scholarly journals Regular Equivalence for Social Networks

2018 ◽  
Vol 9 (1) ◽  
pp. 117 ◽  
Author(s):  
Pieter Audenaert ◽  
Didier Colle ◽  
Mario Pickavet
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Efficient Algorithm ◽  
Large Scale ◽  
Real Life ◽  
Equivalence Classes ◽  
Random Networks ◽  
Formal Definition ◽  
Regular Equivalence ◽  
Definition Of

Networks and graphs are highly relevant in modeling real-life communities and their interactions. In order to gain insight in their structure, different roles are attributed to vertices, effectively clustering them in equivalence classes. A new formal definition of regular equivalence is presented in this paper, and the relation with other equivalence types is investigated and mathematically proven. An efficient algorithm is designed, able to detect all regularly equivalent roles in large-scale complex networks. We apply it to both Barabási–Albert random networks, as well as real-life social networks, which leads to interesting insights.

scholarly journals Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Christos Makris ◽  
Georgios Pispirigos
Keyword(s):  
Social Networks ◽  
Graph Partitioning ◽  
Large Scale ◽  
Real Life ◽  
Information Networks ◽  
Digital Marketing ◽  
Partitioning Problems ◽  
Iterative Solutions ◽  
Community Extraction ◽  
Stability And Accuracy

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

scholarly journals Training of a future social professional for the formation of safe adolescent behavior in social networks

2021 ◽  
Keyword(s):  
Social Networks ◽  
Information Exchange ◽  
Professional Training ◽  
Social Groups ◽  
Real Life ◽  
Well Being ◽  
The Internet ◽  
Stage Of Development ◽  
Safe Behavior ◽  
Definition Of

The article analyzes different approaches to the definition of «social networks» as technological complexes of organization and management of electronic information exchange among the subjects of social relations, united by common interests, information needs and skills. Based on the analysis of the scientific literature the essential characteristics of social networks that affect the formation and development of the adolescent's personality are revealed. Role of social networks at the present stage of development of society, which is manifested in the representation of interests not only of social groups but also of entire social groups, is defined in the article. The negative impact of social networks on the personality of the adolescent, which is manifested in the expansion of adolescents in cyberspace, the desire for independence and adulthood, selfexperimentation, which leads to risky activities both on the Internet and in real life are revealed. Concept of safe behavior in social networks as a set of actions of the individual when using the Internet, helping to meet the needs and at the same time prevent the possibility of causing damage to physical, mental, social well-being and property of man and others is analyzed. The basic rules of safe behavior in social Internet communities are highlighted. The structural components of safe behavior of adolescents in social networks are singled out: cognitive, motivational and actionreflexive; the concept of «professional training of future social professionals for the formation of safe behavior of adolescents in social networks» is revealed. Readiness is revealed as a result of the process of training future social specialists for professional activity on the formation of safe behavior of adolescents in social networks; the author's definition of the concept «readiness of future social professionals to form safe behavior of adolescents in social networks» is given. Components of readiness of future social workers to form safe behavior of teenagers in social networks, such as cognitive, motivational-personal and activity, are described.

scholarly journals Online Social Networks (OSN) Evolution Model Based on Homophily and Preferential Attachment

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 654 ◽  
Author(s):  
Jebran Khan ◽  
Sungchang Lee
Keyword(s):  
Social Networks ◽  
Structural Properties ◽  
Online Social Networks ◽  
Large Scale ◽  
Preferential Attachment ◽  
Real Life ◽  
Synthetic Data ◽  
Evolution Model ◽  
Scale Invariant ◽  
Scale Free

In this paper, we propose a new scale-free social networks (SNs) evolution model that is based on homophily combined with preferential attachments. Our model enables the SN researchers to generate SN synthetic data for the evaluation of multi-facet SN models that are dependent on users’ attributes and similarities. Homophily is one of the key factors for interactive relationship formation in SN. The synthetic graph generated by our model is scale-invariant and has symmetric relationships. The model is dynamic and sustainable to changes in input parameters, such as number of nodes and nodes’ attributes, by conserving its structural properties. Simulation and evaluation of models for large-scale SN applications need large datasets. One way to get SN data is to generate synthetic data by using SN evolution models. Various SN evolution models are proposed to approximate the real-life SN graphs in previous research. These models are based on SN structural properties such as preferential attachment. The data generated by these models is suitable to evaluate SN models that are structure dependent but not suitable to evaluate models which depend on the SN users’ attributes and similarities. In our proposed model, users’ attributes and similarities are utilized to synthesize SN graphs. We evaluated the resultant synthetic graph by analyzing its structural properties. In addition, we validated our model by comparing its measures with the publicly available real-life SN datasets and previous SN evolution models. Simulation results show our resultant graph to be a close representation of real-life SN graphs with users’ attributes.

scholarly journals Betting on the underdog: the influence of social networks on vote choice

2020 ◽  
pp. 1-8
Author(s):  
Annika Fredén ◽  
Ludovic Rheault ◽  
Indridi H. Indridason
Keyword(s):  
Social Networks ◽  
Real Life ◽  
Vote Choice ◽  
Random Networks ◽  
Popular Support ◽  
Treatment Groups ◽  
Echo Chambers ◽  
Simulated Network ◽  
High Degree ◽  
Small Probabilities

Abstract People are commonly expected not to waste their vote on parties with small probabilities of being elected. Yet, many end up voting for underdogs. We argue that voters gauge the popular support for their preferred party from their social networks. When social networks function as echo chambers, a feature observed in real-life networks, voters overestimate underdogs’ chances of winning. We conduct voting experiments in which some treatment groups receive signals from a simulated network. We compare the effect of networks with a high degree of homogeneity against random networks. We find that homophilic networks increase the level of support for underdogs, which provides evidence to back up anecdotal claims that echo chambers foster the development of fringe parties.

scholarly journals Formal Definition of Collaborative Spaces

2012 ◽  
Vol 22 ◽  
pp. 75-82 ◽  
Author(s):  
Josefina Guerrero García ◽  
Juan Manuel González-Calleros ◽  
Claudia Zepeda-Cortés
Keyword(s):  
Real Life ◽  
Formal Definition ◽  
Collaborative Spaces ◽  
Definition Of

Collaborative spaces are widely used for diverse organizations and purposes. Despite the fact that technological solutions exist there is a lack of methodological support to develop such environments. In this paper we illustrate how FlowiXML methodology can be used to develop collaborative spaces using a real life case study. The benefits of the resulting system are evaluated and the results are discussed.

scholarly journals CLUSTERING METHODS IN LARGE-SCALE SYSTEMS

2020 ◽  
Vol 6 (5) ◽  
pp. 21-24
Author(s):  
Denis V. Gadasin ◽  
◽  
Andrey V. Shvedov ◽  
Alyona A. Yudin ◽  
◽  
...  
Keyword(s):  
Social Network ◽  
Complex Networks ◽  
Large Scale ◽  
Physical Nature ◽  
Clustering Methods ◽  
Large Scale Systems ◽  
Practical Applications ◽  
System Properties ◽  
Definition Of ◽  
And Training

Interactions between people, groups, organizations, and biological cells have a relationship character that can be represented as a network. The system properties of such networks, regardless of their physical nature, but clearly determining the performance of networks, create the totality of the real world. Complex networks – are naturally existing networks (graphs) that have complex topological properties. The researchers who participate and also make discoveries in this field come from various Sciences such as mathematics, computer science, physics, sociology, and engineering. Therefore, the results of research carry both theoretical knowledge and practical applications in these Sciences. This paper discusses the definition of complex networks. The main characteristics of complex networks, such as clustering and congestion, are considered. A popular social network is considered as a complex network. The calculation of nodes and links of the considered social network is made. The main types of AI development and training are highlighted.

scholarly journals Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks

PLoS ONE ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. e99966 ◽  
Author(s):  
Raghvendra Mall ◽  
Rocco Langone ◽  
Johan A. K. Suykens
Keyword(s):  
Complex Networks ◽  
Spectral Clustering ◽  
Large Scale ◽  
Real Life

scholarly journals MULTI-HOP GENERALIZED CORE PERCOLATION ON COMPLEX NETWORKS

2020 ◽  
Vol 23 (01) ◽  
pp. 2050001 ◽  
Author(s):  
YILUN SHANG
Keyword(s):  
Phase Transition ◽  
Complex Networks ◽  
Heterogeneous Networks ◽  
Large Scale ◽  
Real Life ◽  
Continuous Phase ◽  
Global Vision ◽  
Discontinuous Phase ◽  
Analytical Frameworks ◽  
Selection Of

Recent theoretical studies on network robustness have focused primarily on attacks by random selection and global vision, but numerous real-life networks suffer from proximity-based breakdown. Here we introduce the multi-hop generalized core percolation on complex networks, where nodes with degree less than [Formula: see text] and their neighbors within [Formula: see text]-hop distance are removed progressively from the network. The resulting subgraph is referred to as [Formula: see text]-core, extending the recently proposed [Formula: see text]-core and classical core of a network. We develop analytical frameworks based upon generating function formalism and rate equation method, showing for instance continuous phase transition for [Formula: see text]-core and discontinuous phase transition for [Formula: see text]-core with any other combination of [Formula: see text] and [Formula: see text]. We test our theoretical results on synthetic homogeneous and heterogeneous networks, as well as on a selection of large-scale real-world networks. This unravels, e.g., a unique crossover phenomenon rooted in heterogeneous networks, which raises a caution that endeavor to promote network-level robustness could backfire when multi-hop tracing is involved.

scholarly journals DISCIPLINARY LIABILITY FOR VIOLATION OF LEGAL ETHICS

2019 ◽  
pp. 31-35 ◽  
Author(s):  
D. Kukhnyuk ◽  
B. Shylenko
Keyword(s):  
Social Networks ◽  
Real Life ◽  
Professional Activity ◽  
Public Attention ◽  
Legal Ethics ◽  
Ethical Component ◽  
Definition Of ◽  
The Individual ◽  
The Right ◽  
Ukrainian Society

The article is devoted to the research of legal ethics and disciplinary liability of the lawyer for their violation, the necessity and expediency of such liability. It has been discovered that the legal community is a part of the modern Ukrainian society, which stands guard over the protection of its interests. The appropriate constitutional status and responsibilities imposed on the advocacy cause the society's acute attention to the moral and ethical component of the activity of the advocacy in general and each lawyer in particular. It concerns both daily activities in real life and legal activities in social networks, which have become a significant part of our reality due to their total dissemination in personal and professional life of the individual. Such increased public attention to advocacy determines the need to ensure appropriate quality control over the specified professional activity, which is carried out by specialized structural divisions of the advocacy as an autonomous and self-governing institute in Ukraine. The results of such control are the disciplinary liability of the lawyer. The definition of disciplinary liability of a lawyer is a special type of legal liability applicable to a lawyer based on the results of a disciplinary proceeding carried out by a qualification and disciplinary commission of the advocacy for committing a disciplinary offensce. The content of the Legal Ethics Rules has been researched and found to be rather extensive and contains only imperative obligations and prohibitions but does not contain specific guidance on the use of clearly defined sanctions for violation of a particular Rule. And the adherence of the lawyer to the Rules of Legal Ethics, the admissibility of their actions and statements in real life, as well as their activities in social networks are is determined on the basis of appraisal concepts and depends on the discretion of the authorities with the right of official interpretation of the Rules of Legal Ethics in the process of disciplinary proceedings and, as a result, disciplinarypenalties.

scholarly journals Cooperation, clustering, and assortative mixing in dynamic networks

2018 ◽  
Vol 115 (5) ◽  
pp. 951-956 ◽  
Author(s):  
David Melamed ◽  
Ashley Harrell ◽  
Brent Simpson
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Network Dynamics ◽  
Dynamic Networks ◽  
Partner Choice ◽  
Random Networks ◽  
Network Clustering ◽  
Initial Network ◽  
Scale Experiment ◽  
Individual Choices

Humans’ propensity to cooperate is driven by our embeddedness in social networks. A key mechanism through which networks promote cooperation is clustering. Within clusters, conditional cooperators are insulated from exploitation by noncooperators, allowing them to reap the benefits of cooperation. Dynamic networks, where ties can be shed and new ties formed, allow for the endogenous emergence of clusters of cooperators. Although past work suggests that either reputation processes or network dynamics can increase clustering and cooperation, existing work on network dynamics conflates reputations and dynamics. Here we report results from a large-scale experiment (total n = 2,675) that embedded participants in clustered or random networks that were static or dynamic, with varying levels of reputational information. Results show that initial network clustering predicts cooperation in static networks, but not in dynamic ones. Further, our experiment shows that while reputations are important for partner choice, cooperation levels are driven purely by dynamics. Supplemental conditions confirmed this lack of a reputation effect. Importantly, we find that when participants make individual choices to cooperate or defect with each partner, as opposed to a single decision that applies to all partners (as is standard in the literature on cooperation in networks), cooperation rates in static networks are as high as cooperation rates in dynamic networks. This finding highlights the importance of structured relations for sustained cooperation, and shows how giving experimental participants more realistic choices has important consequences for whether dynamic networks promote higher levels of cooperation than static networks.

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