Information Cascades in Professional Networks: A Graph-Based Study of LinkedIn Post Engagement
DOI:
https://doi.org/10.30871/jaic.v9i4.10212Keywords:
Cascades, Professional Networks, Linkedin, Knowledge Transfer, Network Analysis, Academic-Industry , CollaborationAbstract
Information cascades in professional networks represent a critical mechanism for knowledge transfer and career development, yet their dynamics remain poorly understood. This study presents a comprehensive empirical analysis of information cascades in LinkedIn professional networks, focusing on computer science professionals and academic-industry knowledge transfer. We analysed 50,000 CS professionals, 500,000 connections, and 100,000 technical posts over 12 months using a Modified Independent Cascade Model that incorporates professional context factors. Our analysis reveals that hybrid professionals, representing only 25% of the network, account for 52% of inter-cluster connections and achieve 2.8× higher cross-domain transfer rates. Educational content demonstrates superior cross-domain appeal (0.47) compared to research papers (0.23), with optimal posting windows between 10 AM-12 PM achieving 23% higher cross-domain engagement. Bridge users in academic-industry transitions show significantly higher transfer effectiveness (Cohen's d = 1.47, p < 0.001). These findings provide evidence-based strategies for optimising professional networking and knowledge dissemination across academic and industry domains
Downloads
References
[1] S. Bond, "Low-cost, high-impact altruistic punishment promotes cooperation cascades in human social networks," Scientific Reports, vol. 9, no. 1, 2019, doi: 10.1038/s41598-018-38323-7.
[2] S. Dutta et al., "Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals," 2021, doi: 10.48550/arxiv.2106.07012.
[3] S. Gao et al., "General Threshold Model for Social Cascades," in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, doi: 10.1145/2940716.2940778.
[4] M. Hu et al., "Recovery of infrastructure networks after localised attacks," Scientific Reports, vol. 6, no. 1, 2016, doi: 10.1038/srep24522.
[5] M. Kamil, "User Experience Analysis of LinkedIn Social Media Using Usability Metric for User Experience (UMUX)," Journal of Information Engineering and Educational Technology, vol. 7, no. 2, pp. 78-82, 2023, doi: 10.26740/jieet.v7n2.p78-82.
[6] T. Kobayashi, "Trend-driven information cascades on random networks," Physical Review E, vol. 92, no. 6, 2015, doi: 10.1103/physreve.92.062823.
[7] D. Kornbluth et al., "Network overload due to massive attacks," Physical Review E, vol. 97, no. 5, 2018, doi: 10.1103/physreve.97.052309.
[8] Y. Li et al., "How do social network sites support product users' knowledge construction? A study of LinkedIn," Online Information Review, vol. 42, no. 7, 2018, doi: 10.1108/oir-04-2017-0133.
[9] C. Nishioka, "Cascading behaviour of an extended Watts model on networks," 2021, doi: 10.48550/arxiv.2112.10524.
[10] C. Shao and J. Wang, "Asymmetric Game Dynamics in Complex Networks against Cascading Failures," in Proc. 2015 Int. Conf. Management Engineering, Information and Control, 2015, doi: 10.2991/meic-15.2015.146.
[11] A. Smolyak et al., "Mitigation of cascading failures in complex networks," Scientific Reports, vol. 10, no. 1, 2020, doi: 10.1038/s41598-020-72771-4.
[12] Wang et al., "Topological Recurrent Neural Network for Diffusion Prediction," in Proc. 2017 IEEE Int. Conf. Data Mining (ICDM), 2017, doi: 10.1109/icdm.2017.57.
[13] W. Wei and L. Bo, "Persistent Threshold Dynamics with Recovery in Complex Networks," 2019, doi: 10.48550/arxiv.1905.08358.
[14] D. Witthaut and M. Timme, "Nonlocal effects and countermeasures in cascading failures," Physical Review E, vol. 92, no. 3, 2015, doi: 10.1103/physreve.92.032809.
[15] W. Wu et al., "Full-scale Cascade Dynamics Prediction with a Local-First Approach," 2015, doi: 10.48550/arxiv.1512.08455.
[16] J. Yang and A. E. Motter, "Cascading Failures as Continuous Phase-Space Transitions," Physical Review Letters, vol. 119, no. 24, 2017, doi: 10.1103/physrevlett.119.248302.
[17] J. Yang et al., "Small vulnerable sets determine large network cascades in power grids," Science, vol. 358, no. 6365, 2017, doi: 10.1126/science.aan3184.
[18] J. Yang et al., "Vulnerability and Cosusceptibility Determine the Size of Network Cascades," Physical Review Letters, vol. 118, no. 4, 2017, doi: 10.1103/physrevlett.118.048301.
[19] H. Yin et al., "Model and Analyse the Cascading Failure of Scale-Free Network Considering the Selective Forwarding Attack," IEEE Access, vol. 9, 2021, doi: 10.1109/access.2021.3063928.
[20] H. Yu et al., "From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics," in Proc. 2015 IEEE Int. Conf. Data Mining, 2015, doi: 10.1109/icdm.2015.79.
[21] Y. Zhang et al., "Cascading failures in interdependent systems under a flow redistribution model," Physical Review E, vol. 97, no. 2, 2018, doi: 10.1103/physreve.97.022307.
[22] Y. Zhang et al., "The Cascade Effect of Collaborative Innovation in Infrastructure Project Networks," Journal of Civil Engineering and Management, vol. 27, no. 4, 2021, doi: 10.3846/jcem.2021.14525.
[23] L. Zhao et al., "Spatio-temporal propagation of cascading overload failures in spatially embedded networks," Nature Communications, vol. 7, no. 1, 2016, doi: 10.1038/ncomms10094.
[24] L. Zhong et al., "Predicting the cascading dynamics in complex networks via the bimodal failure size distribution," 2022, doi: 8550/arxiv.2208.06726.
[25] S. Zhou et al., "A new model of network cascading failures with dependent nodes," in Proc. 2015 Annual Reliability and Maintainability Symposium (RAMS), 2015, doi: 10.1109/rams.2015.7105077.
[26] M. B. Mutanga, O. Ureke, and T. Chani, "Social media and the COVID-19: South African and Zimbabwean netizens' response to a pandemic," Indonesian Journal of Information Systems, vol. 4, no. 1, pp. 1-14, 2021.
[27] M. B. Mutanga and A. Abayomi, "Tweeting on COVID-19 pandemic in South Africa: LDA-based topic modelling approach," African Journal of Science, Technology, Innovation and Development, vol. 14, no. 1, pp. 163-172, 2022.
[28] T. Chani, O. Olugbara, and B. Mutanga, "The problem of data extraction in social media: A theoretical framework," Journal of Information Systems and Informatics, vol. 5, no. 4, pp. 1363-1384, 2023.
[29] D. A. Egbe, A. O. Akingbesote, M. O. Adigun, and M. B. Mutanga, "Context based service discovery algorithm for ad hoc mobile cloud," in Proc. 2016 Int. Conf. Industrial Informatics and Computer Systems (CIICS), 2016, pp. 1-6.
[30] T. M. Fagbola, A. Abayomi, M. B. Mutanga, and V. Jugoo, "Lexicon-based sentiment analysis and emotion classification of climate change related tweets," in Proc. Int. Conf. Soft Computing and Pattern Recognition, 2021, pp. 637-646.
[31] Z. Revesai, B. Tungwa, T. A. Chisosa, and V. R. Meki, "Digital Empowerment in Social Work: Leveraging AI to Enhance Educational Access in Developing Nations," IJIE (Indonesian Journal of Informatics Education), vol. 8, no. 2, pp. 79-89.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Zvinodashe Revesai, Murimo Bethel Mutanga, Tarirai Chani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
