Dataset Information

Detection of communities with Naming Game-based methods.

ABSTRACT: Complex networks are often organized in groups or communities of agents that share the same features and/or functions, and this structural organization is built naturally with the formation of the system. In social networks, we argue that the dynamic of linguistic interactions of agreement among people can be a crucial factor in generating this community structure, given that sharing opinions with another person bounds them together, and disagreeing constantly would probably weaken the relationship. We present here a computational model of opinion exchange that uncovers the community structure of a network. Our aim is not to present a new community detection method proper, but to show how a model of social communication dynamics can reveal the (simple and overlapping) community structure in an emergent way. Our model is based on a standard Naming Game, but takes into consideration three social features: trust, uncertainty and opinion preference, that are built over time as agents communicate among themselves. We show that the separate addition of each social feature in the Naming Game results in gradual improvements with respect to community detection. In addition, the resulting uncertainty and trust values classify nodes and edges according to role and position in the network. Also, our model has shown a degree of accuracy both for non-overlapping and overlapping communities that are comparable with most algorithms specifically designed for topological community detection.

SUBMITTER: Uzun TG

PROVIDER: S-EPMC5552283 | biostudies-other | 2017

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:INTRODUCTION:High rates of mental health problems, such as suicidal behaviours, among First Nations youth in Canada are a major public health concern. The Good Behavior Game (GBG) is a school-based intervention that provides a nurturing environment for children and has been shown to promote positive outcomes. PAX Good Behavior Game (PAX GBG) is an adaptation and enhancement of the GBG. While PAX GBG has been implemented in Indigenous communities, little research exists examining the cultural and contextual appropriateness and effectiveness of the intervention in First Nations communities. METHODS AND ANALYSIS:The present paper describes a protocol of the mixed-methods approach guided by an Indigenous ethical engagement model adopted to implement, adapt and evaluate PAX GBG in First Nations communities in Manitoba, Canada. First, implementation outcomes (eg, acceptability, adoption) of PAX GBG will be evaluated using qualitative interviews with teachers, principals and community members from Swampy Cree Tribal Council (SCTC) communities. Second, by linking administrative databases to programme data from schools in 38 First Nations communities, we will compare PAX GBG and control groups to evaluate whether PAX GBG is associated with improved mental health and academic outcomes. Third, the qualitative results will help inform a cultural and contextual adaptation of PAX GBG called First Nations PAX (FN PAX). Fourth, FN PAX will be implemented in a few SCTC communities and evaluated using surveys and qualitative interviews followed by the remaining communities the subsequent year. ETHICS AND DISSEMINATION:Ethical approval was obtained from the University of Manitoba Health Research Ethics Board and will be obtained from the Health Information Privacy Committee and respective data providers for the administrative database linkages. Dissemination and knowledge translation will include community and stakeholder engagement throughout the research process, reports and presentations for policymakers and community members, presentations at scientific conferences and journal publications.

Dataset Information

Detection of communities with Naming Game-based methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure