Community Detection Based on DeepWalk Model in Large-Scale Networks

Abstract

The large-scale and complex structure of real networks brings enormous challenges to traditional community detection methods. In order to detect community structure in large-scale networks more accurately and efficiently, we propose a community detection algorithm based on the network embedding representation method. Firstly, in order to solve the scarce problem of network data, this paper uses the DeepWalk model to embed a high-dimensional network into low-dimensional space with topology information. Then, low-dimensional data are processed, with each node treated as a sample and each dimension of the node as a feature. Finally, samples are fed into a Gaussian mixture model (GMM), and in order to automatically learn the number of communities, variational inference is introduced into GMM. Experimental results on the DBLP dataset show that the model method of this paper can more effectively discover the communities in large-scale networks. By further analyzing the excavated community structure, the organizational characteristics within the community are better revealed.

General Community Detection in Attributed Networks with Consistent-Module Constrained Nonnegative Matrix Factorization

“…Jin et al utilized both the community structure matrix and the node attribute matrix in NMF framework SCI [29]. Chen et al [27] combined node attribute information and community structure information in the NMF framework to accurately find the relationships between networks. Some recent research work focus on building NMF model to learn low-dimensional representation of nodes for discovering communities in attributed networks [33,37].…”

“…In the past decades, several methods have been proposed to detect communities in attributed networks. They are mainly classified into modularity based methods [14,15], clustering based methods [16][17][18][19][20], random walk based methods [21,22], statistical inference models [13,23,24], and matrix factorization based methods [3,[25][26][27]. Among them, nonnegative matrix factorization (NMF) based methods have attracted much interest due to their good performance and strong interpretability.…”

“…To introduce node attributes, Jin et al [29] used the node attribute matrix to construct a NMF framework for underlying community membership. Chen et al [27] proposed CDCN by combining node attribute information and community structure information using the NMF framework to identify communities with semantic annotation. However, above attributed methods commonly assumed that the community structure obtained from the link structure is consistent with the community structure obtained from node attribute mining.…”

Classifying Urban Functional Zones Based on Modeling POIs by Deepwalk

“…Deepwalk is a representative network structure analysis model that generates node sequences through random walks, and then inputs the sequences into the Word2vec to learn node embedding [31,32]. The model is proven effective in extracting node homogeneity and structural similarity, and is widely used in POI recommendation [30], community detection [33,34], and other areas [35,36]. In the proposed method, POI data is used to represent geographical elements; POIs within a zone are organized on a graph based on the spatial distances among POIs; then, the Deepwalk model is used to study the spatial relationship of POIs for urban functional classification.…”