Learning Automata Approach for Social Networks

Abstract

The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.The books of this series are submitted to indexing to Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink.

Ontology based recommender system using social network data

“…In the second experiment, we used data provided from Kaggle [6] , called Twitter Friends and hashtags. It is a collection of Twitter users that includes users’ information, friends, last seen, language, trending topics the user tweeted about, and others.…”

“…The scope is to define a standard sampling methodology applicable where the OSN information flow is readily available. In [6] , four sampling algorithms such as DLAS, EDLAS, ICLA-NS, and FLAS based on learning automata, are explored to produce a scale-down representative subgraphs from OSN. The random walk exploring strategy, adopted in [15] , [16] , [17] , [18] , provides the base method to ensure unbiased sampling.…”

“…Event detection is for example crucial in traffic management [1] , fire control [2] , TV show hosting [3] , and smart-city management systems [4] , [5] . In combination with other data sources, OSN can boost complex decision making and risk management methodologies [6] . For instance, Twitter has been effectively exploited in many real-world incidents to communicate disaster warnings and disseminate information, capture the evolving trends, control resource consumption, or discover effective mitigation strategies bottom-up [7] , [8] , [9] .…”

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

“…The authors conducted several experiments to show the performance of the CLACD algorithm and compared it with some well-known community detection algorithms [28]. A comprehensive study on the learning automata approaches for community detection in complex social networks is reported in [7].…”

“…Many of real networks such as information, technological and social networks, are represented as network systems which have several common characteristics, such as a small‐world phenomenon [1], scale‐free network property [2] and in particular having modular structures feature (also known a cluster or community structures) [3]. In recent years, some studies have been made to show that community structures are a significant characteristic in the most complex networks such as social and information networks due to the strong tendencies among human beings to form common interest groups or social communities with those users having similar activities or common interests [4, 5]. In a network, a community structure is the set of vertices with more internal links inside the community than external links between them.…”