seeds which are based on the gravitational degree, which makes the community discovery robust. Then, the seeds are expanded by the greedy strategy based on the fitness function, and the community cleaning strategy is employed to avoid the nodes with negative fitness so as to improve the accuracy of community discovery. After that, the gravitational degree is used to expand the communities for the second time. Thus, all nodes in the network belong to at least one community. Finally, we calculate the distance between the communities and merge similar communities to obtain a less- undant community structure. Experimental results demonstrate that our algorithm outperforms other state-of-the-art algorithms.

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Network Structural Transformation-Based Community Detection with Autoencoder

Cited by 7 publications

References 20 publications

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

An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Overlapping Community Discovery Method Based on Two Expansions of Seeds

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