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Community Detection in Feature-Rich Networks Using Gradient Descent Approach
Ch. 15. P. 185–196.
Keywords: community detectionFeature-rich Networksgradient descent approachsteepest descent optimization
Publication based on the results of:
Shalileh S., Social Network Analysis and Mining 2025 Vol. 15212 P. 137–148
Community detection in attributed networks aims to recover clusters in which the within-community nodes are as interconnected and as homogeneous as possible, while the between-communities nodes are as disconnected and as heterogeneous as possible. The current research proposes a straightforward data-driven model with an integrated regularization term to recover communities. For further improvement of the ...
Added: November 30, 2024
Popov V., Chepovskiy A., , in: Complex Networks & Their Applications XIII, Proceedings of The Thirteenth International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2024 - Volume 3.: Springer, 2025. P. 83–90.
Added: September 20, 2024
Noskov F., Panov M., / Series arXiv "math". 2023.
Community detection is one of the most critical problems in modern network science. Its applications can be found in various fields, from protein modeling to social network analysis. Recently, many papers appeared studying the problem of overlapping community detection, where each node of a network may belong to several communities. In this work, we consider ...
Added: May 31, 2024
Alina V. Vladimirova, , in: Alternative Paths to Influence: Soft Power and International Politics.: Oxon: Routledge, 2023. Ch. 9 P. 556–571.
Soft power is one of the most influential concepts in the discipline of international relations, yet it is highly criticized for ambiguity and analytical weakness. Thus, through thoughtful literature reviews, scholars continue with clarifying and mapping the theory within a broader political power research programme. It is in this scholarly context that this article offers ...
Added: February 14, 2024
Alina V. Vladimirova, Journal of political power 2022 Vol. 15 No. 3 P. 556–571
Soft power is one of the most influential concepts in the discipline of international relations, yet it is highly criticized for ambiguity and analytical weakness. Thus, through thoughtful literature reviews, scholars continue with clarifying and mapping the theory within a broader political power research programme. It is in this scholarly context that this article offers ...
Added: February 14, 2024
Shalileh S., Mathematics 2023 Vol. 11 No. 12 Article 2617
Enhancing the effectiveness of clustering methods has always been of great interest. Therefore, inspired by the success story of the gradient descent approach in supervised learning in the current research, we proposed an effective clustering method using the gradient descent approach. As a supplementary device for further improvements, we implemented our proposed method using an ...
Added: June 9, 2023
Chepovskiy A., , in: The 6th International Conference on Complex Networks and Their Applications. Nov. 29 - Dec. 01, 2017, Lyon (France), Book of abstracts. ISBN 978-2-9557050-2-5.: Springer, 2017. P. 336–340.
One of the tasks related to the study of the of complex networks is the problem of revealing communities structure. Algorithms to split all vertices into groups, named communities, so that the vertices of each group are more closely related to each other than to the rest of the graph are widely investigated nowadays. This is so-called ...
Added: March 22, 2023
Chepovskiy A., Вопросы кибербезопасности 2023 № 1(53) С. 75–81
The purpose of the study: search for a technique for constructing and analyzing a graph of interacting objects in the network of Telegram channels, including the calculation of psycholinguistic characteristics of texts. This technique makes it possible to classify groups of channels and evaluate their informational impact on users.
Method: (U, M, R)-model is used to build a weighted ...
Added: March 6, 2023
Chepovskiy A., М.: Национальный открытый университет «ИНТУИТ», 2022.
В монографии рассмотрены различные математические модели для решения задач анализа сетей взаимодействующих объектов систем телекоммуникаций. Предназначена для разработчиков информационных систем, специалистов в области анализа данных. ...
Added: November 18, 2022
Popov V., Chepovskiy A., Труды Института системного анализа Российской академии наук 2022 Т. 72 № 4 С. 39–50
In this paper, the authors present the “Galaxies method” to reveal implicit communities on the
graph of interacting objects obtained by importing a network of channels from the Telegram messenger. This
method is based on successive identification of overlapping communities on the initial weighted graph, further
construction of a new graph, in which the vertices are the communities ...
Added: October 30, 2022
Popov V., Chepovskiy A., Вестник Новосибирского государственного университета. Серия: Информационные технологии 2022 Т. 20 № 2 С. 60–71
In this paper, an algorithm to import data from the messenger Telegram and to build weighted graphs of interacting objects is described. To import data, the given Telegram-channels are taken as a basis. Then, iteractively channels that had any of the recorded three interactions with previous ones are revealed: common external links, mentions of each ...
Added: September 2, 2022
Shalileh S., Mirkin B., Entropy 2022 Vol. 24 No. 5 Article 626
This paper proposes a meaningful and effective extension of the celebrated K-means algorithm to detect communities in feature-rich networks, due to our assumption of non-summability mode. We least-squares approximate given matrices of inter-node links and feature values, leading to a straightforward extension of the conventional K-means clustering method as an alternating minimization strategy for the ...
Added: August 1, 2022
Makarov I., Oborevich A., , in: Proceedings of IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI'21), 18-20 Nov. 2021.: NY: IEEE, 2021. P. 000127–000130.
Graph visualization is an effective and efficient way to discover complex inter-connections between elements within the nested structure of data. To accomplish this type of representation machine learning algorithms use a technique called graph embedding and node embedding in particular. However, in this paper, we will compare well-known techniques to yet largely under-explored setting of ...
Added: January 19, 2022
Shalileh S., Mirkin B., Social Network Analysis and Mining 2021 Vol. 11 No. 1 P. 1–23
A feature-rich network is a network whose nodes are characterized by categorical or quantitative features. We propose a data-driven model for finding a partition of the nodes to approximate both the network link data and the feature data. The model involves summary quantitative characteristics of both network links and features. We distinguish between two modes ...
Added: July 29, 2021
Popov V., Chepovskiy A., Вестник Новосибирского государственного университета. Серия: Информационные технологии 2021 Т. 19 № 2 С. 76–91
In this paper, the authors describe an algorithm for importing data from the social network Twitter and building weighted social graphs. To import data, the given posts are taken as a basis, users who have had any of the recorded interactions with them are downloaded. Further, the algorithm focuses on the given configuration and uses ...
Added: July 25, 2021
Shalileh S., Mirkin B., Plos One 2021 Vol. 16 No. 7 Article 0254377
We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightfor- wardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and intensity weight(s) over the network each. Our least- squares additive criterion allows ...
Added: July 22, 2021
Makarov I., Makarov M., Kiselev D., PeerJ Computer Science 2021 Vol. 7 Article e526
Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, ...
Added: March 31, 2021
Shalileh S., Mirkin B., , in: Proceedings of MARAMI 2020 - Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques - The 11th Conference on Network Modeling and Analysis(Vol-2750)Vol. Vol-2750: Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques - Network Modeling and Analysis 2020.: CEUR-WS.org, 2020. P. 1–12.
The problem of community detection in a network with features at its nodes takes into account both the graph structure and node features. The goal is to find relatively dense groups of interconnected entities sharing some features in common. Existing approaches require the number of communities pre-specified. We apply the so-called data recovery approach to ...
Added: January 13, 2021
Shalileh S., Mirkin B., , in: Complex Networks & Their Applications IX. Volume 1: Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020.: Springer, 2021. P. 3–14.
The problem of community detection in a network with features at its nodes takes into account both the graph structure and node features. The goal is to find relatively dense groups of interconnected entities sharing some features in common. Algorithms based on probabilistic community models require the node features to be categorical. We use a ...
Added: January 13, 2021