scholarly journals SOCIAL NETWORKS, BIG DATA AND TRANSPORT PLANNING

2016 ◽  
Author(s):  
Tomás Ruiz Sánchez ◽  
María del Lidón Mars Aicart ◽  
María Rosa Arroyo López ◽  
Ainhoa Serna Nocedal
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Social Life ◽  
Quality Data ◽  
Future Prospects ◽  
Huge Amount ◽  
Transport Planning ◽  
High Quality Data ◽  
Potential Sources

The characteristics of people who are related or tied to each individual affects her activitytravel behavior. That influence is especially associated to social and recreational activities, which are increasingly important. Collecting high quality data from those social networks is very difficult, because respondents are asked about their general social life, which is most demanding to remember that specific facts. On the other hand, currently there are different potential sources of transport data, which is characterized by the huge amount of information available, the velocity with it is obtained and the variety of format in which is presented. This sort of information is commonly known as Big Data. In this paper we identify potential sources of social network related big data that can be used in Transport Planning. Then, a review of current applications in Transport Planning is presented. Finally, some future prospects of using social network related big data are highlighted.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4251

Introduction

2020 ◽  
pp. xxii-13
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Bibliometric Analysis ◽  
Social Life ◽  
New Directions ◽  
Central Paradigm

This chapter provides an introduction to this volume on social networks. It argues that social network analysis is greater than a method or data, but serves as a central paradigm for understanding social life. The chapter offers evidence of the influence of social network analysis with a bibliometric analysis of research on social networks. This analysis underscores how pervasive network analysis has become and highlights key theoretical and methodological concerns. It also introduces the sections of the volume broadly structured around theory, methods, broad conceptualizations like culture and temporality, and disciplinary contributions. The chapter concludes by discussing several promising new directions in the field of social network analysis.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

scholarly journals Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

2012 ◽  
Vol 367 (1599) ◽  
pp. 2108-2118 ◽  
Author(s):  
Louise Barrett ◽  
S. Peter Henzi ◽  
David Lusseau
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
The Other ◽  
Feedback Process ◽  
Cognitive Evolution ◽  
Systems Approaches ◽  
Entropy Measures ◽  
Free Ranging ◽  
Source Of Information

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural ‘knock-outs’ in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.

scholarly journals Determining the proximity of groups in social networks based on text analysis using big data

2019 ◽  
pp. 521-526
Author(s):  
A S Mukhin ◽  
I A Rytsarev ◽  
R A Paringer ◽  
A V Kupriyanov ◽  
D V Kirsh
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Data Collection ◽  
Text Analysis ◽  
Software Tool ◽  
Text Data ◽  
The Social ◽  
Information Research ◽  
Definition Of

The article is devoted to the definition of such groups in social networks. The object of the study was selected data social network Vk. Text data was collected, processed and analyzed. To solve the problem of obtaining the necessary information, research was conducted in the field of optimization of data collection of the social network Vk. A software tool that provides the collection and subsequent processing of the necessary data from the specified resources has been developed. The existing algorithms of text analysis, mainly of large volume, were investigated and applied.

Intranet Exploitation of Social Network Knowledge Intelligence

2017 ◽  
pp. 273-298
Author(s):  
Mark Alan Underwood
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Life Cycle ◽  
Online Social Networks ◽  
Intelligent Agents ◽  
Process Mining ◽  
Just In Time ◽  
Domain Specific ◽  
Social Graphs

Intranets are almost as old as the concept of a web site. More than twenty-five years ago the text Business Data Communications closed with a discussion of intranets (Stallings, 1990). Underlying technology improvements in intranets have been incremental; intranets were never seen as killer developments. Yet the popularity of Online Social Networks (OSNs) has led to increased interest in the part OSNs play – or could play – in using intranets to foster knowledge management. This chapter reviews research into how social graphs for an enterprise, team or other collaboration group interacts with the ways intranets have been used to display, collect, curate and disseminate information over the knowledge life cycle. Future roles that OSN-aware intranets could play in emerging technologies, such as process mining, elicitation methods, domain-specific intelligent agents, big data, and just-in-time learning are examined.

scholarly journals Social Network Extraction Unsupervised

2021 ◽  
Vol 12 (3) ◽  
pp. 4443-4449
Author(s):  
Mahyuddin K. M. Nasution Et.al
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Information Technology ◽  
Big Data ◽  
Social Network ◽  
Data Science ◽  
Information Sources ◽  
Scientific Data

In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.

scholarly journals A multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaques

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8712 ◽  
Author(s):  
Brianne Beisner ◽  
Niklas Braun ◽  
Márton Pósfai ◽  
Jessica Vandeleest ◽  
Raissa D’Souza ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
Rhesus Macaques ◽  
Dominance Rank ◽  
Social Groups ◽  
Single Layer ◽  
Demographic Factors ◽  
Socio Demographic Factors ◽  
The Impact

Members of a society interact using a variety of social behaviors, giving rise to a multi-faceted and complex social life. For the study of animal behavior, quantifying this complexity is critical for understanding the impact of social life on animals’ health and fitness. Multilayer network approaches, where each interaction type represents a different layer of the social network, have the potential to better capture this complexity than single layer approaches. Calculating individuals’ centrality within a multilayer social network can reveal keystone individuals and more fully characterize social roles. However, existing measures of multilayer centrality do not account for differences in the dynamics and functionality across interaction layers. Here we validate a new method for quantifying multiplex centrality called consensus ranking by applying this method to multiple social groups of a well-studied nonhuman primate, the rhesus macaque. Consensus ranking can suitably handle the complexities of animal social life, such as networks with different properties (sparse vs. dense) and biological meanings (competitive vs. affiliative interactions). We examined whether individuals’ attributes or socio-demographic factors (sex, age, dominance rank and certainty, matriline size, rearing history) were associated with multiplex centrality. Social networks were constructed for five interaction layers (i.e., aggression, status signaling, conflict policing, grooming and huddling) for seven social groups. Consensus ranks were calculated across these five layers and analyzed with respect to individual attributes and socio-demographic factors. Generalized linear mixed models showed that consensus ranking detected known social patterns in rhesus macaques, showing that multiplex centrality was greater in high-ranking males with high certainty of rank and females from the largest families. In addition, consensus ranks also showed that females from very small families and mother-reared (compared to nursery-reared) individuals were more central, showing that consideration of multiple social domains revealed individuals whose social centrality and importance might otherwise have been missed.

A Research Approach to Detect Unreliable Information in Online Professional Social Networks

2015 ◽  
Vol 6 (4) ◽  
pp. 39-56
Author(s):  
Nan Jing ◽  
Mengdi Li ◽  
Su Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Research Approach ◽  
Web Pages ◽  
Huge Amount ◽  
Pagerank Algorithm ◽  
Amount Of Information ◽  
Professional Network ◽  
Cross Site

Professional social network gives companies a platform to post hiring information and locate professional talents. However, the professional network has a great number of users who generate huge amount of information every day, which makes it difficult for the hiring company to distinguish reliability of users' information and evaluate their professional abilities. In this context, this article bases on LinkedIn Mobile as the online professional social network and proposes a research approach to effectively identify unreliable information and evaluate users' abilities. First, the authors look for relevant social network profiles for a cross-site check. Second, on a single professional social networking they site, the authors check the similarity between the user's background and his connections' backgrounds, to detect any possible unreliable information. Third, they propose an algorithm to rank the trustfulness of users' recommendations based on a PageRank algorithm that was traditionally to evaluate the importance of web pages.

Big Data Analysis for Terroristic Behavior Identification and Study Using Social Networks

2019 ◽  
pp. 197-235
Author(s):  
Yuriy V. Kostyuchenko ◽  
Victor Pushkar ◽  
Olga Malysheva ◽  
Maxim Yuschenko
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Stochastic Methods ◽  
Armed Groups ◽  
Conflict Zones ◽  
Analysis Of Dynamics

This chapter aimed to consider of approaches to big data (social network content) utilization for understanding social behavior in the conflict zones, and analysis of dynamics of illegal armed groups. The analysis directed to identify of structure of illegal armed groups, and detection of underage militants. The probabilistic and stochastic methods of analysis and classification of number, composition, and dynamics of illegal armed groups in active conflict areas are proposed. Data of armed conflict in Donbas (Eastern Ukraine) in the period 2014-2015 is used for analysis. The numerical distribution of age, gender composition, origin, social status, and nationality of militants among illegal armed groups has been calculated. Conclusions on the applicability of described method in criminological practice, as well as about the possibilities of interpretation of obtaining results in the context of study of terrorism are proposed.

scholarly journals Social Networks among the Older Chinese Population in the USA: Findings from the PINE Study

Gerontology ◽  
2017 ◽  
Vol 63 (3) ◽  
pp. 238-252 ◽  
Author(s):  
XinQi Dong ◽  
E-Shien Chang
Keyword(s):  
Older Adults ◽  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
Community Dwelling ◽  
Socioeconomic Characteristics ◽  
The Social ◽  
Study Results ◽  
The Usa ◽  
Older Chinese

Background: Social network research has become central to studies of health and aging. Its results may yield public health insights that are actionable and improve the quality of life of older adults. However, little is known about the social networks of older immigrant adults, whose social relationships often develop in the context of migration, compounded by cultural and linguistic barriers. Objectives: This report aims to describe the structure, composition, and emotional components of social networks in the Chinese aging population of the USA, and to explore ways in which their social networks may be critical to their health decision-making. Methods: Our data come from the PINE study, a population-based epidemiological study of community-dwelling older Chinese American adults, aged 60 years and above, in the greater Chicago area. We conducted individual interviews in participants' homes from 2011 until 2013. Based on sociodemographic and socioeconomic characteristics, this study computed descriptive statistics and trend tests for the social network measures adapted from the National Social Life, Health, and Aging Project study. Results: The findings show that older Chinese adults have a relatively small social network in comparison with their counterparts from other ethnic and racial backgrounds. Only 29.6% of the participants could name 5 close network members, and 2.2% could name 0 members. Their network composition was more heavily kin oriented (95.0%). Relationships with network members differed according to the older adults' sociodemographic and socioeconomic characteristics. Subgroup variations included the likelihood of discussing health-related issues with network members. Conclusion: This study highlights the dynamic nature of social networks in later-life Chinese immigrants. For healthcare practitioners, developing cost-effective strategies that can mobilize social network support remains a critical undertaking in health intervention. Longitudinal studies are needed to examine the causal impact of social networks on various domains of health.

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