Modularized convex nonnegative matrix factorization for community detection in signed and unsigned networks

Yan, Chao; Chang, Zhenhai

doi:10.1016/j.physa.2019.122904

Cited by 13 publications

(8 citation statements)

References 59 publications

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“…Representative methods Topology networks NMTF [21], BNMTF [22], PCSNMF [23], PSSNMF [24], HPNMF [25], HNMF [26], A 2 NMF [27], PNMF [28] Signed networks JNMF [31], SGNMF [32], MCNMF [33], ReS-NMF [36], BRSNMF [37], SPOCD [38] Attributed networks FSL [40], JWNMF [41], NMTFR [42], CFOND [43], SCI [44], ASCD [45], DII [46], RSECD [47] Multi-layer networks WSSNMTF [50], NF-CCE [51], MTRD [53], LJ-SNMF [54], S2-jNMF [55] Dynamic networks sE-NMF [57], GrENMF [58], Cr-ENMF [59], ECGNMF [60], DGR-SNMF [61], DBNMF [62], C 3 [66], Chimera [70] Large-scale networks BIGCLAM [73], HierSymNMF2 [75], cyclicCDSymNMF [77], OGNMF [79], DRNMFSR [80], TCB [81] are often utilized. One is prior knowledge, also known as semi-supervised information, such as ground-truth community labels, node must-link and cannot-link constraints.…”

Section: Categorymentioning

confidence: 99%

“…where B is the weight matrix containing priorities of links to community structures, S 1 and S 2 both denote community interaction matrices. Similar to JNMF, methods SGNMF [32] and MCNMF [33] both jointly factor A + and A − to obtain the consensus H. However, they still have a little difference that they introduce graph regularized items into the joint NMF model. This enables them to integrate more information to improve the performance.…”

Section: B Signed Networkmentioning

confidence: 99%

“…Attributed networks FSL [32] O((mn + n 2 )k) m is the length of the attribute set JWNMF [33] O((n 2 + m 2 + mn)k) NMTFR [34] O((nw…”

Section: F Large-scale Networkmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Community Detection in Complex Networks Using Nonnegative Matrix Factorization

Fei

Cheng

et al. 2022

IEEE Trans. Comput. Soc. Syst.

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Section: Categorymentioning

confidence: 99%

Section: B Signed Networkmentioning

confidence: 99%

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A Survey of Community Detection in Complex Networks Using Nonnegative Matrix Factorization

Fei

Cheng

et al. 2022

IEEE Trans. Comput. Soc. Syst.

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“…And the evolving mechanism of nodes is updated by its neighbors' information which leads to form optimal community structure. Yan et al [70] proposed a new modularized convex nonnegative matrix factorization (NMF) model, which combined signed modularized information with convex NMF model to improve the accuracy of community detection in signed and unsigned networks. As for model selection, Yan et al extended the modularity density to signed networks and employed the signed modularity density to determine the number of communities automatically.…”

Section: Related Workmentioning

confidence: 99%

A Memetic Algorithm for Community Detection in Signed Networks

2020

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Community discovery (i.e. community detection) in signed networks is a division of nodes, such that the edges in the communities are positive and the edges between the communities are negative. Davis and Harary have solved the problem of community detection when a signed graph is balanced or weakly balanced. When the signed network is unbalanced, community detection becomes very complex. In this paper, we propose a novel memetic algorithm (MA) called MACD-SN for community partition (i.e. community detection) in signed networks. Firstly, we present a novel initialization algorithm used in initialization of MACD-SN. This method can accelerate the convergence rate of MACD-SN algorithm. Next, in addition to using frequently-used variation operation (in this paper, variation and mutation are interchangeable), this paper presents a novel crossover operation and a novel variation operation, which contributes to increasing the correctness of the MACD-SN algorithm's operation result and reduces its running time. Lastly, this paper proposes a new local search algorithm, which may enable the algorithm's result to jump away the local best result with a certain probability and draw near the global best result quickly. For testing the performance of MACD-SN algorithm, we have done many experiments using five kinds of synthetic signed networks and five real-world signed networks. The test outcomes show that the proposed algorithm is valid and efficient for signed network cluster partition (i.e. community detection).

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“…Additionally, the algorithm was based on deep learning was proposed [30]. As a valid method in unsupervised learning, Nonnegative Matrix Factorization (NMF) has also been gradually applied to analyze community structure [31,32]. Although the algorithm that is based on NMF has good interpretability, it usually needs the prior knowledge of the number of communities in the network, but the number of communities is generally unknown.…”

Section: Introductionmentioning

confidence: 99%

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

Zhang

Zhao

et al. 2021

Entropy

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Community detection is of great significance in understanding the structure of the network. Label propagation algorithm (LPA) is a classical and effective method, but it has the problems of randomness and instability. An improved label propagation algorithm named LPA-MNI is proposed in this study by combining the modularity function and node importance with the original LPA. LPA-MNI first identify the initial communities according to the value of modularity. Subsequently, the label propagation is used to cluster the remaining nodes that have not been assigned to initial communities. Meanwhile, node importance is used to improve the node order of label updating and the mechanism of label selecting when multiple labels are contained by the maximum number of nodes. Extensive experiments are performed on twelve real-world networks and eight groups of synthetic networks, and the results show that LPA-MNI has better accuracy, higher modularity, and more reasonable community numbers when compared with other six algorithms. In addition, LPA-MNI is shown to be more robust than the traditional LPA algorithm.

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Modularized convex nonnegative matrix factorization for community detection in signed and unsigned networks

Cited by 13 publications

References 59 publications

A Survey of Community Detection in Complex Networks Using Nonnegative Matrix Factorization

A Survey of Community Detection in Complex Networks Using Nonnegative Matrix Factorization

A Memetic Algorithm for Community Detection in Signed Networks

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

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