Community Detection in Social Network with Pairwisely Constrained Symmetric Non-Negative Matrix Factorization

Shi, Xiaohua; Lu, Hongtao; He, Yangchen; He, Shan

doi:10.1145/2808797.2809383

Cited by 47 publications

(22 citation statements)

References 22 publications

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“…The new space is soft membership vectors that assign each node to a particular cluster. Shi et al [47] developed a new NMF-based pairwise constrained method to improve the performance of CD. Wang et al [48] also adapted the NMF algorithm to detect overlapping and non-overlapping communities in CNs.…”

Section: A Earlier Conventional Community Detection Methodsmentioning

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 Earlier Conventional Community Detection Methodsmentioning

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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“…Whang et al [26] formulated the problem of Non-Exhaustive, Overlapping Co-Clustering framework for community detection. Shi et al [27] proposed a novel pairwisely constrained nonnegative symmetric matrix factorization (PCSNMF) method, which not only consider symmetric community structures of undirected network, but also takes into consideration the pairwise constraints generated from some ground-truth group information to enhance the community detection. The main limitation of these approaches is that the number of partitions in the network should be decided in advance to obtain a better result.…”

Section: B Global Methods and Local Community Detectionmentioning

confidence: 99%

Target Community Detection With User’s Preference and Attribute Subspace

Liu

Chang

et al. 2019

IEEE Access

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Community detection in the network has become an invaluable tool to explore and reveal the internal organization of nodes. In particular, the target community detection focuses on discovering the ''local'' links within and connecting to the target community related to user's preference, which refers to a limited number of nodes in the whole network. A few works in the literature discuss the target community detection. In this paper, we propose a target community detection with user's preference and attribute subspace. Our method utilizes not only network structure but also node attributes within a certain subspace to quantify both internal consistency and external separability, which is able to capture a user preferred target community. First, the similarity between nodes is calculated via both attributes and structures, and the center node set of the target community can be obtained by extending the sample node given by the user with its neighbors. Second, an attribute subspace calculation method with entropy weights is established based on the center node set, and the attribute subspace of the target community can thus be deduced. Finally, the target community quality, which is the combination of internal connectivity and external separability, is defined, based on which the target community with a user's preference can be detected. The experimental results on both synthetic network and real-world network datasets demonstrated the efficiency and effectiveness of the proposed algorithm.

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“…The most widely-used approach has been to employ pairwise constraints, either must-link or cannot-link, which indicate that either two nodes must be in the same community or must be in different communities. This strategy has been implemented via several algorithms, including modularity-based methods (Li et al 2014), spectral partitioning methods (Habashi et al 2016;Zhang 2013;Zhang et al 2013), a spin-glass model (Eaton and Mansbach 2012), and matrix factorization methods (Shi et al 2015;Zhang 2013). Such approaches have often provided significantly better results on benchmark data, when compared to standard unsupervised algorithms.…”

Section: Semi-supervised Learning In Community Findingmentioning

confidence: 99%

Active semi-supervised overlapping community finding with pairwise constraints

Alghamdi

Greene

2019

Appl Netw Sci

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Algorithms for finding communities in complex networks are generally unsupervised, relying solely on the structure of the network. However, these methods can often fail to uncover meaningful groupings that reflect the underlying communities in the data, particularly when they are highly overlapping. One way to improve these algorithms is by incorporating human expertise or background knowledge in the form of pairwise constraints to direct the community detection process. In this work, we explore the potential of semi-supervised strategies to improve algorithms for finding overlapping communities in networks. We propose a method, based on label propagation, for finding communities using pairwise constraints. Furthermore, we introduce a new strategy, inspired by active learning, for intelligent constraint selection, which is designed to minimize the level of human annotation required. Extensive evaluations on synthetic and real-world datasets demonstrate the potential of this strategy for effectively uncovering meaningful overlapping community structures, using a limited amount of supervision.

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Community Detection in Social Network with Pairwisely Constrained Symmetric Non-Negative Matrix Factorization

Cited by 47 publications

References 22 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

Target Community Detection With User’s Preference and Attribute Subspace

Active semi-supervised overlapping community finding with pairwise constraints

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