Community Detection over Social Media: A Compressive Survey
Keywords:
Social Network Analysis, cloud, spammer, communities, Network Influence
Abstract
Social media mining is an emerging field with a lot of research areas such as, sentiment analysis, link prediction, spammer detection, and community detection. In today’s scenario, researchers are working in the area of community detection and sentiment analysis because the main component of social media is user. Users create different types of community in social world. The ideas and discussions in the community may be negative or positive. To detect the communities and their behavior researcher have done a lot of work, but still two major issues are presents per survey, Scalability and Quality of the community. These issues of community detection motivate to work in this area of social media mining. This paper gives a bird eye view over social media and community detection.
Downloads
Download data is not yet available.
References
[1]. Samadi, Mohammadreza, Alexander Nikolaev, and Rakesh Nagi. "A subjective evidence model for influence maximization in social networks." Omega 59 (2016): 263-278.
[2]. Wang, Qiyao, et al. "Influence maximization in social networks under an independent cascade-based model." Physica A: Statistical Mechanics and its Applications 444 (2016): 20-34.
[3]. Lee, Sang Yup. "Homophily and social influence among online casual game players." Telematics and Informatics 32.4 (2015): 656-666.
[4]. Barbieri, Nicola, Francesco Bonchi, and Giuseppe Manco. "Influence-based network-oblivious community detection." 2013 IEEE 13th International Conference on Data Mining. IEEE, 2013.
[5]. Hosseini, Razieh, and Reza Azmi. "Memory-based label propagation algorithm for
community detection in social networks." 2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP). IEEE, 2015.
[6]. Yang, Jaewon, Julian McAuley, and Jure Leskovec. "Community detection in networks with node attributes." 2013 IEEE 13th International Conference on Data Mining. IEEE, 2013.
[7]. Fortunato, Santo. "Community detection in graphs." Physics reports 486.3-5 (2010): 75-174.
[8]. Malliaros, Fragkiskos D., and Michalis Vazirgiannis. "Clustering and community detection in directed networks: A survey." Physics reports 533.4 (2013): 95-142.
[9]. Gong, Maoguo, et al. "Community detection in networks by using multiobjective evolutionary algorithm with decomposition." Physica A: Statistical Mechanics and its Applications 391.15 (2012): 4050-4060.
[10]. Shen, Huawei, et al. "Detect overlapping and hierarchical community structure in networks." Physica A: Statistical Mechanics and its Applications 388.8 (2009): 1706-1712.
[11]. Xing, Yan, et al. "A node influence based label propagation algorithm for community detection in networks." The Scientific World Journal 2014 (2014).
[12]. Bonchi, Francesco. "Influence Propagation in Social Networks: A Data Mining Perspective." IEEE Intell. Informatics Bull. 12.1 (2011): 8-16.
[13]. Zhou, Xiaokang, et al. "Dynamic community mining and tracking based on temporal social network analysis." 2016 IEEE International Conference on Computer and Information Technology (CIT). IEEE, 2016.
[14]. Roughton, Adam, et al. "The crowd in the cloud: moving beyond traditional boundaries for large scale experiences in the cloud." Proceedings of the Twelfth Australasian User Interface Conference-Volume 117. 2011.
[15]. Zhou, Xiaokang, and Qun Jin. "User correlation discovery and dynamical profiling based on social streams." International Conference on Active Media Technology. Springer, Berlin, Heidelberg, 2012.
[16]. Newman, Mark EJ, and Michelle Girvan. "Finding and evaluating community structure in networks." Physical review E 69.2 (2004): 026113.
[17]. Shen, Wei, Jiawei Han, and Jianyong Wang. "A probabilistic model for linking named entities in web text with heterogeneous information networks." Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 2014.
[18]. Lancichinetti, Andrea, et al. "Finding statistically significant communities in networks." PloS one 6.4 (2011): e18961.
[19]. Long, Bo, et al. "Unsupervised learning on k-partite graphs." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. 2006.
[20]. Long, Bo, Zhongfei Mark Zhang, and Philip S. Yu. "A probabilistic framework for relational clustering." Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. 2007.
[21]. Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu, "Social Media Mining", https://dmml.asu.edu/smm 2014
[22]. Newman, Mark EJ. "Fast algorithm for detecting community structure in networks." Physical review E 69.6 (2004): 066133.
[23]. Flake, Gary William, Steve Lawrence, and C. Lee Giles. "Efficient identification of web communities." Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. 2000.
[24]. Girvan, Michelle, and Mark EJ Newman. "Community structure in social and biological networks." Proceedings of the national academy of sciences 99.12 (2002): 7821-7826.
[25]. Newman, Mark EJ. "Modularity and community structure in networks." Proceedings of the national academy of sciences 103.23 (2006): 8577-8582.
[26]. Ferrara, Emilio. "Community structure discovery in facebook." International Journal of Social Network Mining 1.1 (2012): 67-90.
[27]. Roughton, Adam, et al. "The crowd in the cloud: moving beyond traditional boundaries for large scale experiences in the cloud." Proceedings of the Twelfth Australasian User Interface Conference-Volume 117. 2011.
[28]. Zhou, Xiaokang, et al. "Enriching user search experience by mining social streams with heuristic stones and associative ripples." Multimedia tools and applications 63.1 (2013): 129-144.
[29]. Wang, Chang-Dong, Jian-Huang Lai, and S. Yu Philip. "NEIWalk: Community discovery in dynamic content-based networks." IEEE transactions on knowledge and data engineering 26.7 (2013): 1734-1748.
[2]. Wang, Qiyao, et al. "Influence maximization in social networks under an independent cascade-based model." Physica A: Statistical Mechanics and its Applications 444 (2016): 20-34.
[3]. Lee, Sang Yup. "Homophily and social influence among online casual game players." Telematics and Informatics 32.4 (2015): 656-666.
[4]. Barbieri, Nicola, Francesco Bonchi, and Giuseppe Manco. "Influence-based network-oblivious community detection." 2013 IEEE 13th International Conference on Data Mining. IEEE, 2013.
[5]. Hosseini, Razieh, and Reza Azmi. "Memory-based label propagation algorithm for
community detection in social networks." 2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP). IEEE, 2015.
[6]. Yang, Jaewon, Julian McAuley, and Jure Leskovec. "Community detection in networks with node attributes." 2013 IEEE 13th International Conference on Data Mining. IEEE, 2013.
[7]. Fortunato, Santo. "Community detection in graphs." Physics reports 486.3-5 (2010): 75-174.
[8]. Malliaros, Fragkiskos D., and Michalis Vazirgiannis. "Clustering and community detection in directed networks: A survey." Physics reports 533.4 (2013): 95-142.
[9]. Gong, Maoguo, et al. "Community detection in networks by using multiobjective evolutionary algorithm with decomposition." Physica A: Statistical Mechanics and its Applications 391.15 (2012): 4050-4060.
[10]. Shen, Huawei, et al. "Detect overlapping and hierarchical community structure in networks." Physica A: Statistical Mechanics and its Applications 388.8 (2009): 1706-1712.
[11]. Xing, Yan, et al. "A node influence based label propagation algorithm for community detection in networks." The Scientific World Journal 2014 (2014).
[12]. Bonchi, Francesco. "Influence Propagation in Social Networks: A Data Mining Perspective." IEEE Intell. Informatics Bull. 12.1 (2011): 8-16.
[13]. Zhou, Xiaokang, et al. "Dynamic community mining and tracking based on temporal social network analysis." 2016 IEEE International Conference on Computer and Information Technology (CIT). IEEE, 2016.
[14]. Roughton, Adam, et al. "The crowd in the cloud: moving beyond traditional boundaries for large scale experiences in the cloud." Proceedings of the Twelfth Australasian User Interface Conference-Volume 117. 2011.
[15]. Zhou, Xiaokang, and Qun Jin. "User correlation discovery and dynamical profiling based on social streams." International Conference on Active Media Technology. Springer, Berlin, Heidelberg, 2012.
[16]. Newman, Mark EJ, and Michelle Girvan. "Finding and evaluating community structure in networks." Physical review E 69.2 (2004): 026113.
[17]. Shen, Wei, Jiawei Han, and Jianyong Wang. "A probabilistic model for linking named entities in web text with heterogeneous information networks." Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 2014.
[18]. Lancichinetti, Andrea, et al. "Finding statistically significant communities in networks." PloS one 6.4 (2011): e18961.
[19]. Long, Bo, et al. "Unsupervised learning on k-partite graphs." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. 2006.
[20]. Long, Bo, Zhongfei Mark Zhang, and Philip S. Yu. "A probabilistic framework for relational clustering." Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. 2007.
[21]. Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu, "Social Media Mining", https://dmml.asu.edu/smm 2014
[22]. Newman, Mark EJ. "Fast algorithm for detecting community structure in networks." Physical review E 69.6 (2004): 066133.
[23]. Flake, Gary William, Steve Lawrence, and C. Lee Giles. "Efficient identification of web communities." Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. 2000.
[24]. Girvan, Michelle, and Mark EJ Newman. "Community structure in social and biological networks." Proceedings of the national academy of sciences 99.12 (2002): 7821-7826.
[25]. Newman, Mark EJ. "Modularity and community structure in networks." Proceedings of the national academy of sciences 103.23 (2006): 8577-8582.
[26]. Ferrara, Emilio. "Community structure discovery in facebook." International Journal of Social Network Mining 1.1 (2012): 67-90.
[27]. Roughton, Adam, et al. "The crowd in the cloud: moving beyond traditional boundaries for large scale experiences in the cloud." Proceedings of the Twelfth Australasian User Interface Conference-Volume 117. 2011.
[28]. Zhou, Xiaokang, et al. "Enriching user search experience by mining social streams with heuristic stones and associative ripples." Multimedia tools and applications 63.1 (2013): 129-144.
[29]. Wang, Chang-Dong, Jian-Huang Lai, and S. Yu Philip. "NEIWalk: Community discovery in dynamic content-based networks." IEEE transactions on knowledge and data engineering 26.7 (2013): 1734-1748.
Published
2020-09-23
How to Cite
Sharma, S., & Pandey, Y. (2020). Community Detection over Social Media: A Compressive Survey. International Journal of Advanced Computer Technology, 9(5), 01-06. Retrieved from https://ijact.org/index.php/ijact/article/view/58
Section
Articles
