Community Detection Based on the Nodes Role in a Network: The Telegram Platform Case

K. Tikhomirova; I. Makarov

doi:10.1007/978-3-030-72610-2_22

Publications

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Community Detection Based on the Nodes Role in a Network: The Telegram Platform Case

P. 294–302.

Tikhomirova K., Makarov I.

The paper studies the community detection problem on Telegram channels. The dataset is received from TGStat service and includes the information of 58k forwards between 100 politician Telegram channels. We implement modern clustering approaches to solve the problem of missing social links. Our study is based on a combination of structural features with strategy-based attributes, including indicators designed according to the nodes’ role in a network. Authors provide ten novel indicators, which are calculated for each network’s member per each message in order to vectorize a Telegram channel with regard to its strategy of information spread and the way of contacting other channels. Authors construct a metric-based graph of channel relations and cluster channels representations using network science techniques. Obtained results are studied using quantitative and qualitative analysis showing promising results in applying joint network-based and KPI-based models for the stated problem.

Keywords: Community detection обнаружение сообществ Gatekeeping theory Clustering media Media embedding

In book

Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected Papers

Vol. 12602. , Springer, 2021.

Complex Networks & Their Applications XIII, Proceedings of The Thirteenth International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2024 - Volume 3

Springer, 2025.

Added: September 20, 2024

Delegating Issue Importance Judgments: An Experimental Test of the Agenda Cueing Hypothesis in an Online News Aggregator

Bryanov K., International Journal of Communication 2023 No. 17 P. 4284–4303

Agenda cueing is a theorized mechanism whereby news consumers form judgments of relative social issue importance based on exposure to media coverage. Agenda cueing entails taking issue importance signals from surface features of news presentations, such as the mere frequency of the topic’s coverage. This study relies on an experimental stimulus simulating an interface of a major news platform, puts ...

Added: August 17, 2023

Summable and nonsummable data‐driven models for community detection in feature‐rich networks

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

Detection of an unspecified number of communities in feature-rich networks

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

A Method for Community Detection in Networks with Mixed Scale Features at Its Nodes

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

A Data Recovery Method for Community Detection in Feature-Rich Networks

Shalileh S., Mirkin B., , in: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).: Association for Computing Machinery (ACM), 2020. P. 99–104.

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. We apply the so-called data recovery approach to the problem by combining the least-squares recovery criteria ...

Added: December 11, 2020

Complex Networks & Their Applications IX. Volume 1: Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020

Springer, 2021.

Volume 1 & 2 Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020 ...

Added: October 31, 2020

Survey on graph embeddings and their applications to machine learning problems on graphs

Makarov I., Kiselev D., Nikitinsky N. et al., PeerJ Computer Science 2021 Vol. 7 P. 1–62

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering in order to incorporate structural information into predictive model. Nowadays, a family of automated graph feature engineering techniques have been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs ...

Added: October 27, 2020

Network partitioning algorithms as cooperative games

Avrachenkov K. E., Kondratev Aleksei Y, Mazalov V. V. et al., Computational Social Networks 2018 Vol. 5 No. 11 P. 1–28

The paper is devoted to game-theoretic methods for community detection in networks. The traditional methods for detecting community structure are based on selecting dense subgraphs inside the network. Here we propose to use the methods of cooperative game theory that highlight not only the link density but also the mechanisms of cluster formation. Specifically, we ...

Added: October 30, 2018

Cooperative Game Theory Approaches for Network Partitioning

Avrachenkov K. E., Kondratev Aleksei Yu, Mazalov V. V., , in: Computing and Combinatorics. COCOON 2017. Lecture Notes in Computer Science, vol 10392.: Springer, 2017. P. 591–602.

The paper is devoted to game-theoretic methods for community detection in networks. The traditional methods for detecting community structure are based on selecting denser subgraphs inside the network. Here we propose to use the methods of cooperative game theory that highlight not only the link density but also the mechanisms of cluster formation. Specifically, we ...

Added: October 2, 2018

Multimodal Clustering for Community Detection

Ignatov D. I., Semenov A., Комиссарова Д. В. et al., , in: Formal Concept Analysis of Social Networks.: Springer, 2017. Ch. 4 P. 59–96.

Multimodal clustering is an unsupervised technique for mining interesting patterns in n-ary relations or n-mode networks. Among different types of such generalised patterns one can find biclusters and formal concepts (maximal bicliques) for two-mode case, triclusters and triconcepts for three-mode case, closed n-sets for n-mode case, etc. Object-attribute biclustering (OA-biclustering) for mining large binary datatables (formal contexts or two-mode ...

Added: December 17, 2017