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Intelligent Data Engineering and Automated Learning – IDEAL 2020/ 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part II
21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part II
- Editors
- (view affiliations)
- Cesar Analide
- Paulo Novais
- David Camacho
- Hujun Yin
Conference proceedings IDEAL 2020
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 for both, the graph structure and node features. In this way, we obtain a new clustering criterion and a corresponding algorithm for finding clusters/communities one-by-one. We show that our proposed method is effective on real-world data, as well as on synthetic data involving either only quantitative features or only categorical attributes or both. Our algorithm appears competitive against state-of-the-art algorithms.
We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. This generalization lifts the set to a “head subject” in the higher ranks of the taxonomy, that is supposed to “tightly” cover the query set, possibly bringing in some errors, both “gaps” and “offshoots”. Our hybrid method involves two more automated analysis techniques: a fuzzy clustering method, FADDIS, involving both additive and spectral properties, and a purely structural string-to-text relevance measure based on suffix trees annotated by frequencies. We apply this to extract research tendencies from two collections of research papers: (a) about 18000 research papers published in Springer journals on data science for 20 years, and (b) about 27000 research papers retrieved from Springer and Elsevier journals in response to data science related queries. We consider a taxonomy of Data Science based on the Association for Computing Machinery Classification of Computing System (ACM-CCS 2012). Our findings allow us to make some comments on the tendencies of research that cannot be derived by using more conventional techniques.
ASONAM '20: International Conference on Advances in Social Networks Analysis and Mining, 7-10 December 2020, The Hague, Netherlands (Virtual).
Features of political communication in Russian-speaking segment of the Facebook network are analyzed in the article. Research scope consisted from politically active groups (N 200) and actors (N 291) for the period of January-May, 2014. The research methodology included interdisciplinary analysis. Actors were classified by politic opinions into mainstream, oppositional and nationalist clusters. Following types of the actors were identified based on network activity: designers of communication space, manipulators, graphomaniacs and local cluster authors. Communicative and speech behavior of the actors was also analyzed. Using formal cluster analysis it was shown that specific virtual identities are shaped in Facebook political communities. Virtual identities demonstrate distinguished communicative and speech behavior using different speech strategy and tactics and multimedia rhetorical resources. Today the analysis of communication processes in politically active communities in the network environment is of great importance as the virtual sphere becomes more and more significant for achieve of various political aims both in Russia and around the world.
Trading processes is a vital part of human life and any unstable situation results in the change of living conditions of individuals. We study the power of each country in terms of produce trade. Trade relations between countries are represented as a network, where vertices are territories and edges are export flows. As flows of products between participants are heterogeneous we consider various groups of substitute goods (cereals, fish, vegetables). We detect key participants affecting food retail with the use of classical centrality measures. We also perform clustering procedure in order to find communities in networks.
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 allow a relaxation of the criterion for finding communities one-by-one. We show that our proposed method is effective on real-world data, as well as on synthetic data involving either only quantitative features or only categorical attributes or both. In the cases at which attributes are categorical, state-of-the-art algorithms are available. Our algorithm appears competitive against them.
[EN] Introduction. Features of political communication in Russian-speaking segment of the Facebook network are analyzed in the article. According to researchers unlike their counterparts in the U.S. and elsewhere, Russian bloggers prefer platforms that combine features typical of blogs with features of social network services like Facebook. Objectives. The objectives were: classification of actors in the political groups in the Russian-speaking segment of Facebook and analysis of their sociolinguistic behavior. Method. The interdisciplinary analysis based on scope of politically active groups (N 200) and actors (N 291) during January – May, 2014. Results. Actors were classified by politic opinions into mainstream, oppositional and nationalist Clusters and based on network activity into designers of communication space, manipulators, graphomaniacs and local cluster authors. Their communicative and speech behavior was also analyzed and shown shaped specific virtual identities, which demonstrate distinguished sociolinguistic behavior. Discussion and Conclusion. Today the analysis of communication processes in politically active communities in the network environment is of great importance as the virtual sphere becomes more and more significant for achieve of various political aims both in Russia and around the world. The prospect of an actual study is to identify the ratio of online and off-line communication activity of actors in the political sphere in the Russian segment of Facebook.
This book constitutes the proceedings of the 5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016, held in Yekaterinburg, Russia, in April 2016. The 23 full papers, 7 short papers, and 3 industrial papers were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections on machine learning and data analysis; social networks; natural language processing; analysis of images and video.