Mean Received Resources Meet Machine Learning Algorithms to Improve Link Prediction Methods

The analysis of social networks has attracted a lot of attention during the last two decades. These networks are dynamic: new links appear and disappear. Link prediction is the problem of inferring links that will appear in the future from the actual state of the network. We use information from nodes and edges and calculate the similarity between users. The more users are similar, the higher the probability of their connection in the future will be. The similarity metrics play an important role in the link prediction field. Due to their simplicity and flexibility, many authors have proposed several metrics such as Jaccard, AA, and Katz and evaluated them using the area under the curve (AUC). In this paper, we propose a new parameterized method to enhance the AUC value of the link prediction metrics by combining them with the mean received resources (MRRs). Experiments show that the proposed method improves the performance of the state-of-the-art metrics. Moreover, we used machine learning algorithms to classify links and confirm the efficiency of the proposed combination.

Press X Governement

ABSTRACT – This paper begins with a discussion of the concept of populism in order to analyze how Jair Bolsonaro’s criticisms of the press circulated on the social network Twitter at a time when Brazil had recorded the highest number of covid-related deaths, in the first week of March 2021. This paper presupposes that the president’s support network incorporated the populist binary rhetoric of “us” against a “corrupt elite” which is responsible for conspiring and amplifying the effects of the health crisis in the country. As a methodology, we opted for a mixed proposal based on content analysis and analysis of social networks. As a result, we found that the tweets from Bolsonaro supporters claim that the press is corrupt, and manipulates and harasses the president in its coverage of the pandemic. RESUMO – O artigo parte da discussão do conceito de populismo, com o objetivo de analisar como as críticas de Jair Bolsonaro à imprensa circularam na rede social Twitter no momento em que o Brasil atingia o maior número de mortos pela covid-19, na primeira semana de março de 2021. O texto traz como hipótese que a rede de apoio ao presidente incorporou a retórica binarista do “nós” contra uma “elite corrupta” que é responsável por conspirar e ampliar os efeitos da crise sanitária no país. Como metodologia, optou-se por uma proposta mista ancorada na análise de redes sociais e análise de conteúdo. Como resultado, constatou-se que os tweets dos apoiadores de Bolsonaro associaram a imprensa atributos de corrupção, manipulação e perseguição ao líder na cobertura contra uma “elite corrupta” e atribuiu a imprensa o caráter dos efeitos da pandemia. RESUMEN - Artículo de la discusión del concepto de populismo, con el fin de analizar cómo circularon en la red social Twitter el comunicado de prensa de Jair Bolsonaro al equipo cuando Brasil alcanzó el mayor número de muertes por covid-19, en la primera semana de marzo de 2021. El texto plantea la hipótesis de que la red de apoyo al presidente incorporó la retórica del binarismo populista del “nosotros” frente a una “élite corrupta” y atribuyó a la prensa el carácter de enemigo del gobierno, responsable de conspirar y amplificar los efectos de la salud en el pais. Como metodología, optamos por una propuesta mixta anclada en el análisis de redes sociales y análisis de contenido. Como resultado, se encontró que los tuits de simpatizantes de Bolsonaro asociaron a la prensa con atributos de corrupción, manipulación y acoso al líder para cubrir los efectos de la pandemia.

A Proposal for the EI Index for Fuzzy Groups

In this paper, it is proposed a measure that quantifies the relational structure within and between groups that comprehend not only the analysis of disjoint groups or non-disjoint groups but also in fuzzy groups. This measure is based on the existing measure known as the EI index. The current EI index is a measure of homophily applied to networks with the presence of disjoint groups, although disjoint groups on a large scale rarely exist in many empirical networks. The new measure permits the expansion of the analysis of social networks, for several types of attributes, and thus generating previously untapped knowledge. Moreover, it is also proposed combining edges’ and nodes’ weights in the evaluation of the EI index. The new measure is tested in two networks in different contexts. The first one is a co-authorship network, where researchers, actors in the network, are divided according to the time of completion of the doctorate. The second network is formed by trade relations between countries of the American continent, where countries are grouped according to the Human Development Index.

Contribution of Entrepreneurship to the Social Integration of People Intellectual Disabilities: A Case Study Based on the Analysis of Social Networks

In an environment characterized by high unemployment rates among people with disabilities, the objective of the present work is to analyze entrepreneurship as a labor option which fully inserts people with intellectual disabilities (PwID) into their societies. In order to carry out this research, a case study methodology based on social network analysis has been adopted, given the nature of the variables analyzed. The results indicate that the fact of having managed to start up the company has been an important source of self-confidence and inspiration, as well as increasing and intensifying the social networks of PwID involved in the entrepreneurial project.

Dynamic Analysis of Social Networks of Learners’ Online Peers1 Interaction Behavior in Smoking Cession Education

Objectives: We investigated the dynamic changes in the impact of online peer support on individual behavior, using the online smoking cessation community as an example. Methods: This study included a longitudinal sample of 52 adolescents (aged 17-21) who volunteered to quit smoking in an online smoking cessation community. We described an actor-based model for online friendship network-smoking cessation behavior co-evolution, using social network dynamic analysis to study the interaction between learners to explore the impact of online peer support on adolescent smoking cessation behavior. Results: Three RSiena models exhibited high goodness of fit in the structural effect part (The estimated values of the three reciprocity effect parameters were 1.7067, 1.07384, and 1.07401, respectively). The estimated value of the in-degree effect parameter of smoking cessation behavior influenced by network structure was 0.0934. Conclusions: Online peer support has a significant impact on the propensity for adolescents to select offline friends. The more online peers’ support, the better results of teenagers online smoking cessation. Online peer support significantly affects the changes in individual behaviors.

Conducting Psychological Examinations for Citizens in Possession of Weapons: Strengthening State Control Over Circulation of Weapons

In the article, in connection with the introduction of amendments to the federal legislation, specific issues regarding the conducting of a psychological survey of citizens gun owners and candidates for gun ownership are considered. A battery of psychodiagnostic techniques that can be used in the course of a psychological examination (progressive matrices of J. Raven, a questionnaire of the Level of subjective control, and a method for diagnosing frustration tolerance from Rosenzweig, Minnesota Multidisciplinary Personality Questionnaire) is proposed and justified, as is an algorithm for conducting psychodiagnostic research. Furthermore, a proposal to include in the psychological examination, a psychological analysis of social networks (provided that the subject is a member of their group) has been made. The possible participation in the psychological survey gun owners and candidates for gun ownership psychologists of Rosgvardiya are justified and the specific form of their participation in the survey is indicated.

Differentially Private Attributed Network Releasing Based on Early Fusion

Vertex attributes exert huge impacts on the analysis of social networks. Since the attributes are often sensitive, it is necessary to seek effective ways to protect the privacy of graphs with correlated attributes. Prior work has focused mainly on the graph topological structure and the attributes, respectively, and combining them together by defining the relevancy between them. However, these methods need to add noise to them, respectively, and they produce a large number of required noise and reduce the data utility. In this paper, we introduce an approach to release graphs with correlated attributes under differential privacy based on early fusion. We combine the graph topological structure and the attributes together with a private probability model and generate a synthetic network satisfying differential privacy. We conduct extensive experiments to demonstrate that our approach could meet the request of attributed networks and achieve high data utility.

Peer-making: the interconnections between PhD thesis committee membership and co-publishing

the article relies on the analysis of Social Networks in order to compare the networks at work in the composition of thesis committees between 2003 and 2008 in a French provincial university in three very different disciplines – astrophysics, archaeology and economics – so as to test the hypothesis that connections actually pre-existed to graduation. Were members co-authors of scientific publications or were committees constituted only for the sake of awarding a PhD? Astrophysics and its “equipment” ethos is the one to superimpose most often committee membership and co-publishing. Archaeology falls somewhere in-between, due to the greatest scarcity of committee members. Last of the three, economics actually separates the two types of collaboration by most frequently inviting international researchers. Peer Review https://publons.com/publon/10.1162/qss_a_00143

blockmodeling: an R package for Generalized Blockmodeling

This paper presents the R package blockmodeling which is primarily meant as an implementation of generalized blockmodeling (more broadly blockmodeling) for valued networks where the values of the ties are assumed to be measured on at least interval scale. Blockmodeling is one of the most commonly used approaches in the analysis of (social) networks, which deals with the analysis of relationships or connections, between the units studied (e.g., peoples, organizations, journals etc.). The R package blockmodeling implements several approaches for the generalized blockmodeling of binary and valued networks. Generalized blockmodeling is commonly used to cluster nodes in a network with regard to the structure of their links. The theoretical foundations of generalized blockmodeling for binary and valued networks are summarized in the paper while the use of the R package blockmodeling is illustrated by applying it to an empirical dataset.