What Influences Relationship Formation in a Global Civil Society Network? An Examination of Valued Multiplex Relations

2021 ◽  
pp. 009365022110161
Author(s):  
Adam J. Saffer ◽  
Andrew Pilny ◽  
Erich J. Sommerfeldt
Keyword(s):  
Communication Network ◽  
Civil Society ◽  
Real World ◽  
Network Formation ◽  
Preferential Attachment ◽  
Interorganizational Networks ◽  
Communication Research ◽  
Relationship Formation ◽  
Interorganizational Communication ◽  
Strength Of Ties

Recent interorganizational communication research has taken up the question: why are networks structured the way they are? This line of inquiry has advanced communication network research by helping explain how and why networks take on certain structures or why certain organizations become positioned advantageously (or not). Yet, those studies assume relationships among organizations are either present or absent. This study considers how the strength of ties and multiplex relationships among organizations may reveal a more complex explanation for why networks take on certain structures. Our results challenge some long held assumptions about the mechanisms that influence network formation. For instance, our results offer important insights into the consequences of closure mechanisms, the applicability of preferential attachment to real-world networks, and the nuances of homophily in network formation on multidimensional relationships in a communication network. Implications for interorganizational networks are discussed.

Models of network formation

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Distribution ◽  
Network Formation ◽  
Preferential Attachment ◽  
Network Size ◽  
Large Network ◽  
Citation Networks ◽  
Airline Networks ◽  
Attachment Model ◽  
Delivery Networks

This chapter describes models of the growth or formation of networks, with a particular focus on preferential attachment models. It starts with a discussion of the classic preferential attachment model for citation networks introduced by Price, including a complete derivation of the degree distribution in the limit of large network size. Subsequent sections introduce the Barabasi-Albert model and various generalized preferential attachment models, including models with addition or removal of extra nodes or edges and models with nonlinear preferential attachment. Also discussed are node copying models and models in which networks are formed by optimization processes, such as delivery networks or airline networks.

scholarly journals Friendship paradox in growth networks: analytical and empirical analysis

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Sergei P. Sidorov ◽  
Sergei V. Mironov ◽  
Alexey A. Grigoriev
Keyword(s):  
Real World ◽  
Preferential Attachment ◽  
Empirical Studies ◽  
Mean Field ◽  
Network Models ◽  
Average Degree ◽  
Closure Model ◽  
Triadic Closure ◽  
Attachment Mechanism ◽  
Over Time

AbstractMany empirical studies have shown that in social, citation, collaboration, and other types of networks in real world, the degree of almost every node is less than the average degree of its neighbors. This imbalance is well known in sociology as the friendship paradox and states that your friends are more popular than you on average. If we introduce a value equal to the ratio of the average degree of the neighbors for a certain node to the degree of this node (which is called the ‘friendship index’, FI), then the FI value of more than 1 for most nodes indicates the presence of the friendship paradox in the network. In this paper, we study the behavior of the FI over time for networks generated by growth network models. We will focus our analysis on two models based on the use of the preferential attachment mechanism: the Barabási–Albert model and the triadic closure model. Using the mean-field approach, we obtain differential equations describing the dynamics of changes in the FI over time, and accordingly, after obtaining their solutions, we find the expected values of this index over iterations. The results show that the values of FI are decreasing over time for all nodes in both models. However, for networks constructed in accordance with the triadic closure model, this decrease occurs at a much slower rate than for the Barabási–Albert graphs. In addition, we analyze several real-world networks and show that their FI distributions follow a power law. We show that both the Barabási–Albert and the triadic closure networks exhibit the same behavior. However, for networks based on the triadic closure model, the distributions of FI are more heavy-tailed and, in this sense, are closer to the distributions for real networks.

scholarly journals Communication network formation with link specificity and value transferability

2013 ◽  
Vol 229 (1) ◽  
pp. 199-211 ◽  
Author(s):  
Marjolein J.W. Harmsen - van Hout ◽  
P. Jean-Jacques Herings ◽  
Benedict G.C. Dellaert
Keyword(s):  
Communication Network ◽  
Network Formation

scholarly journals Self-organizing topology for energy-efficient ad-hoc communication networks of mobile devices

2020 ◽  
Author(s):  
Indushree Banerjee ◽  
Martijn Warnier ◽  
Frances M. T Brazier
Keyword(s):  
Communication Network ◽  
Mobile Devices ◽  
Communication Networks ◽  
Energy Efficient ◽  
Ad Hoc ◽  
Preferential Attachment ◽  
Scale Free ◽  
Simulated Environment ◽  
Adaptive Topology ◽  
Hoc Networks

Abstract When physical communication network infrastructures fail, infrastructure-less communication networks such as mobile ad-hoc networks (MANET), can provide an alternative. This, however, requires MANETs to be adaptable to dynamic contexts characterized by the changing density and mobility of devices and availability of energy sources. To address this challenge, this paper proposes a decentralized context-adaptive topology control protocol. The protocol consists of three algorithms and uses preferential attachment based on the energy availability of devices to form a loop-free scale-free adaptive topology for an ad-hoc communication network. The proposed protocol has a number of advantages. First, it is adaptive to the environment, hence applicable in scenarios where the number of participating mobile devices and their availability of energy resources is always changing. Second, it is energy-efficient through changes in the topology. This means it can be flexibly be combined with different routing protocols. Third, the protocol requires no changes on the hardware level. This means it can be implemented on all current phones, without any recalls or investments in hardware changes. The evaluation of the protocol in a simulated environment confirms the feasibility of creating and maintaining a self-adaptive ad-hoc communication network, consisting of multitudes of mobile devices for reliable communication in a dynamic context.

scholarly journals Participation in real-world laboratories in a new light?! Closing the gap between co-creative and deliberative participation

2021 ◽  
Vol 79 (4) ◽  
pp. 424-437
Author(s):  
Marco Sonnberger ◽  
Doris Lindner
Keyword(s):  
Civil Society ◽  
Real World ◽  
Social Worlds ◽  
Opinion Formation ◽  
Sustainable Mobility ◽  
Active Involvement ◽  
Knowledge Based ◽  
Decision Making Processes ◽  
Practical Experiences ◽  
Participation Research

Real-world laboratories (RWL) involve co-design and co-creation of knowledge based on a transdisciplinary cooperation of actors from different social worlds – academia, administration, economy, civil society – each endowed with specific interests, resources and worldviews. According to their claim, RWLs are supposed to be a means of inclusive participation in the co-creative shaping of solutions for socioecological issues. In the literature dealing with RWLs, participation is thus mainly understood as an active involvement by civil society, change agents and citizens in processes of experimentation and implementation of solutions. We call this co-creative participation. However, participation in talk-based opinion formation and decision-making processes – we call this deliberative participation – is hardly a subject of discussion in the respective literature, although deliberative participation has been at the heart of participation research for several decades. In this paper, we argue that co-creative and deliberative participation are two distinct forms of participation which can be conceptualized differently but are both relevant for successful experimentation in RWLs. Based on our practical experiences in the ‘real-world laboratory for sustainable mobility culture’ (RNM), we propose an ideal-typical conceptual framework for participation in RWLs that combines co-creative and deliberative participation, thereby aiming to contribute to a systematization of, and rationale for, different forms of participation in RWLs.

Multi-Agent–Based Simulation of University Email Communities

2011 ◽  
pp. 273-291
Author(s):  
David Rodrigues
Keyword(s):  
Communication Network ◽  
Network Formation ◽  
Real Data ◽  
Informal Communication ◽  
University Environment ◽  
Agent Based ◽  
Detection Techniques ◽  
The Social ◽  
Multi Agent ◽  
Full Knowledge

In this chapter, a study on informal communication network formation in a university environment is presented. The teacher communication network is analyzed through community detection techniques. It is evident that informal communication is an important process that traverses the vertical hierarchical structure of departments and courses in a university environment. A multi-agent model of the case study is presented here, showing the implications of using real data as training sets for multi-agent-based simulations. The influence of the “social neighborhood,” as a mechanism to create assortative networks of contacts without full knowledge of the network, is discussed. It is shown that the radius of this social neighborhood has an effect on the outcome of the network structure and that in a university’s case this distance is relatively small.

Interorganizational Relationships, Strategic Alliances, and Networks

2003 ◽  
pp. 216-245
Author(s):  
Keiichi Yamada
Keyword(s):  
Significant Role ◽  
Strategic Alliances ◽  
Communication Systems ◽  
Interorganizational Relationships ◽  
Interorganizational Networks ◽  
Cooperative System ◽  
Formal Organization ◽  
Interorganizational Communication ◽  
The Subject

This chapter deals with roles and ways of interorganizational communication systems. Prior to the subject, author refers to three topics related to the subject: what are interorganizational relationships, strategic alliances, and interorganizational networks. In order to understand interorganizational networks, the author utilizes Barnard’s theory of cooperative system and formal organization, in which communication plays a significant role to formulate and to maintain interorganizational networks as organization of organizations. Furthermore, there are some problems for effective interorganizational communication systems – both human and machine: standardization of communication systems, impact of IT development, and interorganizational strategy using IT.

Can we be friends with Mitsuku? A longitudinal study on the process of relationship formation between humans and a social chatbot

2020 ◽  
Vol 38 (1) ◽  
pp. 279-300
Author(s):  
Emmelyn A. J. Croes ◽  
Marjolijn L. Antheunis
Keyword(s):  
Longitudinal Study ◽  
Social Processes ◽  
Communication Research ◽  
Relationship Formation ◽  
Social Attraction ◽  
Self Disclosure ◽  
Abcde Model ◽  
The Social ◽  
Machine Communication ◽  
The Relationship

This explorative study investigated (a) whether social attraction, self-disclosure, interaction quality, intimacy, empathy and communicative competence play a role in getting-acquainted interactions between humans and a chatbot, and (b) whether humans can build a relationship with a chatbot. Although human-machine communication research suggests that humans can develop feelings for computers, this does not automatically imply that humans experience feelings of friendship with a chatbot. In this longitudinal study, 118 participants had seven interactions with chatbot Mitsuku over a 3-week period. After each interaction participants filled out a questionnaire. The results showed that the social processes decreased after each interaction and feelings of friendship were low. In line with the ABCDE model of relationship development, the social processes that aid relationship continuation decrease, leading to deterioration of the relationship. Furthermore, a novelty effect was at play after the first interaction, after which the chatbot became predictable and the interactions less enjoyable.

scholarly journals Power-law distribution of degree–degree distance: A better representation of the scale-free property of complex networks

2020 ◽  
Vol 117 (26) ◽  
pp. 14812-14818 ◽  
Author(s):  
Bin Zhou ◽  
Xiangyi Meng ◽  
H. Eugene Stanley
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Power Law ◽  
Real World ◽  
Preferential Attachment ◽  
Statistical Tests ◽  
Finite Size ◽  
Distance Distribution ◽  
Scale Free ◽  
Degree Distance

Whether real-world complex networks are scale free or not has long been controversial. Recently, in Broido and Clauset [A. D. Broido, A. Clauset,Nat. Commun.10, 1017 (2019)], it was claimed that the degree distributions of real-world networks are rarely power law under statistical tests. Here, we attempt to address this issue by defining a fundamental property possessed by each link, the degree–degree distance, the distribution of which also shows signs of being power law by our empirical study. Surprisingly, although full-range statistical tests show that degree distributions are not often power law in real-world networks, we find that in more than half of the cases the degree–degree distance distributions can still be described by power laws. To explain these findings, we introduce a bidirectional preferential selection model where the link configuration is a randomly weighted, two-way selection process. The model does not always produce solid power-law distributions but predicts that the degree–degree distance distribution exhibits stronger power-law behavior than the degree distribution of a finite-size network, especially when the network is dense. We test the strength of our model and its predictive power by examining how real-world networks evolve into an overly dense stage and how the corresponding distributions change. We propose that being scale free is a property of a complex network that should be determined by its underlying mechanism (e.g., preferential attachment) rather than by apparent distribution statistics of finite size. We thus conclude that the degree–degree distance distribution better represents the scale-free property of a complex network.

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