Local Core Members Aided Community Structure Detection

Abstract

The relationship of friends in social networks can be strong or weak. Some research works have shown that a close relationship between friends conducts good community structure. Based on this result, we propose an effective method in detecting community structure in social networks based on the closeness of relations among neighbors. This method calculates the gravity between each neighbor node to core nodes, then makes judgement if the node should be classified in the community or not, and finally form the process of community detection. The experimental results show that the proposed method can mine the social structure efficiently with a low computational complexity. IntroductionCommunity discovery is one of the core research areas in social network analysis [1] which is also benefit to other related research areas, such as public opinion monitoring, advertising precision delivery, impact analysis. As early as in 1927, Stuart Rice [2] proposed the discovery of small political groups based on voting patterns. Until 2002, Newman et al. proposed the GN algorithm [2] , and community analysis began to flourish. The GN algorithm is a classical graph splitting algorithm, which uses the edge betweenness to measure the importance of the edge in the entire network, the larger betweenness the edge has, the edge is more likely to connect two different communities.Newman proposed a fast agglomeration algorithm based on the GN algorithm [3] via repeatedly calculating the network shortest path to update the edge betweennesses until the network is divided into appropriate community structures, for an unweighted graph with n nodes and m edges, the time complexity is as high as ( 3 ) . Through continuously merging nodes and communities, the highest modularity of the community is generated. The efficiency of the algorithm is greatly improved. In order to further reduce the computational complexity, many improved algorithms, such as CNM algorithm [4] and FastUnfolding algorithm [5] based on modularity optimization, have largely sacrificed the quality of the results in order to ensure the speed of computation. Rosvall et al. [6] proposed the Infomap algorithm, a method based on

Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm

“…In Equation 1, the modularity is represented by Q and it is indicative of the ratio of internal links in a given community to the total number of links less than the expected value of the same ratio for the case where the links are distributed randomly across the network (Fan et al, 2018). Thus, the value of Q must lie within the range between -1 and +1.…”

Core-reviewer recommendation based on Pull Request topic model and collaborator social network

“…Meanwhile, their approaches focus on recommending reviewers for new PRs, but the recommended reviewers include all developers, not only the core developers. In open-source ecosystem like GitHub (Liao et al 2018(Liao et al , 2019a, if the recommended reviewer is not a core developer, the reviewer cannot merge or refuse the PR when he or she thinks the PR meets or does not meet the demand of the project directly. The PR still needs waiting core developer to do the merging or refusing operation.…”

“…GitHub, a popular open-source community (Begel et al 2013;Liao et al 2018), has attracted the participations of tens of millions of developers and millions of open-source projects. Pull Request (PR) is a major contributor to external developers of open-source projects (Gousios et al 2014).…”

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“…Thung et al (2013) proposed a network of developers and projects, and applied the PageRank algorithm to calculate the weight of the nodes. Fan et al (2018) proposed a kind of discovery algorithm based on local core members to solve the problem of community detection in social networks, and get the importance of each node in the process. Yang et al (2012) studied the graph structure and random walk model and proposed the SocialRank algorithm to calculate the individual influence.…”