Distributed learning automata-based algorithm for community detection in complex networks

Abstract

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

Hybrid Onion Layered System for the Analysis of Collective Subjectivity in Social Networks

“…For community detection, clustering algorithms and statistical methods are highlighted. [32]; [33]; [34] (DLACD) Distributed learning automata based [35] (CL) Clustering [33]; [36]; [37] (WTS) Weak Tie Score [38] (BCL) BIGClam Overlaping Community detection [39] (CNN) Clauset-Newman-Moore [40] (WIC) Within and Inter Community [41] (CM) Centrality Measures [42]; [43]; [44]; [45]; [46] (RW) Random Walk [47] (DI) Difussion (MLR) Multivariate Linear Regression [48] (CM) Centrality Measures [49]; [50]; [51]; [52]; [53]; [54]; [55] (SM) Statistical Methods [56]; [57]; [58]; [59]; [60]; [61]; [31]; [62] (BoW) Bag of Words [34] (SVM) Support Vector Machine [43] In summary, the results of this search allow us to conclude that research on the phenomena occurring in the context of digital social networks has been marked by the implementation of methods and techniques that allow taking advantage of the potential of the content available on the web, the increase in online interactions and technological evolution. In exponential growth, the collective behavior underlying social networks is undoubtedly a source of knowledge that requires further research.…”

AntLP: ant‐based label propagation algorithm for community detection in social networks

“…Khomami et al [26] investigated a distributed learning automaton‐based algorithm for detecting communities in deterministic graphs. According to this algorithm, a set of learning automata interact with each other in order to identify high‐density local communities by updating the action probability vector of a network of cooperative learning automata.…”

Smart Energy Control Internet of Things based Agriculture Clustered Scheme for Smart Farming

“…With the help of an opportunistic multi-path routing system with the goal of minimizing the energy consumed by the selection process of the forwarding nodes to prolong the life of the network [13]. Authors proposed an algorithm based on Distributed Learning Automation (DLA) to boost network life by taking into account various routing constraints such as end-to-end delay and reliability in the selection process of data transmission routes to the base station [14].Researches proposed a diagnostic data collection protocol that considers the clustering and multi-path routing to extend the network's lifetime in IoT network [15] The LoRa Alliance presents the protocol stack for low-power and wide-area Internet of Things (IoT) networking technologies compatible with indoor transmission [16]. Researchers proposed a protocol named as dynamic distributed framework protocol.…”