Network psychometrics and cognitive network science open new ways for detecting, understanding and tackling the complexity of math anxiety: A review

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   The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools

   David Camacho, Àngel Panizo-LLedot, Gema Bello-Orgaz et al. · 2020 · Réseaux sociaux et d’information · Résumé IA disponible

   Résumé scientifique

   Social network based applications have experienced exponential growth in recent years. One of the reasons for this rise is that this application domain offers a particularly fertile place to test and develop the most advanced computational techniques to extract valuable information from the Web. The main contribution of this work is three-fold: (1) we provide an up-to-date literature review of the state of the art on social network analysis (SNA);(2) we propose a set of new metrics based on four essential features (or dimensions) in SNA; (3) finally, we provide a quantitative analysis of a set of popular SNA tools and frameworks. We have also performed a scientometric study to detect the mos…

2. Apparenté Pas pertinent

   The Massey's method for sport rating: a network science perspective

   Massimo Franceschet, Enrico Bozzo · 2017 · Réseaux sociaux et d’information · Pas encore résumé

   Résumé scientifique

   We revisit the Massey's method for rating and ranking in sports and contextualize it as a general centrality measure in network science.

3. Apparenté Pas pertinent

   A Unified Community Detection, Visualization and Analysis method

   Michel Crampes, Michel Plantié · 2013 · Réseaux sociaux et d’information · Résumé IA disponible

   Résumé scientifique

   Community detection in social graphs has attracted researchers' interest for a long time. With the widespread of social networks on the Internet it has recently become an important research domain. Most contributions focus upon the definition of algorithms for optimizing the so-called modularity function. In the first place interest was limited to unipartite graph inputs and partitioned community outputs. Recently bipartite graphs, directed graphs and overlapping communities have been investigated. Few contributions embrace at the same time the three types of nodes. In this paper we present a method which unifies commmunity detection for the three types of nodes and at the same time merges p…

4. Apparenté Pas pertinent

   The State of the Art in Multilayer Network Visualization

   Mohammad Ghoniem, Fintan Mcgee, Guy Melançon et al. · 2019 · Réseaux sociaux et d’information · Résumé IA disponible

   Résumé scientifique

   Modelling relationships between entities in real-world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real-world datasets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization there are many systems which visualize datasets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary mult…

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   Production Networks Resilience: Cascading Failures, Power Laws and Optimal Interventions

   Marios Papachristou, M. Amin Rahimian · 2023 · Réseaux sociaux et d’information · Résumé IA disponible

   Résumé scientifique

   In this paper, we study the severity of cascading failures in supply chain networks defined by a node percolation process corresponding to product suppliers failing independently due to systemic shocks. We first show that the size of the cascades follows a power law in random directed acyclic graphs, whose topology encodes the natural ordering of products from simple raw materials to complex products. This motivates the need for a supply chain resilience metric, which we define as the maximum magnitude shock that the production network can withstand such that at least $(1 - \varepsilon)$-fraction of the products are produced with high probability as the size of the production network grows t…

6. Apparenté Pas pertinent

   Mental Health Pandemic during the COVID-19 Outbreak: Social Media as a Window to Public Mental Health

   Michelle Bak, Chungyi Chiu, Jessie Chin · 2022 · Réseaux sociaux et d’information · Résumé IA disponible

   Résumé scientifique

   Intensified preventive measures during the COVID-19 pandemic, such as lockdown and social distancing, heavily increased the perception of social isolation (i.e., a discrepancy between one's social needs and the provisions of the social environment) among young adults. Social isolation is closely associated with situational loneliness (i.e., loneliness emerging from environmental change), a risk factor for depressive symptoms. Prior research suggested vulnerable young adults are likely to seek support from an online social platform such as Reddit, a perceived comfortable environment for lonely individuals to seek mental health help through anonymous communication with a broad social network. …

7. Apparenté Pas pertinent

   Predicting soccer matches with complex networks and machine learning

   Eduardo Alves Baratela, Felipe Jordão Xavier, Thomas Peron et al. · 2024 · Réseaux sociaux et d’information · Résumé IA disponible

   Résumé scientifique

   Soccer attracts the attention of many researchers and professionals in the sports industry. Therefore, the incorporation of science into the sport is constantly growing, with increasing investments in performance analysis and sports prediction industries. This study aims to (i) highlight the use of complex networks as an alternative tool for predicting soccer match outcomes, and (ii) show how the combination of structural analysis of passing networks with match statistical data can provide deeper insights into the game patterns and strategies used by teams. In order to do so, complex network metrics and match statistics were used to build machine learning models that predict the wins and los…

8. Apparenté Pas pertinent

   STUDY: Socially Aware Temporally Casual Decoder Recommender Systems

   Eltayeb Ahmed, Diana Mincu, Lauren Harrell et al. · 2023 · Réseaux sociaux et d’information · Résumé IA disponible

   Résumé scientifique

   With the overwhelming amount of data available both on and offline today, recommender systems have become much needed to help users find items tailored to their interests. When social network information exists there are methods that utilize this information to make better recommendations, however the methods are often clunky with complex architectures and training procedures. Furthermore many of the existing methods utilize graph neural networks which are notoriously difficult to train. To address this, we propose Socially-aware Temporally caUsal Decoder recommender sYstems (STUDY). STUDY does joint inference over groups of users who are adjacent in the social network graph using a single f…