scholarly journals Who comes to mind? Dynamic construction of social networks

2020 ◽  
Author(s):  
Joseph Bayer ◽  
Neil Anthony Lewis ◽  
Jonathan Stahl
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Dynamic Process ◽  
Social Groups ◽  
Social Resources ◽  
Personal Relationships ◽  
Cognitive Mechanisms ◽  
Relational Structures ◽  
Network Technologies

Much remains unknown about moment-to-moment social-network cognition — that is, who comes to mind as we go about our day-to-day lives. Responding to this void, we describe the real-time construction of cognitive social networks. First, we outline the types of relational structures that comprise momentary networks, distinguishing the roles of personal relationships, social groups, and mental sets. Second, we discuss the cognitive mechanisms that determine which individuals are activated — and which are neglected — through a dynamic process. Looking forward, we contend that these overlooked mechanisms need to be considered in light of emerging network technologies. Finally, we chart the next steps for understanding social-network cognition across real-world contexts, along with the built-in implications for social resources and intergroup disparities.

scholarly journals Who Comes to Mind? Dynamic Construction of Social Networks

2020 ◽  
Vol 29 (3) ◽  
pp. 279-285 ◽  
Author(s):  
Joseph B. Bayer ◽  
Neil A. Lewis ◽  
Jonathan L. Stahl
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Dynamic Process ◽  
Social Groups ◽  
Social Resources ◽  
Personal Relationships ◽  
Cognitive Mechanisms ◽  
Relational Structures ◽  
Network Technologies

Much remains unknown about moment-to-moment social-network cognition—that is, who comes to mind as we go about our day-to-day lives. Responding to this void, we describe the real-time construction of cognitive social networks. First, we outline the types of relational structures that comprise momentary networks, distinguishing the roles of personal relationships, social groups, and mental sets. Second, we discuss the cognitive mechanisms that determine which individuals are activated—and which are neglected—through a dynamic process. Looking forward, we contend that these overlooked mechanisms need to be considered in light of emerging network technologies. Finally, we chart the next steps for understanding social-network cognition across real-world contexts, along with the built-in implications for social resources and intergroup disparities.

Competence Management over Social Networks Through Dynamic Taxonomies

2013 ◽  
pp. 103-120
Author(s):  
Giuseppe Berio ◽  
Antonio Di Leva ◽  
Mounira Harzallah ◽  
Giovanni M. Sacco
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Reference Model ◽  
Effective Solution ◽  
Network Information ◽  
The Social ◽  
Competence Management

The exploitation and integration of social network information in a competence reference model (CRAI, Competence, Resource, Aspect, Individual) are discussed. The Social-CRAI model, which extends CRAI to social networks, provides an effective solution to this problem and is discussed in detail. Finally, dynamic taxonomies, a model supporting explorative conceptual search, are introduced and their use in the context of the Social-CRAI model for exploring retrieved information available in social networks is discussed. A real-world example is provided.

Dynamics and Evolutional Patterns of Social Networks

2011 ◽  
pp. 156-170
Author(s):  
Yingzi Jin ◽  
Yutaka Matsuo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Real World ◽  
Dynamic Network ◽  
Network Evolution ◽  
Belief Formation ◽  
Network Mining ◽  
Longitudinal Network Analysis ◽  
Relational Network

Previous chapters focused on the models of static networks, which consider a relational network at a given point in time. However, real-world social networks are dynamic in nature; for example, friends of friends become friends. Social network research has, in recent years, paid increasing attention to dynamic and longitudinal network analysis in order to understand network evolution, belief formation, friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of social networks. The chapter introduces real-world applications and reviews major theories and models of dynamic network mining.

scholarly journals Hashtag2Vec: Learning Hashtag Representation with Relational Hierarchical Embedding Model

2018 ◽  
Author(s):  
Jie Liu ◽  
Zhicheng He ◽  
Yalou Huang
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Social Network ◽  
Real World ◽  
Heterogeneous Network ◽  
Event Analysis ◽  
Short Text ◽  
Theme Discovery ◽  
Real World Datasets ◽  
Text Content

Hashtags have always been important elements in many social network platforms and micro-blog services. Semantic understanding of hashtags is a critical and fundamental task for many applications on social networks, such as event analysis, theme discovery, information retrieval, etc. However, this task is challenging due to the sparsity, polysemy, and synonymy of hashtags. In this paper, we investigate the problem of hashtag embedding by combining the short text content with the various heterogeneous relations in social networks. Specifically, we first establish a network with hashtags as its nodes. Hierarchically, each of the hashtag nodes is associated with a set of tweets and each tweet contains a set of words. Then we devise an embedding model, called Hashtag2Vec, which exploits multiple relations of hashtag-hashtag, hashtag-tweet, tweet-word, and word-word relations based on the hierarchical heterogeneous network. In addition to embedding the hashtags, our proposed framework is capable of embedding the short social texts as well. Extensive experiments are conducted on two real-world datasets, and the results demonstrate the effectiveness of the proposed method.

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.

scholarly journals Jesus’s Social Network and the Four Gospels: Exploring the Relational Dynamics of the Gospels Using Social Network Analysis

2020 ◽  
Vol 50 (1) ◽  
pp. 35-53
Author(s):  
Jennifer M. McClure
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Structural Dynamics ◽  
Relational Dynamics ◽  
Relational Structures ◽  
Network Analyses

Building on recent studies of Jesus’s social network, this article seeks to explore how the relational dynamics surrounding Jesus’s life and ministry are depicted differently in the canonical Gospels. Using different perspectives and methods than those usually employed by biblical scholars, the network analyses provide rich illustrations and descriptions of structural dynamics that have not traditionally been the focus of Gospel scholarship. Analyses examine the extent to which the Gospels’ social networks overlap, as well as differences in levels of relational prominence and in relational structures across the Gospels. The results provide a unique window into the relational dynamics portrayed by the Gospels, producing a variety of insights, some which may not surprise biblical scholars but others which hopefully will inspire further consideration.

Opportunities and Threats Analysis Caused by Social Media

2022 ◽  
pp. 46-64
Author(s):  
Elina Ahmadi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Information And Communication Technologies ◽  
Social Groups ◽  
Communication Technologies ◽  
Electronic Learning ◽  
Daily Work ◽  
Information And Communication ◽  
Diverse Cultures

With the advent and widespread use of information and communication technologies, the need for information has become part of the daily work of individuals. Recently, social networks are one of the most important topics in cyberspace. This study seeks to identify and rank the opportunities and threats of social media for the society. Three hundred seventy students active in social networks are selected by clustering sampling method. A conceptual model is developed based on the review of theoretical literature and five opportunities of social network including electronic learning, filling leisure time, organizing social groups, getting to know about diverse cultures, possibility of conversation, as well as five threats include sharing anti values, abusing, dissemination of misinformation, internet addiction, and malacious communication have significant effect on the students.

Parallel Greedy Algorithm to Multiple Influence Maximization in Social Network

2021 ◽  
Vol 15 (3) ◽  
pp. 1-21
Author(s):  
Guanhao Wu ◽  
Xiaofeng Gao ◽  
Ge Yan ◽  
Guihai Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Parallel Algorithms ◽  
Real World ◽  
Online Social Networks ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Memory Overhead ◽  
Influence Spread ◽  
Real World Applications

Influence Maximization (IM) problem is to select influential users to maximize the influence spread, which plays an important role in many real-world applications such as product recommendation, epidemic control, and network monitoring. Nowadays multiple kinds of information can propagate in online social networks simultaneously, but current literature seldom discuss about this phenomenon. Accordingly, in this article, we propose Multiple Influence Maximization (MIM) problem where multiple information can propagate in a single network with different propagation probabilities. The goal of MIM problems is to maximize the overall accumulative influence spreads of different information with the limit of seed budget . To solve MIM problems, we first propose a greedy framework to solve MIM problems which maintains an -approximate ratio. We further propose parallel algorithms based on semaphores, an inter-thread communication mechanism, which significantly improves our algorithms efficiency. Then we conduct experiments for our framework using complex social network datasets with 12k, 154k, 317k, and 1.1m nodes, and the experimental results show that our greedy framework outperforms other heuristic algorithms greatly for large influence spread and parallelization of algorithms reduces running time observably with acceptable memory overhead.

scholarly journals Dimensionality of Social Networks Using Motifs and Eigenvalues

2021 ◽  
Author(s):  
Anthony Bonato ◽  
David F. Gleich ◽  
Myunghwan Kim ◽  
Dieter Mitsche ◽  
Paweł Prałat ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Model ◽  
Power Law ◽  
Metric Space ◽  
Real World ◽  
Eigenvalue Distribution

We consider the dimensionality of social networks, and develop experiments aimed at predicting that dimension. We find that a social network model with nodes and links sampled from an m-dimensional metric space with power-law distributed influence regions best fits samples from real-world networks when m scales logarithmically with the number of nodes of the network. This supports a logarithmic dimension hypothesis, and we provide evidence with two different social networks, Facebook and LinkedIn. Further, we employ two different methods for confirming the hypothesis: the first uses the distribution of motif counts, and the second exploits the eigenvalue distribution.

scholarly journals A blockmodel study of managerial hierarchies, verbal networks, and organizational culture in four hospitals

2012 ◽  
Vol 46 (1) ◽  
pp. 291-314 ◽  
Author(s):  
Reed Elliot Nelson
Keyword(s):  
Social Networks ◽  
Organizational Culture ◽  
Social Network ◽  
Limited Impact ◽  
Relational Structures ◽  
Center Periphery

This study demonstrates and applies a social network methodology for studying the dynamics of hierarchies in organizations. Social network (blockmodel) analysis of verbal networks in four hospitals contrasted hierarchical and structurally equivalent partitions of the sociomatrices of frequent ties and perceptions of organizational culture. It was found that the verbal networks in these organizations follow a center periphery pattern rather than a hierarchical logic and that perceptions of culture vary more by verbal network than by formal hierarchy. The perceptions of culture of central groups in one organization are much like those of peripheral groups in another. In all four hospitals, structurally equivalent social networks are more important in predicting subcultures than are hierarchical groupings and hierarchy has a limited impact on the development of verbal networks. These findings suggest the value of an amoeba rather than a pyramid metaphor in interpreting the cultures and relational structures of organizations.

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