Community structure is an interesting feature of complex networks. The problem of community detection has attracted many research efforts in recent years. Most of the algorithms developed for this purpose take advantage of single-objective optimization methods which may be ineffective for complex networks. In this article, a novel multi-objective community detection method based on a modified version of particle swarm optimization, named MOPSO-Net is proposed. Kernel k-means (KKM) and ratio cut (RC) are employed as objective criteria to be minimized. Our innovation in PSO algorithm is changing the moving strategy of particles. Experiments on synthetic and real-world networks confirm a significant improvement in terms of normalized mutual information (NMI) and modularity in comparison with recent similar approaches.

A multi-objective particle swarm optimization algorithm for community detection in complex networks

https://research.tees.ac.uk/ws/files/8808839/ModularityOptimizationWithK_plex_revision.pdf

Community Detection Based on Modularity and k-Plexes

These days, community detection is an important field to understand the topology and functions in the complex networks. In this article, we propose a novel Community Detection Algorithm based on Structural Similarity (CDASS) that executed in two consecutive phases. In the first phase, we randomly remove some low similarity edges. Therefore, the network graph is converted into several disconnected components that are considered as primary communities. In the following, the primary communities are merged in order to identify the final community structure close to real communities. In the second phase, we use an our identified evaluation function to select the best communities between overall random generated partitions. Finally, we evaluate CDASS algorithm using several scenarios extracted from artificial and real networks. The results, obtained from simulation with these scenarios, show that proposed algorithm detects communities with high accuracy close to optimal case and is applicable in the large and small network topologies.

Community detection in complex networks using structural similarity

A comprehensive literature review on community detection: Approaches and applications

Community detection is an NP-hard problem. Therefore, evolutionary-based optimization methods are conventionally applied to cope with the problem. The primary challenge regarding the application of evolutionary-based approaches, specifically to handle large complex networks, is their relatively long execution time. In this respect, this article proposes an extension of a known genetic algorithm, Genetic Algorithm for Community Detection (GACD), for community detection. This new extension is supplied with a novel local search strategy to speed up the convergence and improve the accuracy of the GACD algorithm. To reduce the search space, a locus-based representation of the complex network, in which communities are to be detected, is applied. This type of representation incorporates domain-specific knowledge with the solutions through initialization and reproduction operators. In addition, it does not need to know the number of communities at the beginning of the search process. Our experiments with the real-world and Lancichinetti–Fortunato–Radicchi (LFR) network datasets demonstrate the relatively high capacity of our proposed genetic algorithm in detecting communities with relatively fewer generations and more precision.

An evolutionary method for community detection using a novel local search strategy

Social network analysis is an important tool that can be used in many domains. Among the social network analysis algorithms and tools we find the community structure detection. Many community structure detection algorithms have been developed over years, but most of them have a high computational complexity. In this paper we propose a new approach to find a community structure in networks. Our approach is more stable, accurate and effective to find the community structure in networks with high inter-community links. Our method operates in two phases. In the first phase, our method finds all the circuits in order to split the network into small elementary groups. Then (in the second phase)the community structure will be found by merging iteratively the different sub graphs resulting from the first phase by a fusion principle used in clique-based methods. Our method was evaluated on different types of networks. We tested our method on generated-computer and real-world networks. A comparison was held between our method and some known methods of community detection. The results show that our method is very effective in finding communities in networks and saving the time complexity.

Detecting communities in social networks based on cliques

Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

A new hierarchical method to find community structure in networks

Ad Hoc network is a collection of wireless devices (nodes) forming a temporary network which has any established infrastructure or centralized administration These devices have the ability to move which means that the topology of Ad Hoc network changes according to the mobility of nodes In order to maintain the connectivity between nodes in Ad Hoc network it is necessary that mobile nodes work together Many routing protocols have been proposed to keep the connectivity between nodes, including the AODV protocol (Ad Hoc On-Demand Distance Vector), which is maintained as a standard by the IETF This protocol has limitations in terms of stability of links between nodes To overcome these limitations, we proposed an improvement of AODV protocol to find stable links, which endure between source and destination Our idea is based on the quality of the received signal strength between the different nodes In this article we proposed a new strategy of finding routes and predicting ruptured links The results of simulation and comparison with the standard version of AODV show the effectiveness of our propositions

New Routing Protocol in Ad Hoc Networks

Wireless Sensor network Lifetime Improving Based on Whale Optimization Algorithm

Routing is very important to ensure the transmission of data between nodes in wireless sensor network (WSN). Many routing protocols have been proposed. WSN has many common features with Ad Hoc network. In this paper we have evaluated two Ad Hoc routing protocols in WSN topology. The results showed that we can use Ad Hoc routing protocols to ensure the transmission of data in WSN topology. The impact of mobility has been studied on Directed Diffusion protocol in WSN topology.

Simulation and Analysis of Routing Protocols in Wireless Sensor Network

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