Codiffusion of Technologies in Social Networks

2020 ◽  
Vol 12 (4) ◽  
pp. 193-228
Author(s):  
Natalia Lazzati
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Diffusion Process ◽  
Technology Diffusion ◽  
Consideration Sets ◽  
Network Structures ◽  
The Social ◽  
Over Time

This paper studies the diffusion process of two complementary technologies among people who are connected through a social network. It characterizes adoption rates over time for different initial allocations and network structures. In doing so, we provide some microfoundations for the stochastic formation of consideration sets. We are particularly interested in the following question: suppose we want to maximize technology diffusion and have a limited number of units of each of the two technologies to initially distribute—how should we allocate these units among people in the social network? (JEL D83, O33, Z13)

scholarly journals Election Control in Social Networks via Edge Addition or Removal

2020 ◽  
Vol 34 (02) ◽  
pp. 1878-1885
Author(s):  
Matteo Castiglioni ◽  
Diodato Ferraioli ◽  
Nicola Gatti
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Positive Result ◽  
Optimization Problem ◽  
Optimal Solution ◽  
The Social ◽  
Wide Range ◽  
Local Modification ◽  
Edge Addition ◽  
Over Time

We focus on the scenario in which messages pro and/or against one or multiple candidates are spread through a social network in order to affect the votes of the receivers. Several results are known in the literature when the manipulator can make seeding by buying influencers. In this paper, instead, we assume the set of influencers and their messages to be given, and we ask whether a manipulator (e.g., the platform) can alter the outcome of the election by adding or removing edges in the social network. We study a wide range of cases distinguishing for the number of candidates or for the kind of messages spread over the network. We provide a positive result, showing that, except for trivial cases, manipulation is not affordable, the optimization problem being hard even if the manipulator has an unlimited budget (i.e., he can add or remove as many edges as desired). Furthermore, we prove that our hardness results still hold in a reoptimization variant, where the manipulator already knows an optimal solution to the problem and needs to compute a new solution once a local modification occurs (e.g., in bandit scenarios where estimations related to random variables change over time).

scholarly journals Social Networks and Social Interaction among People with Psychiatric Disabilities-Comparison of Users of Day Centres and Clubhouses

2016 ◽  
Vol 9 (6) ◽  
pp. 107 ◽  
Author(s):  
Jenny Hultqvist ◽  
Urban Markström ◽  
Carina Tjörnstrand ◽  
Mona Eklund
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interaction ◽  
Social Functioning ◽  
Social Engagement ◽  
Self Esteem ◽  
Psychiatric Disabilities ◽  
The Social ◽  
Over Time

OBJECTIVE: The aim of the study was to compare users of community-based mental health day centres (DCs) and clubhouses in Sweden regarding reported social networks and social interaction and the stability of these over time. A further aim was to investigate social network predictors both cross-sectionally and longitudinally.METHODS: People regularly attending DCs (n=128) or clubhouses (n=57) completed questionnaires about social network and social interaction (social engagement and social functioning), self-esteem and socio-demographics at baseline and a nine-month follow-up. RESULTS: Perceived social engagement and social functioning did not differ between the groups and remained stable over time. Fewer in the DC reported having a close friend but there was no difference regarding having recently (the past week) seen a friend. When naming “someone with whom you can share your innermost thoughts and feelings”, the DC group named more professional contacts, fewer friends and more often “nobody” compared to the clubhouse group. Finally, on both occasions the DC group scored significantly lower on size of the social network compared to the clubhouse users. Self-esteem and having recently seen a friend could predict size of the social network in the cross-sectional perspective. Strong indicators of belonging to the group with a larger social network at follow-up were being a woman, attending a clubhouse programme and having scored high on social network at baseline.CONCLUSION & IMPLICATION FOR PRACTICE: Having friends and strengthening one’s self-esteem may be essential factors for the social network of people with psychiatric disabilities in a short-term perspective. Visiting clubhouses seems advantageous in a longer-term perspective.

Networks of Co-Authorship

2012 ◽  
pp. 981-994
Author(s):  
Antonio José Caulliraux Pithon ◽  
Ralfh Varges Ansuattigui ◽  
Paulo Enrique Stecklow
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Morphological Characteristics ◽  
Network Structures ◽  
Communication Processes ◽  
The Social ◽  
The Subject

The networks are transorganizational arrangements forming a structure and, in a more abstract and generic manner, are built from the interactions between individuals and organizations. These interactions allow the emergence of network structures more related to personal ties and the types of existing social interactions between the actors. Social networks aren’t a recent enterprise, but have been the subject of deeper studies due to universalization and convergence of communication processes, fundamental to the establishment and proliferation of networks. The structure where networks are manifested calls for horizontality, where there is no formal hierarchy of the elements that comprise it, composed by nodes elements and lines elements. This article analyzes the social network of authorship of one of five Postgraduate Programs of CEFET/RJ, presenting the connections between network teachers, justifying the morphological characteristics of the network and suggesting methodologies for continuing the study for the teaching and researching networks.

scholarly journals Networked innovation in the health sector: comparative qualitative study of the role of Collaborations for Leadership in Applied Health Research and Care in translating research into practice

2014 ◽  
Vol 2 (13) ◽  
pp. 1-128 ◽  
Author(s):  
Harry Scarbrough ◽  
Daniela D’Andreta ◽  
Sarah Evans ◽  
Marco Marabelli ◽  
Sue Newell ◽  
...  
Keyword(s):  
Health Care ◽  
Social Networks ◽  
Social Network ◽  
Research Methods ◽  
Health Research ◽  
Health Sector ◽  
Network Structures ◽  
The Uk ◽  
Applied Health Research ◽  
Over Time

BackgroundCollaborations for Leadership in Applied Health Research and Care (CLAHRCs) were an initiative of the National Institute for Health Research in response to a new research and development strategy in the NHS: ‘Best Research for Best Health’. They were designed to address the ‘second gap in translation’ identified by the Cooksey review; namely, the need to improve health care in the UK by translating clinical research into practice more effectively. Nine CLAHRCs, each encompassing a university in partnership with local NHS bodies, were funded over the period 2008–13.AimsThe aim of this report is to provide an independent and theory-based evaluation of CLAHRCs as a new form of networked innovation in the health sector. This evaluation is based on an intensive research study involving three CLAHRCs in the UK and three international organisations (one in the USA and two in Canada). This study was carried out over two overlapping time phases so as to capture changes in the CLAHRCs over time. Networked innovation in the health sector is conceptualised as involving the translation of knowledge via informal social networks.MethodsA mix of research methods was used to help ensure the validity and generalisability of the study. These methods addressed the development of each CLAHRC over time, over multiple levels of analysis, and with particular reference to the translation of knowledge across the groups involved, and the quality of the informal underpinning network ties that supported such translation. Research methods, therefore, included a qualitative enquiry based on case studies and case analysis, cognitive mapping methods, and social network analysis.FindingsThrough our study, we found that each one of our samples of CLAHRCs appropriated the CLAHRC idea in a particular way, depending on their different interpretations or ‘visions’ of the CLAHRC’s role in knowledge translation (KT), and different operating models of how such visions could be achieved. These helped to shape the development of social networks (centralised vs. decentralised) and each CLAHRC’s approach to KT activity (‘bridging’ vs. ‘blurring’ the boundaries between professional groups). Through a comparative analysis, we develop an analytical model of the resultant capabilities which each case, including our international sites, developed for undertaking innovation, encompassing a combination of both ‘integrative capability’ (the ability to move back and forth between scientific evidence and practical application) and ‘relational capability’ (the ability of groups and organisations to work together). This extends previous models of KT by highlighting the effects of leadership and management, and the emergence of social network structures. We further highlight the implications of this analysis for policy and practice by discussing how network structures and boundary-spanning roles and activities can be tailored to different KT objectives.ConclusionsDifferent interpretations and enactments of the CLAHRC mission ultimately led to differing capabilities for KT among our studied initiatives. Further research could usefully explore how these different capabilities are produced, and how they may be more or less appropriate for particular national health-care settings, with a view to improving the design blueprint for future KT initiatives.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

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.

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.

Comparing the social networks of service users with long term mental health needs living in community with those in a general adult in-patient unit

2021 ◽  
pp. 002076402110175
Author(s):  
Roberto Rusca ◽  
Ike-Foster Onwuchekwa ◽  
Catherine Kinane ◽  
Douglas MacInnes
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Social Network ◽  
Patient Group ◽  
Mental Health Problems ◽  
Well Being ◽  
Health Problems ◽  
Practical Support ◽  
The Social

Background: Relationships are vital to recovery however, there is uncertainty whether users have different types of social networks in different mental health settings and how these networks may impact on users’ wellbeing. Aims: To compare the social networks of people with long-term mental illness in the community with those of people in a general adult in-patient unit. Method: A sample of general adult in-patients with enduring mental health problems, aged between 18 and 65, was compared with a similar sample attending a general adult psychiatric clinic. A cross-sectional survey collected demographic data and information about participants’ social networks. Participants also completed the Short Warwick Edinburgh Mental Well-Being Scale to examine well-being and the Significant Others Scale to explore their social network support. Results: The study recruited 53 participants (25 living in the community and 28 current in-patients) with 339 named as important members of their social networks. Both groups recorded low numbers in their social networks though the community sample had a significantly greater number of social contacts (7.4 vs. 5.4), more monthly contacts with members of their network and significantly higher levels of social media use. The in-patient group reported greater levels of emotional and practical support from their network. Conclusions: People with serious and enduring mental health problems living in the community had a significantly greater number of people in their social network than those who were in-patients while the in-patient group reported greater levels of emotional and practical support from their network. Recommendations for future work have been made.

scholarly journals The switching mechanisms of social network densification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teruyoshi Kobayashi ◽  
Mathieu Génois
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Social Interactions ◽  
Regime Switching ◽  
Face To Face ◽  
The Social ◽  
Switching Property ◽  
Distinctive Type ◽  
Insight Into

AbstractDensification and sparsification of social networks are attributed to two fundamental mechanisms: a change in the population in the system, and/or a change in the chances that people in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

Social Networks, Their Basic Factors, as Well as Their Role in Society and the State in the Context of Digital Transformation

2020 ◽  
Vol 144 ◽  
pp. 26-35
Author(s):  
Rem V. Ryzhov ◽  
◽  
Vladimir A. Ryzhov ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Life Support ◽  
Social Institutions ◽  
The State ◽  
Main Task ◽  
Organizational Models ◽  
Control Center ◽  
The Social ◽  
Single Strategy

Society is historically associated with the state, which plays the role of an institution of power and government. The main task of the state is life support, survival, development of society and the sovereignty of the country. The main mechanism that the state uses to implement these functions is natural social networks. They permeate every cell of society, all elements of the country and its territory. However, they can have a control center, or act on the principle of self-organization (network centrism). The web is a universal natural technology with a category status in science. The work describes five basic factors of any social network, in particular the state, as well as what distinguishes the social network from other organizational models of society. Social networks of the state rely on communication, transport and other networks of the country, being a mechanism for the implementation of a single strategy and plan. However, the emergence of other strong network centers of competition for state power inevitably leads to problems — social conflicts and even catastrophes in society due to the destruction of existing social institutions. The paper identifies the main pitfalls using alternative social networks that destroy the foundations of the state and other social institutions, which leads to the loss of sovereignty, and even to the complete collapse of the country.

Factors Affecting Individual Level ERP Assimilation in a Social Network Perspective

2017 ◽  
Vol 25 (3) ◽  
pp. 21-39 ◽  
Author(s):  
Luan Gao ◽  
Luning Liu ◽  
Yuqiang Feng
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Interactive Learning ◽  
Influential Factors ◽  
Three Dimensions ◽  
Organizational Level ◽  
Key Factors ◽  
Factors Affecting ◽  
Individual Level ◽  
The Social

Prior research on ERP assimilation has primarily focused on influential factors at the organizational level. In this study, the authors attempt to extend their understanding of individual level ERP assimilation from the perspective of social network theory. They designed a multi-case study to explore the relations between ERP users' social networks and their levels of ERP assimilation based on the three dimensions of the social networks. The authors gathered data through interviews with 26 ERP users at different levels in five companies. Qualitative analysis was used to understand the effects of social networks and interactive learning. They found that users' social networks play a significant role in individual level ERP assimilation through interactive learning among users. They also found five key factors that facilitate users' assimilation of ERP knowledge: homophily (age, position and rank), tie content (instrumental and expressive ties), tie strength, external ties, and centrality.

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