A survey of community detection methods in multilayer networks

Huang, X.; Chen, Dongming; Ren, Tao; Wang, Dongqi

doi:10.1007/s10618-020-00716-6

Cited by 121 publications

(41 citation statements)

References 159 publications

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“…Gao et al [21] wrote a paper; titled "an adaptive optimal-Kernel time-frequency representation-based complex network method for characterizing fatigued behavior using the SSVEP-based BCI system." Huang et al [22] carried out a survey on techniques of community detection in multilayer networks. Rostami et al [23] presented a genetic algorithm for feature selection that is based on a novel community detection, Li et al [24] proposed the convex relaxation techniques for community detection, and Joo et al [25] utilized the community detection for studying the stream gauge network grouping.…”

Section: Introductionmentioning

confidence: 99%

An Effective Approach for Modular Community Detection in Bipartite Network Based on Integrating Rider with Harris Hawks Optimization Algorithms

Alkhamees

Mosleh

AlSalman

et al. 2021

Journal of Mathematics

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The strenuous mining and arduous discovery of the concealed community structure in complex networks has received tremendous attention by the research community and is a trending domain in the multifaceted network as it not only reveals details about the hierarchical structure of multifaceted network but also assists in better understanding of the core functions of the network and subsequently information recommendation. The bipartite networks belong to the multifaceted network whose nodes can be divided into a dissimilar node-set so that no edges assist between the vertices. Even though the discovery of communities in one-mode network is briefly studied, community detection in bipartite networks is not studied. In this paper, we propose a novel Rider-Harris Hawks Optimization (RHHO) algorithm for community detection in a bipartite network through node similarity. The proposed RHHO is developed by the integration of the Rider Optimization (RO) algorithm with the Harris Hawks Optimization (HHO) algorithm. Moreover, a new evaluation metric, i.e., h-Tversky Index (h-TI), is also proposed for computing node similarity and fitness is newly devised considering modularity. The goal of modularity is to quantify the goodness of a specific division of network to evaluate the accuracy of the proposed community detection. The quantitative assessment of the proposed approach, as well as thorough comparative evaluation, was meticulously conducted in terms of fitness and modularity over the citation networks datasets (cit-HepPh and cit-HepTh) and bipartite network datasets (Movie Lens 100 K and American Revolution datasets). The performance was analyzed for 250 iterations of the simulation experiments. Experimental results have shown that the proposed method demonstrated a maximal fitness of 0.74353 and maximal modularity of 0.77433, outperforming the state-of-the-art approaches, including h-index-based link prediction, such as Multiagent Genetic Algorithm (MAGA), Genetic Algorithm (GA), Memetic Algorithm for Community Detection in Bipartite Networks (MATMCD-BN), and HHO.

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Section: Introductionmentioning

confidence: 99%

An Effective Approach for Modular Community Detection in Bipartite Network Based on Integrating Rider with Harris Hawks Optimization Algorithms

Alkhamees

Mosleh

AlSalman

et al. 2021

Journal of Mathematics

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“…Using GCN as the attribute decoder, after the encoder obtains Z, connect a layer of GCN, output the reconstructed attribute matrix Y, and predict the attribute information of each node in the original network. The specific calculation is shown in Equation (10).…”

Section: Attribute Reconstruction Decodermentioning

confidence: 99%

“…Performing data analysis and data mining on these attribute networks, using network information for prediction and decision making, and discovering useful information latent in them, have strong academic and commercial value. Common network analysis tasks include node classification [6], node clustering [7], community detection [8][9][10], link prediction [11,12], and anomaly detection [13]. The networks are growing in size in the big data era, some reaching hundreds of millions of nodes, with high and sparse dimensions that not only have complex structures but are also rich in information.…”

Section: Introductionmentioning

confidence: 99%

Network Embedding Algorithm Taking in Variational Graph AutoEncoder

et al. 2022

Self Cite

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Complex networks with node attribute information are employed to represent complex relationships between objects. Research of attributed network embedding fuses the topology and the node attribute information of the attributed network in the common latent representation space, to encode the high-dimensional sparse network information to the low-dimensional dense vector representation, effectively improving the performance of the network analysis tasks. The current research on attributed network embedding is presently facing problems of high-dimensional sparsity of attribute eigenmatrix and underutilization of attribute information. In this paper, we propose a network embedding algorithm taking in a variational graph autoencoder (NEAT-VGA). This algorithm first pre-processes the attribute features, i.e., the attribute feature learning of the network nodes. Then, the feature learning matrix and the adjacency matrix of the network are fed into the variational graph autoencoder algorithm to obtain the Gaussian distribution of the potential vectors, which more easily generate high-quality node embedding representation vectors. Then, the embedding of the nodes obtained by sampling this Gaussian distribution is reconstructed with structural and attribute losses. The loss function is minimized by iterative training until the low-dimension vector representation, containing network structure information and attribute information of nodes, can be better obtained, and the performance of the algorithm is evaluated by link prediction experimental results.

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“…For community detection in multilayer graphs, the methods can be categorized into three types. [11] The first is flattening methods, which collapses the information in a multilayer graph into a single layer. These methods are commonly used in the multiplex networks, where inter-layer edges only appears between same nodes.…”

Section: Community Detectionmentioning

confidence: 99%

Data-driven Clustering in Ad-hoc Networks based on Community Detection

Huang

Gao³

2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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High demands for industrial networks lead to increasingly large sensor networks. However, the complexity of networks and demands for accurate data require better stability and communication quality. Conventional clustering methods for ad-hoc networks are based on topology and connectivity, leading to unstable clustering results and low communication quality. In this paper, we focus on two situations: time-evolving networks, and multi-channel ad-hoc networks. We model ad-hoc networks as graphs and introduce community detection methods to both situations. Particularly, in time-evolving networks, our method utilizes the results of community detection to ensure stability. By using similarity or human-inthe-loop measures, we construct a new weighted graph for final clustering. In multi-channel networks, we perform allocations from the results of multiplex community detection. Experiments on realworld datasets show that our method outperforms baselines in both stability and quality. CCS CONCEPTS• Computer systems organization → Sensor networks; • Humancentered computing → Ubiquitous and mobile computing.

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A survey of community detection methods in multilayer networks

Cited by 121 publications

References 159 publications

An Effective Approach for Modular Community Detection in Bipartite Network Based on Integrating Rider with Harris Hawks Optimization Algorithms

An Effective Approach for Modular Community Detection in Bipartite Network Based on Integrating Rider with Harris Hawks Optimization Algorithms

Network Embedding Algorithm Taking in Variational Graph AutoEncoder

Data-driven Clustering in Ad-hoc Networks based on Community Detection

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