Two Informational Complexity Measures in Social Networks and Agent Communities

2011 ◽  
pp. 57-65
Author(s):  
António Jorge Filipe Fonseca
Keyword(s):  
Stochastic Process ◽  
Social Networks ◽  
Social Network ◽  
Structural Properties ◽  
Information Sources ◽  
Complexity Measures ◽  
Dynamic Relation ◽  
Dynamic Measures

Several informational complexity measures rely on the notion of stochastic process in order to extract hidden structural properties behind the apparent randomness of information sources. Following an equivalence approach between dynamic relation evolution within a social network and a generic stochastic process two dynamic measures of network complexity are proposed.

Two Informational Complexity Measures in Social Networks and Agent Communities

2010 ◽  
pp. 1923-1931
Author(s):  
António Jorge Filipe Fonseca
Keyword(s):  
Stochastic Process ◽  
Social Networks ◽  
Social Network ◽  
Structural Properties ◽  
Information Sources ◽  
Complexity Measures ◽  
Dynamic Relation ◽  
Dynamic Measures

Several informational complexity measures rely on the notion of stochastic process in order to extract hidden structural properties behind the apparent randomness of information sources. Following an equivalence approach between dynamic relation evolution within a social network and a generic stochastic process two dynamic measures of network complexity are proposed.

Two Informational Complexity Measures in Social Networks and Agent Communities

2009 ◽  
Vol 1 (4) ◽  
pp. 49-57
Author(s):  
António Jorge Filipe Fonseca
Keyword(s):  
Stochastic Process ◽  
Social Networks ◽  
Social Network ◽  
Structural Properties ◽  
Information Sources ◽  
Complexity Measures ◽  
Dynamic Relation ◽  
Dynamic Measures

Several informational complexity measures rely on the notion of stochastic process in order to extract hidden structural properties behind the apparent randomness of information sources. Following an equivalence approach between dynamic relation evolution within a social network and a generic stochastic process two dynamic measures of network complexity are proposed.

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 Modeling Network Dynamics

2017 ◽  
Author(s):  
Christopher Steven Marcum ◽  
David R. Schaefer
Keyword(s):  
Stochastic Process ◽  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
Network Science ◽  
Dynamic Network ◽  
Process Theory ◽  
Network Data ◽  
Network Change ◽  
Human Actors

One of the great lessons from the last half century of research on social networks is that relationships are constantly in flux. While much social network analysis focuses on static relationships between actors, there is also a rich tradition of work extending back to foundational studies in network science focused on the notion that network change is an indelible aspect of social life for human and non-human actors alike (e.g., Bott, 1957; Heider, 1946; Newcomb 1961; Rapoport, 1949; Sampson, 1969). Today, social network researchers benefit from this history in that a host of methods to collect and analyze such dynamic network data have been developed. Among them, the methods based on stochastic process theory have given rise to a paradigm where inferences and predictions can be made on the mechanisms that drive changes in social structure.

scholarly journals Polarized information ecosystems can reorganize social networks via information cascades

2021 ◽  
Vol 118 (50) ◽  
pp. e2102147118 ◽  
Author(s):  
Christopher K. Tokita ◽  
Andrew M. Guess ◽  
Corina E. Tarnita
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Computational Model ◽  
Network Structure ◽  
Information Sources ◽  
Social Ties ◽  
Network Data ◽  
Information Cascades ◽  
Structure Changes ◽  
Social Network Structure

The precise mechanisms by which the information ecosystem polarizes society remain elusive. Focusing on political sorting in networks, we develop a computational model that examines how social network structure changes when individuals participate in information cascades, evaluate their behavior, and potentially rewire their connections to others as a result. Individuals follow proattitudinal information sources but are more likely to first hear and react to news shared by their social ties and only later evaluate these reactions by direct reference to the coverage of their preferred source. Reactions to news spread through the network via a complex contagion. Following a cascade, individuals who determine that their participation was driven by a subjectively “unimportant” story adjust their social ties to avoid being misled in the future. In our model, this dynamic leads social networks to politically sort when news outlets differentially report on the same topic, even when individuals do not know others’ political identities. Observational follow network data collected on Twitter support this prediction: We find that individuals in more polarized information ecosystems lose cross-ideology social ties at a rate that is higher than predicted by chance. Importantly, our model reveals that these emergent polarized networks are less efficient at diffusing information: Individuals avoid what they believe to be “unimportant” news at the expense of missing out on subjectively “important” news far more frequently. This suggests that “echo chambers”—to the extent that they exist—may not echo so much as silence.

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

Social Networks and Business Performance of Igbo Migrant Traders in Ibadan, South-West Nigeria

2014 ◽  
Vol 12 (2) ◽  
Author(s):  
Deborah O. Obor ◽  
Emeka E. Okafor
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Social Network ◽  
Social Exchange ◽  
Business Performance ◽  
Exploratory Research ◽  
Business Growth ◽  
South West ◽  
Key Informant ◽  
Depth Interviews

This study focused on social networks and business performance among Igbo businessmen in Ibadan, South-west Nigeria through the exploratory research design. Social exchange, social network and social capital theories were employed as theoretical framework. Twenty-six in-depth interviews, key informant interviews and case studies were conducted with purposively selected respondents in four business locations in Ibadan. The results showed that among the factors that facilitated migration of the Igbo to Ibadan were their interest to learn a trade, their inability to attain higher education, and having a relative in Ibadan. The types of social networks available showed that social network was not location bound, as all the respondents belonged to town progressive unions and mutual benefits/cooperative associations. Social networks played vital roles in business performance, including social support, access to loan, business growth and expansion. The main challenges to maintaining adequate social network in business were distrust, envy, unbridled competition, dishonesty and inability to keep terms of agreement. The study concludes that social networks have positively influenced the business performance of migrant Igbo in Ibadan. There is need for the Igbo to strengthen their social networks through honesty, forthrightness, and transparency in all their dealings.

Networks

2020 ◽  
pp. 534-562
Author(s):  
Matthew O. Jackson ◽  
Brian W. Rogers ◽  
Yves Zenou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Statistical Analysis ◽  
Labor Markets ◽  
Network Formation ◽  
Labor Market Outcomes ◽  
Negative Consequences ◽  
Social Scientists ◽  
Market Outcomes

What is the role of social networks in driving persistent differences between races and genders in education and labor market outcomes? What is the role of homophily in such differences? Why is such homophily seen even if it ends up with negative consequences in terms of labor markets? This chapter discusses social network analysis from the perspective of economics. The chapter is organized around the theme of externalities: the effects that one’s behavior has on others’ welfare. Externalities underlie the interdependencies that make networks interesting to social scientists. This chapter discusses network formation, as well as interactions between people’s behaviors within a given network, and the implications in a variety of settings. Finally, the chapter highlights some empirical challenges inherent in the statistical analysis of network-based data.

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.

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