A Four-Stage Algorithm for Community Detection Based on Label Propagation and Game Theory in Social Networks

Abstract

Over the years, detecting stable communities in a complex network has been a major challenge in network science. The global and local structures help to detect communities from different perspectives. However, previous methods based on them suffer from high complexity and fall into local optimum, respectively. The Four-Stage Algorithm (FSA) is proposed to reduce these issues and to allocate nodes to stable communities. Balancing global and local information, as well as accuracy and time complexity, while ensuring the allocation of nodes to stable communities, are the fundamental goals of this research. The Four-Stage Algorithm (FSA) is described and demonstrated using four real-world data with ground truth and three real networks without ground truth. In addition, it is evaluated with the results of seven community detection methods: Three-stage algorithm (TS), Louvain, Infomap, Fastgreedy, Walktrap, Eigenvector, and Label propagation (LPA). Experimental results on seven real network data sets show the effectiveness of our proposed approach and confirm that it is sufficiently capable of identifying those communities that are more desirable. The experimental results confirm that the proposed method can detect more stable and assured communities. For future work, deep learning methods can also be used to extract semantic content features that are more beneficial to investigating networks.

A Hybrid Approach to Detect Researchers’ Communities Based on Deep Learning and Game Theory

“…In this regard, Torkaman et al [44] proposed a Four-Stage Algorithm (FSA), which find the important central nodes, propagate labels, and identify initial communities to solve this problem. However, this method focuses only on structural information and omits content information.…”

“…Figure 1 shows our suggested framework. First, according to our previous work [44], we extract informative features by using three popular deep learning approaches.…”

“…The architecture of the proposed representation is shown in Figure 2. It projects each node into a low dimensional region to capture the network's structural and content properties [44].…”

“…Then, to extract the content features, we used the concatenated vectors of both Long Short-Term Memory (LSTM) [9] and Doc2vec [10] algorithms, to enhance each other to extract the context sequence from the paper's titles or abstracts more accurately. Therefore, in this paper, we applied the same loss function used by Torkaman et al [44] to extract the structural and content properties of the nodes:…”

“…We have used the same parameter settings that are reported by Torkaman et al [44] for deep learning part. We set window size b=8, The embedding size is set to k =300 (100 for each proposed deep learning methods), the learning rate= 0.001 and Adam [58] as the optimizer.…”