Local Community Detection Algorithm Based on Minimal Cluster

Abstract

In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

Overlapping Community Detection Based on Node Importance and Adjacency Information

“…If the conduction value of the cluster decreases when removing the nodes in a given cluster, it is removed outside the cluster and the iteration is repeated until the conduction value of the cluster reaches a stable state. Zhou et al [23] proposed a local community detection algorithm based on minimum cluster.…”

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

“…Overlapping community detection method based on local expansion is one of the most important methods to deal with the problem of overlapping community detection in largescale networks [12], which includes two steps: firstly, some nodes or some node sets in the network are selected as the seed of each community and continue to expand outward through the fitness function (optimization function) until a certain termination condition is met to form a community. Finally, the fitness function reaches the local optimal value as the termination condition for the end of community expansion.…”

“…Another situation is that, as shown in Figure 5, node 1 is an isolated node, but node 1 is connected to nodes 3 and 2. According to formula (12), there is node similarity S vw between the isolated node and its neighbors:where k v and k w represent the degree of node v and node w, respectively. en, calculate the average similarity between the node and all neighbor nodes s according to…”

Exploiting homophily to characterize communities in online social networks

“…In order to overcome these issues, several local community detection algorithms use the ego network structure to model the local information of a node. 21 The authors of Reference 22 proposed an approach that exploits both topological properties and node features to find densely connected sets of users having some common properties. The algorithm tries to maximize the log likelihood (the probability) of obtaining a given ego network graph by varying the groups which users belong to and treating them as latent variables.…”

“…There are several issues concerning the research of local community detection, such as, the need to determine both the initial node for local community and the local information. In order to overcome these issues, several local community detection algorithms use the ego network structure to model the local information of a node 21 …”