Τίτλος: Distributed Community Detection Methodologies for Large Scale Social Networks
Ομιλητής: Πισπιρίγκος Γεώργιος, Υποψήφιος Διδάκτορας, ΤΜΗΥΠ, ΠΠ
Ημερομηνία-χώρος: Παρασκευή 18 Ιουνίου, 3-5μμ
Περίληψη: Nowadays, the amount of digitally available information has tremendously grown, with real-world data graphs outreaching the billion vertices. Focusing on social media, which usage is universally spread, information networks can be deemed as the predominant data structure due to their handily compact representation. Hence, graph analysis techniques have been broadly adopted due to their inherent efficiency in handling hierarchical data. Specifically, one of the most thoroughly studied graph partitioning problems is community detection that aim to form groups of vertices according to a well-defined similarity measure due toits apparent abstraction. With extensive application in a wide range of scientific sectors -e.g. bio-informatics, sociology, nonlinear dynamics, digital marketing, computer science, etc.;- where the concept of retrieving the subjacent hierarchical structure from complex information networks is a major concern, the significance of community detection is strongly underlined. Despite the impressive amount of research that has yet been published to tackle this NP-hard class problem, the existing algorithms have practically been proven not only inefficient but mainly unscalable in case of contemporary social media graphs, for which polynomial solutions are prohibitive. In this regard, the purpose of this seminar is the presentation of distributed community detection methodologies that are capable of efficiently extracting the subjacent community hierarchy of any given social graph in spite of its size and density.
This presentation is based on the following research publications:
• Makris, C.; Pettas, D.; Pispirigos, G. Distributed Community Prediction for Social Graphs Based on Louvain Algorithm. In IFIP International Conference on Artificial Intelligence Applications and Innovations; Springer: Cham, Switzerland, 2019; pp. 500–511. DOI: 10.1007/978-3-030-19823-7_42
• Makris, C.; Pispirigos, G.; Rizos, I.O. A Distributed Bagging Ensemble Methodology for Community Prediction in Social Networks. Information 2020, 11, 199. https://doi.org/10.3390/info11040199
• Makris, C.; Pispirigos, G. Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks. Big Data Cogn. Comput. 2021, 5, 14. https://doi.org/10.3390/bdcc5010014
• Konstantinos Georgiou, Christos Makris, Georgios Pispirigos: A Distributed Hybrid Community Detection Methodology for Social Networks. Algorithms 12(8): 175 (2019). https://doi.org/10.3390/a12080175
• Eleanna Kafeza, Andreas Kanavos, Christos Makris, Georgios Pispirigos, Pantelis Vikatos: T-PCCE: Twitter Personality based Communicative Communities Extraction System for Big Data. IEEE Trans. Knowl. Data Eng. 32(8): 1625-1638 (2020). DOI: 10.1109/TKDE.2019.2906197
• Makris, C.; Pispirigos, G.; Simos, M.A. Text Semantic Annotation: A Distributed Methodology Based on Community Coherence. Algorithms 2020, 13, 160. https://doi.org/10.3390/a13070160
Σχετικά με τον ομιλητή: Georgios Pispirigos is an experienced big data software engineer, born in Patras, Achaia, Greece in 1986. He received his Diploma and his master degree in Computer Science and Engineering in 2011 and 2015 respectively, from the department of Computer Engineering and Informatics (CEID), University of Patras, where he currently is a PhD student under Associate Prof. Christos Makris supervision. His research interests span the broad areas of distributed processing, information retrieval, data mining, graph mining and machine learning.