# Dynamic Community Detection Based on Evolutionary DeepWalk
###### Abstract  
To fully characterize the evolution process of the topological structure of dynamic communities, we propose a dynamic community detection based on Evolutionary DeepWalk (DEDW) for the high-dimensional data and dynamic characteristics. First, DEDW solves the problem of data sparseness in the process of dynamic network data representation through graph embedding. Then, DEDW uses the DeepWalk algorithm to generate node embedding feature vectors based on the characteristics of the stable change of the community structure; finally, DEDW integrates historical network structure information to generate evolutionary graph features and implements dynamic community detection with the K-means algorithm. Experiments show that DEDW can mine the time-smooth change characteristics of dynamic communities, solve the problem of data sparseness in the process of node embedding, fully consider historical structure information, and improve the accuracy and stability of dynamic community detection. graph embedding; dynamic community detection; evolutionary clustering; DeepWalk; evolutionary computing +
Footnote †: journal: Applied Sciences  
network have not changed. How to balance the historical structure information and the current structure information in the network has become a major challenge. For problem 1, the graph embedding technology provides a better solution. Graph embedding provides an efficient way for the dynamic network model and representation. The aim of graph embedding is to map large-scale, high-dimensional networks to a low dimensional space. Graph embedding uses a low dimensional vector matrix to store the structure and attribute characteristics of nodes and networks. The storage space is much lower than the network structure represented by the adjacency matrix. For problem 2, evolutionary clustering provides a solution, which considers that, in the development of networks, dynamic networks have the characteristics of temporal smoothing and only local network elements change. Thus, the historical structure information has a greater influence on the current network. Using the historical network structure information to regularize the current network can make the network structure evolve smoothly. Inspired by evolutionary clustering, we extend the idea of evolutionary clustering to graph the embedding and propose the concept of evolutionary DeepWalk. Deepwalk [13] is one of the representative network structure analysis models, and it can learn the hidden information from networks combining random work with word2 vec models using unsupervised learning. Evolutionary DeepWalk is the fusion of the historical embedding features of the previous snapshot network and the network embedding features of the current snapshot network to construct a new comprehensive embedding feature, so that the static DeepWalk algorithm can be applied to the dynamic graph analysis. Then, we combine evolutionary DeepWalk with the clustering algorithm to build a dynamic community detection framework based on evolutionary DeepWalk (DEDW). Finally, we use the artificial and real-world datasets to verify the performance of the algorithm. The main contributions of our studies can be summarized as follows:  
1. According to the idea of evolutionary clustering, we propose the concept of evolutionary DeepWalk, and extend the application of static graph embedding algorithm DeepWalk to the field of dynamic graph analysis;
2. We propose a method of dynamic community detection based on evolutionary DeepWalk, which combines evolutionary DeepWalk with the clustering algorithm;
3. We compare our algorithm with five algorithms on ten datasets, including six artificial dynamic networks and four real-world dynamic networks. The experimental results demonstrate that DEDW improves accuracy, showing that network embedding is promising for a dynamic community detection. The remaining of this paper is organized as follows. Related work is surveyed in Section 2. Next, we describe the problem formulation and solution in Section 3.