Access the full text.
Sign up today, get DeepDyve free for 14 days.
E. Cozzo, G. Arruda, F. Rodrigues, Y. Moreno (2018)
Multiplex Networks: Basic Formalism and Structural Properties
Dongsheng Luo, Yuchen Bian, Yaowei Yan, Xiao Liu, Jun Huan, Xiang Zhang (2020)
Local Community Detection in Multiple NetworksProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
L. Hagen, A. Kahng (1991)
New spectral methods for ratio cut partitioning and clusteringIEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 11
(Hanteer O, Rossi L (2019) The meaning of dissimilar: an evaluation of various similarity quantification approaches used to evaluate community detection solutions. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, 2019, pp. 513–518)
Hanteer O, Rossi L (2019) The meaning of dissimilar: an evaluation of various similarity quantification approaches used to evaluate community detection solutions. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, 2019, pp. 513–518Hanteer O, Rossi L (2019) The meaning of dissimilar: an evaluation of various similarity quantification approaches used to evaluate community detection solutions. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, 2019, pp. 513–518, Hanteer O, Rossi L (2019) The meaning of dissimilar: an evaluation of various similarity quantification approaches used to evaluate community detection solutions. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, 2019, pp. 513–518
(B. Hu et al. (2020) Loan default analysis with multiplex graph learning. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp. 2525–2532)
B. Hu et al. (2020) Loan default analysis with multiplex graph learning. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp. 2525–2532B. Hu et al. (2020) Loan default analysis with multiplex graph learning. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp. 2525–2532, B. Hu et al. (2020) Loan default analysis with multiplex graph learning. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp. 2525–2532
(Alassad M, Hussain M N, Agarwal N (2019b) Finding fake news key spreaders in complex social networks by using Bi-level decomposition optimization method. In: International conference on modelling and simulation of social-behavioural phenomena in creative societies, pp. 41–54)
Alassad M, Hussain M N, Agarwal N (2019b) Finding fake news key spreaders in complex social networks by using Bi-level decomposition optimization method. In: International conference on modelling and simulation of social-behavioural phenomena in creative societies, pp. 41–54Alassad M, Hussain M N, Agarwal N (2019b) Finding fake news key spreaders in complex social networks by using Bi-level decomposition optimization method. In: International conference on modelling and simulation of social-behavioural phenomena in creative societies, pp. 41–54, Alassad M, Hussain M N, Agarwal N (2019b) Finding fake news key spreaders in complex social networks by using Bi-level decomposition optimization method. In: International conference on modelling and simulation of social-behavioural phenomena in creative societies, pp. 41–54
Cécile Bothorel, J. Cruz, Matteo Magnani, Barbora Micenková (2015)
Clustering attributed graphs: Models, measures and methodsNetwork Science, 3
Weifeng Zhang, Jingwen Mao, Yi Cao, Congfu Xu (2020)
Multiplex Graph Neural Networks for Multi-behavior RecommendationProceedings of the 29th ACM International Conference on Information & Knowledge Management
(Clauset A, Newman M E J, Moore C (2004) Finding community structure in very large networks Cond-Mat/0408187, vol. 70, p. 066111)
Clauset A, Newman M E J, Moore C (2004) Finding community structure in very large networks Cond-Mat/0408187, vol. 70, p. 066111Clauset A, Newman M E J, Moore C (2004) Finding community structure in very large networks Cond-Mat/0408187, vol. 70, p. 066111, Clauset A, Newman M E J, Moore C (2004) Finding community structure in very large networks Cond-Mat/0408187, vol. 70, p. 066111
Mustafa Alassad, Muhammad Hussain, Nitin Agarwal (2020)
Developing Graph Theoretic Techniques to Identify Amplification and Coordination Activities of Influential Sets of Users
(Falih I, Kanawati R (2015) MUNA: a multiplex network analysis library. In: proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and Mining 2015, pp. 757–760)
Falih I, Kanawati R (2015) MUNA: a multiplex network analysis library. In: proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and Mining 2015, pp. 757–760Falih I, Kanawati R (2015) MUNA: a multiplex network analysis library. In: proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and Mining 2015, pp. 757–760, Falih I, Kanawati R (2015) MUNA: a multiplex network analysis library. In: proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and Mining 2015, pp. 757–760
Feng Zou, Debao Chen, De-shuang Huang, Renquan Lu, Xude Wang (2019)
Inverse modelling-based multi-objective evolutionary algorithm with decomposition for community detection in complex networksPhysica A: Statistical Mechanics and its Applications
Mustafa Alassad, Muhammad Hussain, Nitin Agarwal (2021)
Comprehensive decomposition optimization method for locating key sets of commenters spreading conspiracy theory in complex social networksCentral European Journal of Operations Research, 30
Mustafa Alassad, Billy Spann, Samer Al-khateeb, Dr Agarwal (2021)
Using Computational Social Science Techniques to Identify Coordinated Cyber Threats to Smart City NetworksDesign and Construction of Smart Cities
(Basu P, Sundaram R, Dippel M (2015) Multiplex networks. In Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, pp. 456–463)
Basu P, Sundaram R, Dippel M (2015) Multiplex networks. In Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, pp. 456–463Basu P, Sundaram R, Dippel M (2015) Multiplex networks. In Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, pp. 456–463, Basu P, Sundaram R, Dippel M (2015) Multiplex networks. In Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, pp. 456–463
Zhangtao Li, J. Liu (2016)
A multi-agent genetic algorithm for community detection in complex networksPhysica A-statistical Mechanics and Its Applications, 449
(Luo D, Bian Y, Yan Y, Liu X, Huan J, Zhang X (2020) Local community detection in multiple networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 266–274)
Luo D, Bian Y, Yan Y, Liu X, Huan J, Zhang X (2020) Local community detection in multiple networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 266–274Luo D, Bian Y, Yan Y, Liu X, Huan J, Zhang X (2020) Local community detection in multiple networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 266–274, Luo D, Bian Y, Yan Y, Liu X, Huan J, Zhang X (2020) Local community detection in multiple networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 266–274
(Ding C, Wang J (2021) Link reciprocity in directed multiplex networks. In: 2021 5th international conference on cloud and big data computing (ICCBDC), 2021, pp. 102–108)
Ding C, Wang J (2021) Link reciprocity in directed multiplex networks. In: 2021 5th international conference on cloud and big data computing (ICCBDC), 2021, pp. 102–108Ding C, Wang J (2021) Link reciprocity in directed multiplex networks. In: 2021 5th international conference on cloud and big data computing (ICCBDC), 2021, pp. 102–108, Ding C, Wang J (2021) Link reciprocity in directed multiplex networks. In: 2021 5th international conference on cloud and big data computing (ICCBDC), 2021, pp. 102–108
M. Moradi, Saeed Parsa (2019)
An evolutionary method for community detection using a novel local search strategyPhysica A: Statistical Mechanics and its Applications
(Mitra A, Vijayan P, Sanasam R, Goswami D, Parthasarathy S, Ravindran B (2021) Semi-Supervised Deep Learning for Multiplex Networks. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 2021, vol. 1, no. 1, pp. 1234–1244)
Mitra A, Vijayan P, Sanasam R, Goswami D, Parthasarathy S, Ravindran B (2021) Semi-Supervised Deep Learning for Multiplex Networks. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 2021, vol. 1, no. 1, pp. 1234–1244Mitra A, Vijayan P, Sanasam R, Goswami D, Parthasarathy S, Ravindran B (2021) Semi-Supervised Deep Learning for Multiplex Networks. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 2021, vol. 1, no. 1, pp. 1234–1244, Mitra A, Vijayan P, Sanasam R, Goswami D, Parthasarathy S, Ravindran B (2021) Semi-Supervised Deep Learning for Multiplex Networks. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 2021, vol. 1, no. 1, pp. 1234–1244
Anasua Mitra, Priyesh Vijayan, Ranbir Sanasam, D. Goswami, S. Parthasarathy, Balaraman Ravindran (2021)
Semi-Supervised Deep Learning for Multiplex NetworksProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
R. Zafarani, Mohammad Abbasi, Huan Liu (2014)
Social Media Mining: An Introduction
MEJ Newman (2004)
Detecting community structure in networksEur Phys J B - Condens Matter, 38
(Tweepy. [Online]. Available: https://www.tweepy.org/. [Accessed: 25-Jun-2022])
Tweepy. [Online]. Available: https://www.tweepy.org/. [Accessed: 25-Jun-2022]Tweepy. [Online]. Available: https://www.tweepy.org/. [Accessed: 25-Jun-2022], Tweepy. [Online]. Available: https://www.tweepy.org/. [Accessed: 25-Jun-2022]
Demographics of social media users and adoption in the United States | Pew research center
(Alassad M, Agarwal N, Hussain M N (2019a) Examining intensive groups in youtube commenter networks. In: proceedings of 12th international conference, SBP-BRiMS 2019a, no. 12, pp. 224–233)
Alassad M, Agarwal N, Hussain M N (2019a) Examining intensive groups in youtube commenter networks. In: proceedings of 12th international conference, SBP-BRiMS 2019a, no. 12, pp. 224–233Alassad M, Agarwal N, Hussain M N (2019a) Examining intensive groups in youtube commenter networks. In: proceedings of 12th international conference, SBP-BRiMS 2019a, no. 12, pp. 224–233, Alassad M, Agarwal N, Hussain M N (2019a) Examining intensive groups in youtube commenter networks. In: proceedings of 12th international conference, SBP-BRiMS 2019a, no. 12, pp. 224–233
(Alassad M, Hussain M N, Agarwal N (2020) Developing graph theoretic techniques to identify amplification and coordination activities of influential sets of users. In: Accepted in international conference on social computing, behavioral-cultural modeling, & prediction and behavior representation in modeling and simulation, 2020, pp. 192–201)
Alassad M, Hussain M N, Agarwal N (2020) Developing graph theoretic techniques to identify amplification and coordination activities of influential sets of users. In: Accepted in international conference on social computing, behavioral-cultural modeling, & prediction and behavior representation in modeling and simulation, 2020, pp. 192–201Alassad M, Hussain M N, Agarwal N (2020) Developing graph theoretic techniques to identify amplification and coordination activities of influential sets of users. In: Accepted in international conference on social computing, behavioral-cultural modeling, & prediction and behavior representation in modeling and simulation, 2020, pp. 192–201, Alassad M, Hussain M N, Agarwal N (2020) Developing graph theoretic techniques to identify amplification and coordination activities of influential sets of users. In: Accepted in international conference on social computing, behavioral-cultural modeling, & prediction and behavior representation in modeling and simulation, 2020, pp. 192–201
(Scrapy | A Fast and Powerful Scraping and web crawling framework. [Online]. Available: https://scrapy.org/. [Accessed: 02-Jun-2022])
Scrapy | A Fast and Powerful Scraping and web crawling framework. [Online]. Available: https://scrapy.org/. [Accessed: 02-Jun-2022]Scrapy | A Fast and Powerful Scraping and web crawling framework. [Online]. Available: https://scrapy.org/. [Accessed: 02-Jun-2022], Scrapy | A Fast and Powerful Scraping and web crawling framework. [Online]. Available: https://scrapy.org/. [Accessed: 02-Jun-2022]
Cangfeng Ding, Jun Wang (2021)
Link reciprocity in directed multiplex networksProceedings of the 2021 5th International Conference on Cloud and Big Data Computing
(2003)
The European Physical Journal B
Zhaofeng Li, Fuhan Yan, Yichuan Jiang
Autonomous Agents and Multi-agent Systems Cross-layers Cascade in Multiplex Networks Cross-layers Cascade in Multiplex Networks
Fast and Powerful Scraping and web crawling framework
Mustafa Alassad, Billy Spann, Nitin Agarwal (2021)
Combining advanced computational social science and graph theoretic techniques to reveal adversarial information operationsInf. Process. Manag., 58
Z Li, F Yan, Y Jiang (2015)
Cross-layers cascade in multiplex networksAuton Agent Multi Agent Syst, 29
P. Basu, Ravi Sundaram, Matthew Dippel (2015)
Multiplex networks: A generative model and algorithmic complexity2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
(Zhang W, Mao J, Cao Y, Xu C (2020) Multiplex graph neural networks for multi-behavior recommendation. In: Proceedings of the 29th ACM international conference on information & knowledge management, 2020, pp. 2313–2316)
Zhang W, Mao J, Cao Y, Xu C (2020) Multiplex graph neural networks for multi-behavior recommendation. In: Proceedings of the 29th ACM international conference on information & knowledge management, 2020, pp. 2313–2316Zhang W, Mao J, Cao Y, Xu C (2020) Multiplex graph neural networks for multi-behavior recommendation. In: Proceedings of the 29th ACM international conference on information & knowledge management, 2020, pp. 2313–2316, Zhang W, Mao J, Cao Y, Xu C (2020) Multiplex graph neural networks for multi-behavior recommendation. In: Proceedings of the 29th ACM international conference on information & knowledge management, 2020, pp. 2313–2316
(COVID-19 MISINFO | Home Page 2021. [Online]. Available: https://cosmos.ualr.edu/covid-19. [Accessed: 16-Jul-2021])
COVID-19 MISINFO | Home Page 2021. [Online]. Available: https://cosmos.ualr.edu/covid-19. [Accessed: 16-Jul-2021]COVID-19 MISINFO | Home Page 2021. [Online]. Available: https://cosmos.ualr.edu/covid-19. [Accessed: 16-Jul-2021], COVID-19 MISINFO | Home Page 2021. [Online]. Available: https://cosmos.ualr.edu/covid-19. [Accessed: 16-Jul-2021]
(Rastin P, Kanawati R (2015) A multiplex-network based approach for clustering ensemble selection. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, pp. 1332–1339)
Rastin P, Kanawati R (2015) A multiplex-network based approach for clustering ensemble selection. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, pp. 1332–1339Rastin P, Kanawati R (2015) A multiplex-network based approach for clustering ensemble selection. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, pp. 1332–1339, Rastin P, Kanawati R (2015) A multiplex-network based approach for clustering ensemble selection. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, pp. 1332–1339
(Al-Khateeb S, Agarwal N (2014) Modeling flash mobs in cybernetic space: Evaluating threats of emerging socio-technical behaviors to human security. In: Proc. - 2014 IEEE Jt. Intell. Secur. Informatics Conf. JISIC 2014, vol. 7, no. 1, p. 328)
Al-Khateeb S, Agarwal N (2014) Modeling flash mobs in cybernetic space: Evaluating threats of emerging socio-technical behaviors to human security. In: Proc. - 2014 IEEE Jt. Intell. Secur. Informatics Conf. JISIC 2014, vol. 7, no. 1, p. 328Al-Khateeb S, Agarwal N (2014) Modeling flash mobs in cybernetic space: Evaluating threats of emerging socio-technical behaviors to human security. In: Proc. - 2014 IEEE Jt. Intell. Secur. Informatics Conf. JISIC 2014, vol. 7, no. 1, p. 328, Al-Khateeb S, Agarwal N (2014) Modeling flash mobs in cybernetic space: Evaluating threats of emerging socio-technical behaviors to human security. In: Proc. - 2014 IEEE Jt. Intell. Secur. Informatics Conf. JISIC 2014, vol. 7, no. 1, p. 328
Cham COVID-19 MISINFO | Home Page 2021
N. Chen, Yun Liu, Haiqiang Chen, Junjun Cheng (2017)
Detecting communities in social networks using label propagation with information entropyPhysica A-statistical Mechanics and Its Applications, 471
A. Clauset, M. Newman, Cristopher Moore (2004)
Finding community structure in very large networks.Physical review. E, Statistical, nonlinear, and soft matter physics, 70 6 Pt 2
Fatih Şen, R. Wigand, Nitin Agarwal, Serpil Yuce, R. Kasprzyk (2016)
Focal structures analysis: identifying influential sets of individuals in a social networkSocial Network Analysis and Mining, 6
Obaida Hanteer, R. Interdonato, Matteo Magnani, Andrea Tagarelli, L. Rossi (2019)
Community Detection in Multiplex NetworksACM Computing Surveys (CSUR), 54
Issam Falih, R. Kanawati (2015)
MUMA: A multiplex network analysis library2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
R. Guimerà, M. Sales-Pardo, L. Amaral (2007)
Module identification in bipartite and directed networks.Physical review. E, Statistical, nonlinear, and soft matter physics, 76 3 Pt 2
Parisa Rastin, R. Kanawati (2015)
A multiplex-network based approach for clustering ensemble selection2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Mustafa Alassad, Nitin Agarwal, Muhammad Hussain (2019)
Examining Intensive Groups in YouTube Commenter Networks
Xuequn Li, Shuming Zhou, Jiafei Liu, Guanqin Lian, Gaolin Chen, Chen Lin (2019)
Communities detection in social network based on local edge centralityPhysica A: Statistical Mechanics and its Applications
Obaida Hanteer, L. Rossi (2019)
The meaning of dissimilar: an evaluation of various similarity quantification approaches used to evaluate community detection solutionsProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Samer Al-khateeb, Nitin Agarwal (2014)
Modeling flash mobs in cybernetic space: evaluating threats of emerging socio-technical behaviors to human security2014 IEEE Joint Intelligence and Security Informatics Conference
Mustafa Alassad, Muhammad Hussain, Nitin Agarwal (2019)
Finding Fake News Key Spreaders in Complex Social Networks by Using Bi-Level Decomposition Optimization MethodCommunications in Computer and Information Science
Binbin Hu, Zhiqiang Zhang, Jun Zhou, Jingli Fang, Quanhui Jia, Yanming Fang, Quan Yu, Yuan Qi (2020)
Loan Default Analysis with Multiplex Graph LearningProceedings of the 29th ACM International Conference on Information & Knowledge Management
(Demographics of social media users and adoption in the United States | Pew research center, 2021. [Online]. Available: https://www.pewresearch.org/internet/fact-sheet/social-media/. [Accessed: 15-Jan-2022])
Demographics of social media users and adoption in the United States | Pew research center, 2021. [Online]. Available: https://www.pewresearch.org/internet/fact-sheet/social-media/. [Accessed: 15-Jan-2022]Demographics of social media users and adoption in the United States | Pew research center, 2021. [Online]. Available: https://www.pewresearch.org/internet/fact-sheet/social-media/. [Accessed: 15-Jan-2022], Demographics of social media users and adoption in the United States | Pew research center, 2021. [Online]. Available: https://www.pewresearch.org/internet/fact-sheet/social-media/. [Accessed: 15-Jan-2022]
Focal structures are key sets of individuals who may be responsible for coordinating events, protests, or leading citizen engagement efforts on social media networks. Discovering focal structures that are able to promote online social campaigns is important but complex. Unlike influential individuals, focal structures can affect large-scale complex social processes. In our prior work, we applied a greedy algorithm and bi-level decomposition optimization solution to identify focal structures in social media networks. However, the outcomes lacked a contextual representation of the focal structures that affected interpretability. In this research, we present a novel contextual focal structure analysis (CFSA) model to enhance the discovery and the interpretability of the focal structures to provide the context in terms of the content shared by the focal structures through their communication network. The model utilizes multiplex networks, where one layer is the user network based on mentions, replies, friends, and followers, and the second layer is the hashtag co-occurrence network. The two layers have interconnections based on the user hashtag relations. The model's performance was evaluated on various real-world datasets from Twitter related to COVID-19, the Trump vaccine hashtag, and the Black Lives Matter (BLM) social movement during the 2020–2021 time. The model discovered contextual focal structures (CFS) sets revealed the context regarding individuals’ interests. We then evaluated the model's efficacy using various network structural measures such as the modularity method, network stability, and average clustering coefficient to measure the influence of the CFS sets in the network. Ranking correlation coefficient (RCC) was used to conduct the comparative evaluation with real-world scenarios to find the correlated solutions.
Social Network Analysis and Mining – Springer Journals
Published: Dec 1, 2022
Keywords: Multiplex Networks; Complex Network; Focal Structures; Entropy; Information Gain; COVID-19; Contextual Focal Structures
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.