# An Information-Theoretic Approach for Detecting Community Structure Based on Network Representation
## 2 Related Work
The label propagation method detects the communities by first labeling some nodes and propagating labels between nodes by the similarity of two nodes, such as the COPRA algorithm [PERSON] et al. (2018) and the CLPA-GNR algorithm [PERSON] et al. (2018). These types of algorithms are characterized by high operating efficiency but are less stable. On the basis of the idea of local optimization, local expansion methods follow the idea starting from multiple nodes and greedily expand to their neighborhoods until the specified community boundary conditions are reached, thereby obtaining overlapping/non-overlapping community partitions. Examples are LFM Girvan and [PERSON] (1989) and GCE [PERSON] et al. (2018). This type of method can be effectively applied to detect overlapping communities, but it is easy for it to fall into local optima due to the difficulty in dealing with global information in the process of expansion. Heuristic methods are also used for community detection. Infomap Infomap (2018) proposed by [PERSON] et al. encodes and compresses the network information and obtains the optimal community partitioning by simulated annealing. The clone selection algorithms such as CSA-Net [PERSON] et al. (2018) can be applied to detect community structures in complex networks at multiple resolutions. However,such algorithms often have multiple adjustable parameters. It is difficult to determine appropriate parameter values, and it is also easy to obtain trapping in the local optimum. The random-walking methods obtain structural information such as the closeness of the connections between nodes (or node correlation) through random walks in the network, and then are based on this structural information (or the preset community evaluation index) to detect communities. An example is the community detection algorithm based on positive and negative links [18] proposed by [PERSON] et al. Such a type of method is difficult to distinguish between the two partitions with similar community structures, which leads to the failure of finding the best partition. Recently, some motif-based methods have been proposed that focus on the higher-order structural characteristics of the network, e.g., EdMot [19] proposed by [PERSON] et al. Such methods have high efficiency, especially in large neural networks and collaboration networks, but have a lack of accuracy on unweighted and undirected networks compared with other methods. In recent years, researchers have devoted more attention to studying how to extract the structural information of networks by means of network representation learning, such as ComE [6], which constructs the \"community detection-community representation-node representation\" closed-loop framework, and MemeRep [20], which uses network representation to optimize the modularity density. Although gaining of a better representation of a network has been extensively studied in the literature, the issue of how to obtain accurate partitioning by using the representation has not gained much attention, while the community partitions are straightway obtained by clustering the representation vectors using methods such as K-means or DBSCAN, which lead to multiple results depending on the parameters and initialization methods. Our research pays more attention to how to use the network representation obtained to get a community partition with higher stability and accuracy. In our approach, a bottom-up community evolution process is generated using the representation of the target network, so that the most stable state of the process can be found as the optimal partition, by the usage of the AMI-based community evaluation index.