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Erschienen in:
A Randomized Approach for Structural and Message Based Private Friend Recommendation in Online Social Networks Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

An Ant Based Particle Swarm Optimization Algorithm for Maximum Clique Problem in Social Networks

verfasst von : Mohammad Soleimani-pouri, Alireza Rezvanian, Mohammad Reza Meybodi

Erschienen in: State of the Art Applications of Social Network Analysis

Verlag: Springer International Publishing

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Abstract

In recent years, social network services provide a suitable platform for analyzing the activity of users in social networks. In online social networks, interaction between users plays a key role in social network analysis. One of the important types of social structure is a full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for analysis of certain groups and communities in social networks. This paper proposed a new hybrid method using ant colony optimization algorithm and particle swarm optimization algorithm for finding a maximum clique in social networks. In the proposed method, it is improved process of pheromone update by particle swarm optimization in order to attain better results. Simulation results on popular standard social network benchmarks in comparison standard ant colony optimization algorithm are shown a relative enhancement of proposed algorithm.

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Vorheriges Kapitel Spatial and Temporal Evaluation of Network-Based Analysis of Human Mobility
Nächstes Kapitel XEngine: An XML Search Engine for Social Groups
Literatur
1.
Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, Cambridge
2.
Soleimani-Pouri M, Rezvanian A, Meybodi MR (2012) Solving maximum clique problem in stochastic graphs using learning automata. In: 2012 Fourth international conference on computational aspects of social networks (CASoN), pp 115–119
3.
Sadi S, Öğüdücü S, Uyar A (2010) An efficient community detection method using parallel clique-finding ants. In: 2010 IEEE congress on evolutionary computation (CEC), pp 1–7
4.
Fadigas IS, Pereira HBB (2013) A network approach based on cliques. Physica A 392(10):2576–2587
5.
Yang Q, Zhou P, Zhang H, Zhang J (2011) Clique discovery based on user similarity for online shopping recommendation. Inf Technol J 10:1587–1593
6.
Yan F, Cai S, Zhang M, Liu G, Deng Z (2012) A clique-superposition model for social networks. Science China Information Sciences, pp 1–19
7.
Liua Y, Zhanga D, China FYL (2011) A clique-based algorithm for constructing feasible timetables. Optim Methods Softw 26(2):281–294
8.
Butenko S, Wilhelm WE (2006) Clique-detection models in computational biochemistry and genomics. Eur J Oper Res 173(1):1–17MATHMathSciNet
9.
Liu J, Lee YT (2001) Graph-based method for face identification from a single 2D line drawing. IEEE Trans Pattern Anal Mach Intell 23(10):1106–1119
10.
Wang L, Wei R, Lin Y, Wang B (2010) A clique base node scheduling method for wireless sensor networks. J Netw Comput Appl 33(4):383–396
11.
Kleinberg J, Tardos E (2005) Algorithm design. Addison Wesley
12.
Karp RM (1972) Reducibility among combinatorial problems. Complex Comput Comput 40(4):85–103MathSciNet
13.
Fenet S, Solnon C (2003) Searching for maximum cliques with ant colony optimization. In: Cagnoni S, Johnson CG, Cardalda JJR, Marchiori E, Corne DW, Meyer J, Gottlieb J, Middendorf M, Guillot G, Raidl R, Hart E (eds) EvoWorkshops. LNCS, vol 2611. Springer, Heidelberg, pp 236–245
14.
Xu X, Ma J, Lei J (2007) An improved ant colony optimization for the maximum clique problem. In: 3rd International conference on natural computation (ICNC), pp 766–770
15.
Al-Fayoumi M, Banerjee S, Mahanti PK (2009) Analysis of social network using clever ant colony metaphor. World Acad Sci Eng Technol 53:970–974
16.
Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence. Oxford University Press, Oxford
17.
Socha K, Dorigo M (2008) Ant colony optimization for continuous domains. Eur J Oper Res 185(3):1155–1173MATHMathSciNet
18.
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks, pp 1942–1948
19.
Amiri F, Yazdani N, Faili H, Rezvanian A (2012) A novel community detection algorithm for privacy preservation in social networks. In: Abraham A, Thampi SM (eds) ISI 2012. AISC, vol 182. Springer, Heidelberg, pp 443–450
20.
Nabizadeh S, Faez K, Tavassoli S, Rezvanian A (2010) A novel method for multi-level image thresholding using particle swarm Optimization algorithms. In: 2010 2nd International conference on computer engineering and technology (ICCET), pp v4-271–275
21.
Nabizadeh S, Rezvanian A, Meybodi MR (2012) A multi-swarm cellular PSO based on clonal selection algorithm in dynamic environments. In: 2012 International conference on informatics, electronics and vision (ICIEV), pp 482–486
22.
Nabizadeh S, Rezvanian A, Meybodi MR (2012) Tracking extrema in dynamic environment using multi-swarm cellular pso with local search. Int J Electron Inform 1(1):29–37
Metadaten
Titel
An Ant Based Particle Swarm Optimization Algorithm for Maximum Clique Problem in Social Networks
verfasst von
Mohammad Soleimani-pouri
Alireza Rezvanian
Mohammad Reza Meybodi
Copyright-Jahr
2014
Buch
State of the Art Applications of Social Network Analysis

Print ISBN: 978-3-319-05911-2

Electronic ISBN: 978-3-319-05912-9

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-05912-9_14

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