Detecting Overlapping Nodes in MLM Chain Network

When a particular node of a network concurrently belongs to more than a few communities, overlapping communities is form which is an open challenge related to community detection. This article evaluates the state-of-the-art in overlapping community detection algorithm in MLM Chain networks. Community discovery in MLM Chain networks is an interesting problem or task in complex networks, with the number of applications, particularly in the social and information networks knowledge extraction tasks. A community which is also referred or considered as a cluster or module is usually consisting of a group of nodes with more connections between its members than between its members and the remainder of the network. Clustering of social networks is an important task for analysis. A variety of clustering algorithms have recently been given to handle data that is not linearly separable. Customary graph partitioning algorithms are be unsuccessful to get the concealed knowledge present in modular structure appear, because they impose a top-down global view of a network. Due to the difficulties in detected communities and the limitations of scalable algorithms, the problem of overlapping community detection in large networks is remains an open problem. To avoid this problem or to get rid it, we proposed a method called Detecting Overlapping Node Structure a connection based algorithm proposed for discovering high quality overlapping structures in MLM Chain networks. The main idea of this paper is to find the seed by applying algorithm and then expanding these seeds. In our method, communities are allowed to overlap because communities are formed by adding peripheral nodes to cores. Here we focus on commonly used method commonly known as the tree edit distance. a polynomial-time algorithm is used for ordered trees compute it. Proposed method is fast, very limited parameter dependent and only requires local knowledge about the network chain. Furthermore, the community structures discovered is deterministic.