Community Structure Detection In Networks Research Articles

Community structure detection in networks based on Tabu search

Community structure is a main structural feature of networks, which can model many complex systems. It illustrates a lot of information about networks like their internal organisation and the degree of similarity between network nodes. Many methods have been proposed to find community structure in networks. However, there is always requirement to new methods for many reasons especially the accuracy. In this paper, we present a new method to find community structure in networks. Our method is hierarchical algorithm based on Tabu Search metaheuristic. A dendrogram is built by our method after dividing a network several times. The community structure will be selected from the built dendrogram based on quality function called Modularity. Our method has been tested on artificial benchmark and real networks. Results demonstrate the superiority of our method compared with several state-of-art methods.

Approximation of Nash equilibria and the network community structure detection problem.

Game theory based methods designed to solve the problem of community structure detection in complex networks have emerged in recent years as an alternative to classical and optimization based approaches. The Mixed Nash Extremal Optimization uses a generative relation for the characterization of Nash equilibria to identify the community structure of a network by converting the problem into a non-cooperative game. This paper proposes a method to enhance this algorithm by reducing the number of payoff function evaluations. Numerical experiments performed on synthetic and real-world networks show that this approach is efficient, with results better or just as good as other state-of-the-art methods.

A survey on network community detection based on evolutionary computation

Uncovering community structures of a complex network can help us to understand how the network functions. Over the past few decades, network community detection has attracted growing research interest from many fields. Many community detection methods have been developed. Network community structure detection can be modelled as optimisation problems. Due to their inherent complexity, these problems often cannot be well solved by traditional optimisation methods. For this reason, evolutionary algorithms have been adopted as a major tool for dealing with community detection problems. This paper presents a survey on evolutionary algorithms for network community detection. The evolutionary algorithms in this survey cover both single objective and multiobjective optimisations. The network models involve weighted/unweighted, signed/unsigned, overlapping/non-overlapping and static/dynamic ones.

Network Community Structure Detection for Directional Neural Networks Inferred From Multichannel Multisubject EEG Data

In many neuroscience applications, one is interested in identifying the functional brain modules from multichannel, multiple subject neuroimaging data. However, most of the existing network community structure detection algorithms are limited to single undirected networks and cannot reveal the common community structure for a collection of directed networks. In this paper, we propose a community detection algorithm for weighted asymmetric (directed) networks representing the effective connectivity in the brain. Moreover, the issue of finding a common community structure across subjects is addressed by maximizing the total modularity of the group. Finally, the proposed community detection algorithm is applied to multichannel multisubject electroencephalogram data.

Two Phases Aggregation Method for Network Community Structure Detection

Two Phases Aggregation Method for Network Community Structure Detection

Group similarity based algorithm for network community structure detection

Community structure has an important influence on the structural and dynamic characteristics of the complex system. In the present study, a group similarity model is proposed for the measurement of similarity between two communities. So it can help us understand the mechanism of inter action between these communities. Moreover, based on this model, a hierarchical clustering based algorithm for network community structure detection is put forward. By this algorithm, one pair of communities with the largest similarity is merged in each iteration. And then an evaluation function is adopted for choosing the optimal partition. The algorithm gives a higher performance than many state-of-the-art community detection algorithms when tested on a series of real-world and synthetic networks. Especially, it performs better when the edge density of the network is high.