106,436 research outputs found

    Cooperative Game Theory Approaches for Network Partitioning

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    International audienceThe 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 suggest two approaches from cooperative game theory: the first approach is based on the Myerson value, whereas the second approach is based on hedonic games. Both approaches allow to detect clusters with various resolution. However, the tuning of the resolution parameter in the hedonic games approach is particularly intuitive. Furthermore, the modularity based approach and its generalizations can be viewed as particular cases of the hedonic games

    Network partitioning algorithms as cooperative games

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    International audienceThe 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 suggest two approaches from cooperative game theory: the first approach is based on the Myerson value, whereas the second approach is based on hedonic games. Both approaches allow to detect clusters with various resolutions. However, the tuning of the resolution parameter in the hedonic games approach is particularly intuitive. Furthermore, the modularity-based approach and its generalizations as well as ratio cut and normalized cut methods can be viewed as particular cases of the hedonic games. Finally, for approaches based on potential hedonic games we suggest a very efficient computational scheme using Gibbs sampling

    The Bullying Game: Sexism Based Toxic Language Analysis on Online Games Chat Logs by Text Mining

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    As a unique type of social network, the online gaming industry is a fast-growing, changing, and men-dominated field which attracts diverse backgrounds. Being dominated by male users, game developers, game players, game investors, the non-inclusiveness and gender inequality reside as salient problems in the community. In the online gaming communities, most women players report toxic and offensive language or experiences of verbal abuse. Symbolic interactionists and feminists assume that words matter since the use of particular language and terms can dehumanize and harm particular groups such as women. Identifying and reporting the toxic behavior, sexism, and harassment that occur in online games is a critical need in preventing cyberbullying, and it will help gender diversity and equality grow in the online gaming industry. However, the research on this topic is still rare, except for some milestone studies. This paper aims to contribute to the theory and practice of sexist toxic language detection in the online gaming community, through the automatic detection and analysis of toxic comments in online games chat logs. We adopted the MaXQDA tool as a data visualization technique to reveal the most frequently used toxic words used against women in online gaming communities. We also applied the Naïve Bayes Classifier for text mining to classify if a chat log content is sexist and toxic. We also refined the text mining model Laplace estimator and re-tested the model’s accuracy. The study also revealed that the accuracy of the Naïve Bayes Classifier did not change by the Laplace estimator. The findings of the study are expected to raise awareness about the use of gender-based toxic language in the online gaming community. Moreover, the proposed mining model can inspire similar research on practical tools to help moderate the use of sexist toxic language and disinfect these communities from gender-based discrimination and sexist bullying

    Detection of Deception in a Virtual World

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    This work explores the role of multimodal cues in detection of deception in a virtual world, an online community of World of Warcraft players. Case studies from a five-year ethnography are presented in three categories: small-scale deception in text, deception by avoidance, and large-scale deception in game-external modes. Each case study is analyzed in terms of how the affordances of the medium enabled or hampered deception as well as how the members of the community ultimately detected the deception. The ramifications of deception on the community are discussed, as well as the need for researchers to have a deep community knowledge when attempting to understand the role of deception in a complex society. Finally, recommendations are given for assessment of behavior in virtual worlds and the unique considerations that investigators must give to the rules and procedures of online communities.</jats:p
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