A Novel Clonal Selection Algorithm for Community Detection in Complex Networks

An Information-Theoretic Approach for Detecting Community Structure Based on Network Representation

Community structure is a network characteristic where nodes can be naturally divided into densely connected groups. Community structures are ubiquitous in social, biological, and technological networks. Revealing community structure in the network helps in the understanding of the topological associations and interactions of elements in the network, as well as helping to mine their potential information. However, this has been proven to be a difficult challenge. On the one hand, this is because there is no unified definition of the quality of a community; on the other hand, due to the complexity of the network, it is impossible to traverse all the possibilities of community partitions to find the best one. Aiming at performing high-accuracy community detection, an information-theoretic approach AMI-NRL was proposed. The approach first constructs a community evolution process based on the representation of the target network, then finds the most stable community structure during the evolution using an average-mutual-information-based criterion. The experiments show that the approach can effectively detect community structures on real-world datasets and synthetic datasets.

Community detection in dynamic social networks: A local evolutionary approach

Communities in social networks are groups of individuals who are connected with specific goals. Discovering information on the structure, members and types of changes of communities have always been of great interest. Despite the extensive global researches conducted on these, discovery has not been confirmed yet and researchers try to find methods and improve estimated techniques by using Data Mining tools, Graph Mining tools and artificial intelligence techniques. This paper proposes a novel two-phase approach based on global and local information to detect communities in social network. It explores the global information in the first phase and then exploits the local information in the second phase to discover communities more accurately. It also proposes a novel algorithm which exploits the local information and mines deeply for the second phase. Experimental results show that the proposed method has better performance and achieves more accurate results compared with the previous ones.

Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey

Bioinspired intelligent algorithm (BIA) is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research.