Community Detection in Fully-Connected Multi-layer Networks Through Joint Nonnegative Matrix Factorization

Al-sharoa, Esraa; Aviyente, Selin

doi:10.1109/access.2022.3168659

Cited by 6 publications

(7 citation statements)

References 70 publications

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“…5. HPNMF [37]: HPNMF is based on a graph regularization NMF model, which can integrate network topology and node homogeneity information for community discovery. 6.…”

Section: Comparison Methodsmentioning

confidence: 99%

Graph Convolutional Networks based Non-Negative Matrix Factorization aware Community detection

Scholar

Professor

2023

Preprint

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Nonnegative matrix factorization (NMF) is widely used in community discovery because of its effectiveness and easy interpretability. However, most of the existing NMF-based community detection methods are linear. They cannot effectively deal with the nonlinear characteristics of complex networks, resulting in further improvement in community detection performance. Aiming at this problem, a convolution graph network (GCN) enhanced nonlinear NMF community discovery method NMFGCN is proposed. NMFGCN consists of two main modules: GCN and NMF, where GCN is used to learn network node representations, and NMF uses node representations as input to obtain network community representations. In addition, a joint optimization method is proposed to train NMFGCN, which enables NMFGCN to have nonlinear feature representation capabilities and enables GCN and NMF to promote each other and obtain better community segmentation results. Many experiments on artificial synthetic networks and entire networks show that NMFGCN is superior to current NMF-based community discovery methods , thus proving that NMFGCN can improve the performance of NMF community discovery methods. In addition, NMFGCN also outperforms Deep Walk and LINE standard graph representation learning methods.

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“…5. HPNMF [37]: HPNMF is based on a graph regularization NMF model, which can integrate network topology and node homogeneity information for community discovery. 6.…”

Section: Comparison Methodsmentioning

confidence: 99%

Graph Convolutional Networks based Non-Negative Matrix Factorization aware Community detection

Scholar

Professor

2023

Preprint

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“…In the case of two-layer networks, for example, a two-layer network with interlayer edges is constructed by introducing a third type of interaction between individuals (e.g., friendship) into the two-layer network as an interlayer edge of the network. In 2020, Contisciani et al 46 proposed a principled probabilistic approach for community detection in multilayer networks by fusing the attributes of nodes and network structure information; in 2022, Al-sharoa et al 3 proposed a joint non-negative matrix decomposition method for the community detection of multilayer networks by dividing the multilayer network design into a combination of multiplexing and dichotomous networks.…”

Section: Related Workmentioning

confidence: 99%

“…In general, a network in which the nodes are homogeneous and the edges are heterogeneous is called a multiplexing network 1 – 4 , for example, in social networks, there are two different social relationships between the same users, friendship and work, which can be abstracted into different layers in networks 5 , 6 . At present, research on multiplexing networks has covered many aspects, such as robustness 7 , 8 , dynamics 9 , 10 , community structure 3 , 11 , 12 , disease transmission 13 , 14 , etc. Further, a network in which both nodes and edges are heterogeneous is called the heterogeneous multilayer network 15 – 17 .…”

Section: Introductionmentioning

confidence: 99%

Motif-based community detection in heterogeneous multilayer networks

Liu,

Li,

Zeng

et al. 2024

Sci Rep

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Multilayer networks composed of intralayer edges and interlayer edges are an important type of complex networks. Considering the heterogeneity of nodes and edges, it is necessary to design more reasonable and diverse community detection methods for multilayer networks. Existing research on community detection in multilayer networks mainly focuses on multiplexing networks (where the nodes are homogeneous and the edges are heterogeneous), but few studies have focused on heterogeneous multilayer networks where both nodes and edges represent different semantics. In this paper, we studied community detection on heterogeneous multilayer networks and proposed a motif-based detection algorithm. First, the communities and motifs of multilayer networks are defined, especially the interlayer motifs. Then, the modularity of multilayer networks based on these motifs is designed, and the community structure of the multilayer network is detected by maximizing the modularity of multilayer networks. Finally, we verify the effectiveness of the detection algorithm on synthetic networks. In the experiments on synthetic networks, comparing with the classical community detection algorithms (without considering interlayer heterogeneity), the motif-based modularity community detection algorithm can obtain better results under different evaluation indexes, and we found that there exists a certain relationship between motifs and communities. In addition, the proposed algorithm is applied in the empirical network, which shows its practicability in the real world. This study provides a solution for the investigation of heterogeneous information in multilayer networks.

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“…More recently, community detection methods that consider fully connected MLNs, MLNs with inter-relations, have been proposed [13]. The authors of [14,15] propose to extend the modularity function and its solution to account for MLNs with inter-layer relations.…”

Section: Related Workmentioning

confidence: 99%

A Unified Spectral Clustering Approach for Detecting Community Structure in Multilayer Networks

Al-sharoa

Aviyente

2023

Symmetry

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Networks offer a compact representation of complex systems such as social, communication, and biological systems. Traditional network models are often inadequate to capture the diverse nature of contemporary networks, which may exhibit temporal variation and multiple types of interactions between entities. Multilayer networks (MLNs) provide a more comprehensive representation by allowing interactions between nodes to be represented by different types of links, each reflecting a distinct type of interaction. Community detection reveals meaningful structure and provides a better understanding of the overall functioning of networks. Current approaches to multilayer community detection are either limited to community detection over the aggregated network or are extensions of single-layer community detection methods with simplifying assumptions such as a common community structure across layers. Moreover, most of the existing methods are limited to multiplex networks with no inter-layer edges. In this paper, we introduce a spectral-clustering-based community detection method for two-layer MLNs. The problem of detecting the community structure is formulated as an optimization problem where the normalized cut for each layer is minimized simultaneously with the normalized cut for the bipartite network along with regularization terms that ensure the consistency of the within- and across-layer community structures. The proposed method is evaluated on both synthetic and real networks and compared to state-of-the-art methods. MLNs. The problem of detecting the community structure is formulated as an optimization problem where the normalized cut for each layer is minimized simultaneously with the normalized cut for the bipartite network along with regularization terms that ensure the consistency of the intra- and inter-layer community structures. The proposed method is evaluated on both synthetic and real networks and compared to state-of-the-art methods.

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Community Detection in Fully-Connected Multi-layer Networks Through Joint Nonnegative Matrix Factorization

Cited by 6 publications

References 70 publications

Graph Convolutional Networks based Non-Negative Matrix Factorization aware Community detection

Graph Convolutional Networks based Non-Negative Matrix Factorization aware Community detection

Motif-based community detection in heterogeneous multilayer networks

A Unified Spectral Clustering Approach for Detecting Community Structure in Multilayer Networks

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