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New centrality measure for nodes based on user social status and behavior on Facebook
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Purpose: This paper aims to analyze the role of influential nodes on other users on Facebook social media sites by social and behavioral characteristics of users. Hence, a new centrality for user is defined, applying susceptible-infected recovered (SIR) model to identify influence of users. Results show that the combination of behavioral and social characteristics would be determined the most influential users that influence majority of nodes on social networks. Design/methodology/approach: In this paper, the authors define a new centrality for users, considering node status and behaviors. Thus, this node has a high level of influence. Node social status includes node degree, clustering coefficient and average neighbors’ node, and social status of node refers to user activities on Facebook social media website such as sending posts and receiving likes from other users. According to social status and user activity, the new centrality is defined. Finally, through the SIR model, the authors explore infection power of nodes and their influences of other node in the network. Findings: Results show that the proposed centrality is more effective than other centrality approaches, infecting more nodes in social network. Another significant point in this research is that users who have high social status and activities on Facebook are more influential than users who have only high social status on the Facebook social media. Originality/value: The influence of user on others in social media includes two key factors. The first factor is user social status such as node degree and clustering coefficient in social media graph and the second factor is related to user social activities in social media sites. Most centralities focused on node social status without considering node behavior. This paper analyzes the role of influential nodes on other users on Facebook social media site by social and behavioral characteristics of users.
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The ability of health care organizations to engage patients in health care service coproduction and value cocreation has been largely overlooked.
“However, the ability of health care organizations to engage patients in the health care service coproduction and value cocreation has been largely overlooked [22-24].”
From Improving the Theoretical Understanding Toward Patient-Driven Health Care Innovation Through Online Value Cocreation: Systematic Review (2020) · cites this paper as [22]
Citations
Cites
Gephi: An Open Source Software for Exploring and Manipulating Networks
Bastian, M., Heymann, S., & Jacomy, M. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13937 · Cited by 11,675
Identification of influential spreaders in complex networks
Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, E., & Makse, H. A. (2010) · Nature Physics · doi:10.1038/nphys1746 · Cited by 3,265
Nadkarni, A., & Hofmann, S. G. (2012) · Personality and Individual Differences · doi:10.1016/j.paid.2011.11.007 · Cited by 1,276
Social structure of Facebook networks
Traud, A. L., Mucha, P. J., & Porter, M. A. (2011) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2011.12.021 · Cited by 728
Betweenness centrality as an indicator of the interdisciplinarity of scientific journals
Loet Leydesdorff, A. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20614 · Cited by 624
Studying Facebook via data extraction
Rieder, B. (2013) · ACM Web Science Conference (WebSci) · doi:10.1145/2464464.2464475 · Cited by 445
Betweenness centrality measures for directed graphs
White, D. R., & Borgatti, S. P. (1994) · Social Networks · doi:10.1016/0378-8733(94)90015-9 · Cited by 320
A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship
Li, Y.-M., Wu, C.-T., & Lai, C.-Y. (2013) · Decision Support Systems · doi:10.1016/j.dss.2013.02.009 · Cited by 239
Ranking the spreading ability of nodes in complex networks based on local structure
Gao, S., Ma, J., Chen, Z., Wang, G., & Xing, C. (2014) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2014.02.032 · Cited by 208
A Survey of Models and Algorithms for Social Influence Analysis
Sun, J., & Tang, J. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8462-3_7 · Cited by 207
Li, Y., Chen, W., Wang, Y., & Zhang, Z.-L. (2013) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2433396.2433478 · Cited by 202
Extracting influential nodes on a social network for information diffusion
Kimura, M., Saito, K., Nakano, R., et al. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-009-0150-5 · Cited by 195
Survey on Social Community Detection
Plantié, M., & Crampes, M. (2012) · Computer communications and networks · doi:10.1007/978-1-4471-4555-4_4 · Cited by 169
Representation Learning for Information Diffusion through Social Networks
Bourigault, S., Lamprier, S., & Gallinari, P. (2016) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2835776.2835817 · Cited by 139
Maximizing the Spread of Positive Influence in Online Social Networks
Zhang, H., Dinh, T. N., & Thai, M. T. (2013) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2013.37 · Cited by 126
How to run a campaign: Optimal control of SIS and SIR information epidemics
Kandhway, K., & Kuri, J. (2014) · Applied Mathematics and Computation · doi:10.1016/j.amc.2013.12.164 · Cited by 121
Inferring user interests in the Twitter social network
Bhattacharya, P., Zafar, M. B., Ganguly, N., Ghosh, S., & Gummadi, K. P. (2014) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2645710.2645765 · Cited by 118
Identification of multi-spreader users in social networks for viral marketing
Sheikhahmadi, A., & Nematbakhsh, M. A. (2016) · Journal of Information Science · doi:10.1177/0165551516644171 · Cited by 117
Self-citationInfluential nodes ranking in complex networks: An entropy-based approach
Zareie, A., Sheikhahmadi, A., & Fatemi, A. (2017) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2017.09.010 · Cited by 107
Self-citationImproving detection of influential nodes in complex networks
Sheikhahmadi, A., Nematbakhsh, M. A., & Shokrollahi, A. (2015) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2015.04.035 · Cited by 90
Self-citationSaito, K., Kimura, M., Ohara, K., et al. (2015) · Information Sciences · doi:10.1016/j.ins.2015.03.034 · Cited by 86
Using Node Centrality and Optimal Control to Maximize Information Diffusion in Social Networks
Kandhway, K., & Kuri, J. (2016) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2016.2531690 · Cited by 77
Using Node Centrality and Optimal Control to Maximize Information Diffusion in Social Networks
Kandhway, K., & Kuri, J. (2017) · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) · Cited by 74
Identification of influential users by neighbors in online social networks
Sheikhahmadi, A., Nematbakhsh, M. A., & Zareie, A. (2017) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.05.098 · Cited by 71
Self-citationOptimal control of information epidemics modeled as Maki Thompson rumors
Kandhway, K., & Kuri, J. (2014) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2014.04.022 · Cited by 68
Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach
Michalski, R., Kajdanowicz, T. J., Bródka, P., & Kazienko, P. (2014) · New Generation Computing · doi:10.1007/s00354-014-0402-9 · Cited by 59
Degree Centrality for Social Network with Opsahl Method
Yustiawan, Y., Maharani, W., & Gozali, A. A. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.07.559 · Cited by 56
Identification of influential nodes from social networks based on Enhanced Degree Centrality Measure
Srinivas, A., & Velusamy, R. L. (2015) · IEEE International Advance Computing Conference · doi:10.1109/iadcc.2015.7154889 · Cited by 50
Combining fuzzy logic and eigenvector centrality measure in social network analysis
Parand, F., Rahimi, H., & Gorzin, M. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2016.03.079 · Cited by 47
Influence maximization in social networks under an independent cascade-based model
Wang, Q., Jin, Y., Lin, Z., et al. (2015) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2015.10.020 · Cited by 45
Campaigning in Heterogeneous Social Networks: Optimal Control of SI Information Epidemics
Kandhway, K., & Joy Kuri, B. (2014) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2014.2361801 · Cited by 45
Optimal control of information epidemics
Karnik, A. U., & Dayama, P. S. (2012) · doi:10.1109/comsnets.2012.6151316 · Cited by 37
Latency-Bounded Minimum Influential Node Selection in Social Networks
Zou, F., Zhang, Z., & Wu, W. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03417-6_51 · Cited by 35
A survey on semantic Web and big data technologies for social network analysis
Kulcu, S., Doğdu, E., & Özbayoğlu, A. M. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840792 · Cited by 29
Degree centrality, eigenvector centrality and the relation between them in Twitter
Howlader, P., & Sudeep, K. S. (2016) · IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology · doi:10.1109/rteict.2016.7807909 · Cited by 26
A survey of social network and information dissemination analysis
Fang, B., Jia, Y., Han, Y., Li, S., & Zhou, B. (2014) · Chinese Science Bulletin · doi:10.1007/s11434-014-0368-5 · Cited by 24
Content marketing through data mining on Facebook social network
Forouzandeh, S., Soltanpanah, H., & Sheikhahmadi, A. (2014) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 24
Self-citationDistributed estimation of closeness centrality
Wang, W., & Tang, C. Y. (2015) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.2015.7402978 · Cited by 20
Community analysis of influential nodes for information diffusion on a social network
Kimura, M., Yamakawa, K., Saito, K., et al. (2008) · IEEE International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4633974 · Cited by 19
Estimating the degree centrality ranking
Saxena, A., Malik, V., & Iyengar, S. R. S. (2016) · doi:10.1109/comsnets.2016.7440022 · Cited by 18
Application of Data Mining in Designing a Recommender System on Social Networks
Forouzandeh, S., Soltanpanah, H., & Sheikhahmadi, A. (2015) · International Journal of Computer Applications · doi:10.5120/ijca2015905313 · Cited by 18
Self-citationKas, M., Carley, K. M., & Carley, L. R. (2014) · Social Network Analysis and Mining · doi:10.1007/s13278-014-0235-z · Cited by 17
Efficient Anytime Anywhere Algorithms for Closeness Centrality in Large and Dynamic Graphs
Santos, E. E., Korah, J., Murugappan, V., et al. (2016) · IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) · doi:10.1109/ipdpsw.2016.215 · Cited by 15
Bhandari, A., Gupta, A., & Das, D. (2017) · International Conference on Software and Computer Applications (ICSCA) · doi:10.1145/3056662.3056673 · Cited by 9
A learning-based model for predicting information diffusion in social networks: Case of Twitter
Hoang, B.-T., Chelghoum, K., & Kacem, I. (2016) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit.2016.7593657 · Cited by 8
A survey on influence spreader identification in online social network
Kumaran, P., & Chitrakala, S. (2016) · International Conference on Information Communication and Embedded Systems · doi:10.1109/icices.2016.7518924 · Cited by 6
Ranking Node Influence in Social Networks
Chen, Z., Liu, Y., & Zhu, W. (2016) · International Symposium on Parallel and Distributed Computing · doi:10.1109/ispdc.2016.47 · Cited by 5
Kumaran, P., & Chitrakala, S. (2016) · doi:10.1109/icctide.2016.7725363 · Cited by 4
Marketing based on user behavior on Facebook social network through recommender system design
Zamenjani, B. S. S. (2015) · Journal of Communications Technology Electronics and Computer Science · doi:10.22385/jctecs.v3i0.45 · Cited by 2
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- 2016, Trusted Social Node: Evaluating the Effect of Trust and Trust Variance to Maximize Social Influence in a Multilevel Social Node Influential Diffusion Model, 530
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Rezaei Aghdam, A., Watson, J., Cliff, C., & Miah, S. J. (2020) · Journal of Medical Internet Research · doi:10.2196/16324 · Cited by 57
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Metadata
- Authors
- Forouzandeh, S., Sheikhahmadi, A., Rezaei Aghdam, A., & Xu, S.
- Affiliations
- University of Applied Science and Technology (IR)
- Islamic Azad University Sanandaj Branch (IR)
- University of Tasmania (AU)
- Venue
- International Journal of Web Information Systems
- Publisher
- Emerald
- Published
- 2018-06-18
- Volume
- 14
- Issue
- 2
- Pages
- 158-176
- ISSN
- 1744-0084 (ISSN-L)
- OpenAlex
- W2801698662
- Cited by
- 33
- References
- 49
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 8 · 2023: 10 · 2022: 4 · 2021: 3 · 2020: 3 · 2019: 2
- FWCI
- 1.02
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- International Journal of Web Information Systems (publishedVersion)
- eCite Digital Repository (University of Tasmania) (submittedVersion)
- UTAS Research Repository (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Social Sciences › Communication › Social Media and Politics
- Keywords
- network centrality, influential users, Facebook, social media influence, SIR model, user behavior, node degree, clustering coefficient, social network analysis
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