Detection of Influential Individuals, Communities, and Link Prediction in Social Networks
| Thesis title in Czech: | Detekce významných uzlů, komunit a predikce linků v sociálních sítích |
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| Thesis title in English: | Detection of Influential Individuals, Communities, and Link Prediction in Social Networks |
| Key words: | dobývání znalostí|sociální sítě|detekce významných uzlů|detekce komunit|predikce linků|reprezentace znalostí |
| English key words: | data mining|social networks|detection of influential individuals|community detection|link prediction|knowledge representation |
| Academic year of topic announcement: | 2021/2022 |
| Thesis type: | Bachelor's thesis |
| Thesis language: | angličtina |
| Department: | Department of Theoretical Computer Science and Mathematical Logic (32-KTIML) |
| Supervisor: | doc. RNDr. Iveta Mrázová, CSc. |
| Author: | Bc. Matúš König - assigned and confirmed by the Study Dept. |
| Date of registration: | 19.05.2022 |
| Date of assignment: | 26.05.2022 |
| Confirmed by Study dept. on: | 03.06.2022 |
| Date and time of defence: | 29.06.2023 09:00 |
| Date of electronic submission: | 10.05.2023 |
| Date of submission of printed version: | 10.05.2023 |
| Date of proceeded defence: | 29.06.2023 |
| Opponents: | RNDr. Jan Hric |
| Guidelines |
| The student shall review the following topics in his bachelor thesis:
- overview of known paradigms relevant to the detection of influential individuals, detection of communities, and feature-based link prediction in graph-based models (PageRank, HITS, betweenness, modularity, Jaccard coefficients, Adamic/Adar scores, Katz measure, etc.), - recapitulation and mutual comparison of the techniques applicable to community detection in (dynamic) social networks (e.g., Kerninghan-Lin, Girvan-Newman, Greedy modularity, Spectral clustering, and METIS, among others), The student will focus on some of these topics in more detail. Further, he shall propose a suitable strategy for finding influential individuals and communities in real-world social networks (e.g., the Zachary´s Karate Club, the Enron Email Data Set (https://www.cs.cmu.edu/~enron/) or the data obtained from the Full Movie Lens Dataset from Kaggle (https://www.kaggle.com/rounakbanik/the-movies-dataset), and he shall implement the models (application of the NetworkX library is assumed). Evaluating the obtained results and experience gained while working on the above-mentioned tasks will form an important part of the thesis. |
| References |
| 1. Některé z dostupných základních učebnic, resp. přehledových článků vhodných pro zvolené téma, např.:
- Ch. C. Aggarwal: Data Mining: The Textbook, Springer, (2015). - B. Liu: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, (2007). 2. Články, resp. kapitoly z knih: - B. Aven: The Effects of Corruption on Organizational Networks and Individual Behavior, TR / 2012, Tepper Scool of Business, Carnegie Mellon University, (July 2012), accessible at: http://repository.cmu.edu/tepper, 40p. - B. L. Aven: The Paradox of Corrupt Networks: An Analysis of Organizational Crime at Enron, Vol. 26, No. 4, Organization Science, Special Issue on the Psychology of Organizational Networks, (2015), 980-996, https://doi.org/10.1287/orsc.2015.0983, pdf available from: (https://www.researchgate.net/publication/281380921_The_Paradox_of_Corrupt_Networks_An_Analysis_of_Organizational_Crime_at_Enron). - A.-L. Barabasi: Communities (Chapter 9) from A.-L. Barabasi, M. Posfai: Network Science, Cambridge University Press, (2016), pp. 321-376. - B. Bringmann, M. Berlingerio, F. Bonchi, F., and A. Gionis: Learning and Predicting the Evolution of Social Networks, in: IEEE Intelligent Systems, pp. 26-34, July/Agust (2010), available at: http://www.francescobonchi.com/is2010.pdf - M. Al Hasan and M. J, Zaki: A Survey of Link Prediction in Social Networks (Chapter 9) from C. D. Aggarwal (Ed.): Social Network Data Analytics, Springer, (2011), https://link.springer.com/content/pdf/10.1007%2F978-1-4419-8462-3_9.pdf - J. Leskovec, D. Huttenlocher, and J. Kleinberg: Predicting Positive and Negative Links in Online Social Networks, in: Proc. of WWW 2010, NC, USA, April 26-30, (2010). - S. Parthasarathy, Y. Ruan, and V. Satuluri: Community Discovery in Social Networks: Applications, Methods, and Emerging Trends (Chapter 4) from: C. D. Aggarwal (Ed.): Social Network Data Analytics, Springer, (2011), https://link.springer.com/content/pdf/10.1007%2F978-1-4419-8462-3_4.pdf 3. Aktuální články z profilujících světových časopisů, např.: Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Machine Learning, apod. |