Abstract
Different methods and algorithms have been employed to carry out the task of community mining. Conversely, in the real world, many applications entail distributed and dynamically evolving networks, wherein resources and controls are not only decentralized, but also restructured frequently. This leads a problem of finding all communities from a given network, within that the links are dense, but between which they are sparse. This is referred as Network Community Mining Problem (NCMP). A network community contains a group of nodes connected based on certain relationships sometimes that refers to a special sort of network arrangement where the community mining is discovering all communities hidden in distributed networks based on their relevant local outlooks. To avoid the above mentioned problem, the existing work presented a novel model for characterizing network communities via introducing a stochastic process on networks and analyzing its dynamics based on the large deviation theory. By Using the fundamental properties of local mixing, then proposed an efficient implementation for that framework, called the LM (Network community mining based on Local Mixing properties) algorithm. There also some drawbacks are identified. Actual number of communities is estimated by using a recursive bisection approach, jointly with a predefined stopping criterion. The recursive bisection strategy does not optimize communication performance and the complexity of performing the partitioning. To solve these problems in proposed work a new community bipartition scheme is developed by using KD-Tree. Also, the stopping criterion is calculated automatically by efficiently determining the minimum Eigen-gap without explicitly computing eigenvalues. The experimental result shows that the proposed scheme is more effective and scalable when compared with the existing scheme.
Highlights
A social network is a social structure made up of single person called" nodes," which are joined by one or more particular types of interdependency, such as friendship, kinship, based on common interest, or a few financial exchange
As to speed up the pre-processing step, the construction of the initial KD-Tree can be distributed between the social networks
In any case of how the initial tree is built, each process accepts a node of the level log (p), which describes the local data partition on which a complete local KD-Tree is built
Summary
A social network is a social structure made up of single person (or entire organizations) called" nodes," which are joined (connected) by one or more particular types of interdependency, such as friendship, kinship, based on common interest, or a few financial exchange. Revising the dynamics of these social networks suits an interesting data mining charge. One of such applications is to perceive the up-and-coming communities. We suppose to utilize the methods of social network analysis and web mining to demonstrate the networks of the users and by this to determine the interest groups. Based on the perception of spectral signature, this work has offered a theoretical framework for characterizing, analyzing and mining communities of a specified network through gathering its spectral signature and one of its metastable states. Spectral signature quantity, to differentiate and analyze network communities and the utilizing the fundamental properties of meta-stability, that is nearby consistent and for the moment predetermined, a scalable completion for this structure, called LM algorithm. By computing the minimum Eigen-gap exclusive of unambiguously computing eigenvalues, regularly computing the stopping criterion
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