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The Use of Community Detection Algorithms to the Graphs of Interactive Objects

Student: Lobanova Sofiya

Supervisor: Alexander Chepovskiy

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Applied Informatics (Bachelor)

Year of Graduation: 2017

Today there are tons of real-life complex networks all around us, so it is getting vital to understand network nature in its core for further benefits extraction. In terms of complex network analysis, community detection is rather timely and definitely expedient brunch to study. A great deal of community detection methods, metrics and other studies are available at the moment. However, they may yield poor results in general case due to their specific restrictions and assumptions. The paper dwells on several field essentials and then deals with the new framework that merge a couple of strikingly different methods. These methods are well-known hierarchical clustering technique, proposed by Radicchi and Palla’s Clique Percolation Method. The new method also offers a number of terminal options designed to possibly refine the resulting community structure. Different methods were examined for diverse networks to reveal specific patterns both for methods and for graphs. The results were compared and a number of meaningful insights were depicted. The method outperformed some classical approaches.

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