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Journal articles on the topic 'Endogenous diffusion social networks'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Pavan, Elena. "Embedding Digital Communications Within Collective Action Networks: A Multidimensional Network Approach." Mobilization: An International Quarterly 19, no. 4 (2014): 441–55. http://dx.doi.org/10.17813/maiq.19.4.w24rl524u074126k.

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In this article, we conceive of digital media as embedded within social networks, and use this perspective to examine the role of online communications in collective action. We claim that the adoption of this perspective requires two shifts: first, rethinking the ontological separation between media and social networks of action that has, so far, characterized research in this domain; second, the adoption of flexible tools that enable us to account, simultaneously, for the multiplicity of relations underpinning collective efforts and the hybrid interplay between direct and technology-mediated

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2

Chang, Myong‐Hun, and Joseph E. Harrington, Jr. "Discovery and Diffusion of Knowledge in an Endogenous Social Network." American Journal of Sociology 110, no. 4 (2005): 937–76. http://dx.doi.org/10.1086/426555.

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3

Goldberg, Amir, and Sarah K. Stein. "Beyond Social Contagion: Associative Diffusion and the Emergence of Cultural Variation." American Sociological Review 83, no. 5 (2018): 897–932. http://dx.doi.org/10.1177/0003122418797576.

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Network models of diffusion predominantly think about cultural variation as a product of social contagion. But culture does not spread like a virus. We propose an alternative explanation we call associative diffusion. Drawing on two insights from research in cognition—that meaning inheres in cognitive associations between concepts, and that perceived associations constrain people’s actions—we introduce a model in which, rather than beliefs or behaviors, the things being transmitted between individuals are perceptions about what beliefs or behaviors are compatible with one another. Conventional

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4

Koley, Paramita, Avirup Saha, Sourangshu Bhattacharya, Niloy Ganguly, and Abir De. "Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach." ACM Transactions on Knowledge Discovery from Data 15, no. 6 (2021): 1–25. http://dx.doi.org/10.1145/3449361.

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The networked opinion diffusion in online social networks is often governed by the two genres of opinions— endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news and feeds. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this article, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize

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5

Wejnert, Barbara. "Diffusion, Development, and Democracy, 1800-1999." American Sociological Review 70, no. 1 (2005): 53–81. http://dx.doi.org/10.1177/000312240507000104.

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While a trend of growth in democratization over the past two centuries has been generally observed, it is the remarkable growth in the democratization of the world over the past 30 years that has truly captured the imagination of social scientists, policymakers, and the general public alike. Two major sets of factors have dominated studies attempting to predict democratization. One set characterizes endogenous or internal features of countries, and may be referred to as socioeconomic development. The other set, less often tested, characterizes exogenous variables that influence democratization

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6

Everall, Jordan P., Fabian Tschofenig, Jonathan F. Donges, and Ilona M. Otto. "The Pareto effect in tipping social networks: from minority to majority." Earth System Dynamics 16, no. 1 (2025): 189–214. https://doi.org/10.5194/esd-16-189-2025.

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Abstract. How do social networks tip? A popular theory is that a small minority can trigger population-wide social change. This aligns with the Pareto principle, a semi-quantitative law which suggests that, in many systems, 80 % of effects arise from 20 % of the causes. In the context of the transition to net-zero emissions, this vital 20 % can be a critical instigator of social tipping, a process which can rapidly change social norms. In this work, we asked whether the Pareto effect can be observed in social systems by conducting a literature review, placing a focus on social norm diffusion a

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Bartesaghi, Paolo, Gian Paolo Clemente, and Rosanna Grassi. "A Self-Adaptive Centrality Measure for Asset Correlation Networks." Economies 12, no. 7 (2024): 164. http://dx.doi.org/10.3390/economies12070164.

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We propose a new centrality measure based on a self-adaptive epidemic model characterized by an endogenous reinforcement mechanism in the transmission of information between nodes. We provide a strategy to assign to nodes a centrality score that depends, in an eigenvector centrality scheme, on that of all the elements of the network, nodes and edges, connected to it. We parameterize this score as a function of a reinforcement factor, which for the first time implements the intensity of the interaction between the network of nodes and that of the edges. In this proposal, a local centrality meas

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8

Huang, Hung-Chun, and Hsin-Yu Shih. "Exploring the structure of international technology diffusion." Foresight 16, no. 3 (2014): 210–28. http://dx.doi.org/10.1108/fs-11-2012-0085.

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Purpose – This paper aims to provide a macro perspective on diffusion structure research, and to investigate the deep structure of international technology diffusion and structural differences between technology diffusion networks. This work also provides an understanding of the nature of globalization. Globalization has highlighted changes in socioeconomics and is reshaping the world. However, when comparing endogenous factors, exogenous factors are complex and demonstrate themselves as network phenomena. These network phenomena compose themselves as neither sole nor independent units. Countr

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Ioannidis, Evangelos, Nikos Varsakelis, and Ioannis Antoniou. "Promoters versus Adversaries of Change: Agent-Based Modeling of Organizational Conflict in Co-Evolving Networks." Mathematics 8, no. 12 (2020): 2235. http://dx.doi.org/10.3390/math8122235.

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The social adoption of change is usually hard because in reality, forces opposing the social adoption of change manifest. This situation of organizational conflict corresponds to the case where two competing groups of influential agents (“promoters” versus “adversaries” of change) operate concurrently within the same organizational network. We model and explore the co-evolution of interpersonal ties and attitudes in the presence of conflict, taking into account explicitly the microscopic “agent-to-agent” interactions. In this perspective, we propose a new ties-attitudes co-evolution model wher

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10

Hojman, Daniel A., and Adam Szeidl. "Endogenous networks, social games, and evolution." Games and Economic Behavior 55, no. 1 (2006): 112–30. http://dx.doi.org/10.1016/j.geb.2005.02.007.

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11

Christozov, Dimitar, and Stefka Toleva-Stoimenova. "Knowledge Diffusion via Social Networks." International Journal of Digital Literacy and Digital Competence 4, no. 2 (2013): 1–12. http://dx.doi.org/10.4018/jdldc.2013040101.

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The paper focuses on the phenomenon of social networks and their role in the process of knowledge diffusion. Social networks define the structure of a population of individuals. Diverse and dynamic environments lead to evolution of social networks as informing media. The Internet revolution affected especially the way people communicate and it naturally produced a new infrastructure for maintaining social networks. Different topologies of social networks are considered as different paths of knowledge diffusion. The paper addresses the challenges and opportunities this new infrastructure provid

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12

Agrawal, Divyakant, Ceren Budak, and Amr El Abbadi. "Information diffusion in social networks." Proceedings of the VLDB Endowment 4, no. 12 (2011): 1512–13. http://dx.doi.org/10.14778/3402755.3402811.

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13

D’Agostino, Gregorio, Fulvio D’Antonio, Antonio De Nicola, and Salvatore Tucci. "Interests diffusion in social networks." Physica A: Statistical Mechanics and its Applications 436 (October 2015): 443–61. http://dx.doi.org/10.1016/j.physa.2015.05.062.

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14

López-Pintado, Dunia. "Diffusion in complex social networks." Games and Economic Behavior 62, no. 2 (2008): 573–90. http://dx.doi.org/10.1016/j.geb.2007.08.001.

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15

Adriani, Fabrizio, and Dan Ladley. "ENDOGENOUS SOCIAL DISTANCING AND CONTAINMENT POLICIES IN SOCIAL NETWORKS." National Institute Economic Review 257 (2021): 101–17. http://dx.doi.org/10.1017/nie.2021.20.

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Can smart containment policies crowd out private efforts at social distancing? We analyse this question from the perspective of network formation theory. We focus in particular on the role of externalities in social distancing choices. We also look at how these choices are affected by factors such as the agents’ risk perception, the speed of the policy intervention, the structure of the underlying network and the presence of strategic complementarities. We argue that crowding out is a problem when the probability that an outbreak may spread undetected is relatively high (either because testing

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16

Fogli, Alessandra, and Laura Veldkamp. "Germs, Social Networks, and Growth." Review of Economic Studies 88, no. 3 (2021): 1074–100. http://dx.doi.org/10.1093/restud/rdab008.

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Abstract Does the pattern of social connections between individuals matter for macroeconomic outcomes? If so, where do differences in these patterns come from and how large are their effects? Using network analysis tools, we explore how different social network structures affect technology diffusion and thereby a country’s rate of growth. The correlation between high-diffusion networks and income is strongly positive. But when we use a model to isolate the effect of a change in social networks on growth, the effect can be positive, negative, or zero. The reason is that networks diffuse both id

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17

Valente, Thomas W., and George G. Vega Yon. "Diffusion/Contagion Processes on Social Networks." Health Education & Behavior 47, no. 2 (2020): 235–48. http://dx.doi.org/10.1177/1090198120901497.

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This study models how new ideas, practices, or diseases spread within and between communities, the diffusion of innovations or contagion. Several factors affect diffusion such as the characteristics of the initial adopters, the seeds; the structure of the network over which diffusion occurs; and the shape of the threshold distribution, which is the proportion of prior adopting peers needed for the focal individual to adopt. In this study, seven seeding conditions are modeled: (1) three opinion leadership indicators, (2) two bridging measures, (3) marginally positioned seeds, and (4) randomly s

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18

HU, HAI-BO, CANG-HAI LI, and QING-YING MIAO. "OPINION DIFFUSION ON MULTILAYER SOCIAL NETWORKS." Advances in Complex Systems 20, no. 06n07 (2017): 1750015. http://dx.doi.org/10.1142/s0219525917500151.

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In this paper, to reveal the influence of multilayer network structure on opinion diffusion in social networks, we study an opinion dynamics model based on DeGroot model on multilayer networks. We find that if the influence matrix integrating the information of connectedness for each layer and correlation between layers is strongly connected and aperiodic, all agents’ opinions will reach a consensus. However, if there are stubborn agents in the networks, regular agents’ opinions will finally be confined to the convex combinations of the stubborn agents’. Specifically, if all stubborn agents ho

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19

Avgerou, Artemis D., and Yannis C. Stamatiou. "Privacy Awareness Diffusion in Social Networks." IEEE Security & Privacy 13, no. 6 (2015): 44–50. http://dx.doi.org/10.1109/msp.2015.136.

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20

Kreindler, G. E., and H. P. Young. "Rapid innovation diffusion in social networks." Proceedings of the National Academy of Sciences 111, Supplement_3 (2014): 10881–88. http://dx.doi.org/10.1073/pnas.1400842111.

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21

Hu, Haibo. "Competing opinion diffusion on social networks." Royal Society Open Science 4, no. 11 (2017): 171160. http://dx.doi.org/10.1098/rsos.171160.

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Opinion competition is a common phenomenon in real life, such as with opinions on controversial issues or political candidates; however, modelling this competition remains largely unexplored. To bridge this gap, we propose a model of competing opinion diffusion on social networks taking into account degree-dependent fitness or persuasiveness. We study the combined influence of social networks, individual fitnesses and attributes, as well as mass media on people’s opinions, and find that both social networks and mass media act as amplifiers in opinion diffusion, the amplifying effect of which c

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22

Guille, Adrien, Hakim Hacid, Cecile Favre, and Djamel A. Zighed. "Information diffusion in online social networks." ACM SIGMOD Record 42, no. 2 (2013): 17–28. http://dx.doi.org/10.1145/2503792.2503797.

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23

Acemoglu, Daron, Kostas Bimpikis, and Asuman Ozdaglar. "Dynamics of information exchange in endogenous social networks." Theoretical Economics 9, no. 1 (2014): 41–97. http://dx.doi.org/10.3982/te1204.

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24

Luu, Duc, Ee-Peng Lim, Tuan-Anh Hoang, and Freddy Chua. "Modeling Diffusion in Social Networks Using Network Properties." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (2021): 218–25. http://dx.doi.org/10.1609/icwsm.v6i1.14259.

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Diffusion of items occurs in social networks due to spreading of items through word of mouth and exogenous factors. These items may be news, products, videos, advertisements or contagious viruses. Previous research has studied diffusion process at both the macro and micro levels. The former models the number of item adopters in the diffusion process while the latter determines which individuals adopt item. In this paper, we establish a general probabilistic framework, which can be used to derive macro-level diffusion models, including the well known Bass Model (BM). Using this framework, we de

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25

Kim, Minkyoung, David Newth, and Peter Christen. "Modeling Dynamics of Diffusion Across Heterogeneous Social Networks: News Diffusion in Social Media." Entropy 15, no. 12 (2013): 4215–42. http://dx.doi.org/10.3390/e15104215.

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26

Yichuan Jiang and J. C. Jiang. "Diffusion in Social Networks: A Multiagent Perspective." IEEE Transactions on Systems, Man, and Cybernetics: Systems 45, no. 2 (2015): 198–213. http://dx.doi.org/10.1109/tsmc.2014.2339198.

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27

Centola, Damon. "The Social Origins of Networks and Diffusion." American Journal of Sociology 120, no. 5 (2015): 1295–338. http://dx.doi.org/10.1086/681275.

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28

Ok, Jungseul, Youngmi Jin, Jinwoo Shin, and Yung Yi. "On maximizing diffusion speed in social networks." ACM SIGMETRICS Performance Evaluation Review 42, no. 1 (2014): 301–13. http://dx.doi.org/10.1145/2637364.2591991.

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29

Christoff, Zoé, and Jens Ulrik Hansen. "A logic for diffusion in social networks." Journal of Applied Logic 13, no. 1 (2015): 48–77. http://dx.doi.org/10.1016/j.jal.2014.11.011.

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30

Naumov, Pavel, and Jia Tao. "Marketing impact on diffusion in social networks." Journal of Applied Logic 20 (March 2017): 49–74. http://dx.doi.org/10.1016/j.jal.2016.11.034.

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31

Christoff, Zoé, and Pavel Naumov. "Diffusion in social networks with recalcitrant agents." Journal of Logic and Computation 29, no. 1 (2018): 53–70. http://dx.doi.org/10.1093/logcom/exy037.

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32

Lu, Zongqing, Yonggang Wen, Weizhan Zhang, Qinghua Zheng, and Guohong Cao. "Towards Information Diffusion in Mobile Social Networks." IEEE Transactions on Mobile Computing 15, no. 5 (2016): 1292–304. http://dx.doi.org/10.1109/tmc.2015.2451624.

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33

Long, Cheng, Anhua Chen, Pakawadee Pengcharoen, and Raymond Chi-Wing Wong. "On optimal preference diffusion over social networks." Information Systems 88 (February 2020): 101441. http://dx.doi.org/10.1016/j.is.2019.101441.

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34

Lobel, Ilan, and Evan Sadler. "Information diffusion in networks through social learning." Theoretical Economics 10, no. 3 (2015): 807–51. http://dx.doi.org/10.3982/te1549.

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35

Li, Pei, Yini Zhang, Fengcai Qiao, and Hui Wang. "Information diffusion in structured online social networks." Modern Physics Letters B 29, no. 13 (2015): 1550063. http://dx.doi.org/10.1142/s0217984915500633.

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Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize

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36

Sharma, Devyani, and Robin Dodsworth. "Language Variation and Social Networks." Annual Review of Linguistics 6, no. 1 (2020): 341–61. http://dx.doi.org/10.1146/annurev-linguistics-011619-030524.

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The close relationship between language variation and the nature of social ties among people has been the focus of long-standing commentary in linguistics. A central puzzle in this relationship is the seeming contradiction between two bodies of evidence: automatic, mechanistic diffusion of linguistic forms through social networks and ideologically mediated choice in uptake of forms. Nearly a century of research has revealed that certain types of network structure facilitate the diffusion of linguistic innovation, but these network structures are always anchored in temporally specific and ideol

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37

Lazzati, Natalia. "Codiffusion of Technologies in Social Networks." American Economic Journal: Microeconomics 12, no. 4 (2020): 193–228. http://dx.doi.org/10.1257/mic.20180220.

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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? (

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38

Board, Simon, and Moritz Meyer-ter-Vehn. "Experimentation in Networks." American Economic Review 114, no. 9 (2024): 2940–80. http://dx.doi.org/10.1257/aer.20230233.

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We propose a model of strategic experimentation on social networks in which forward-looking agents learn from their own and neighbors’ successes. In equilibrium, private discovery is followed by social diffusion. Social learning crowds out own experimentation, so total information decreases with network density; we determine density thresholds below which agents’ asymptotic learning is perfect. By contrast, agent welfare is single peaked in network density and achieves a second-best benchmark level at intermediate levels that strike a balance between discovery and diffusion. (JEL D82, D83, D86

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39

Yudai Arai, Tomoko Kajiyama, and Noritomo Ouchi. "Impact of Social Networks on Diffusion of Products." Journal of Technology Management for Growing Economies 5, no. 1 (2014): 35–50. http://dx.doi.org/10.15415/jtmge.2014.51002.

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In light of the rapid growth of social networks around the world, this study analyses the impact of social networks on the diffusion of products and demonstrates the effective way to diffuse products in the society where social networks play an important role. We construct a consumer behaviour model by multi-agent simulation taking the movie market as an example. After validating it by using data from 13 US movies, we conduct simulations. Our simulation results show that the impact of social networks on the diffusion differs according to the customers’ expectations and evaluation for a movie.

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40

Estrada, Pablo, Juan Estrada, Kim P. Huynh, David Jacho-Chávez, and Leonardo Sánchez-Aragón. "netivreg: Estimation of peer effects in endogenous social networks." Stata Journal: Promoting communications on statistics and Stata 25, no. 2 (2025): 344–73. https://doi.org/10.1177/1536867x251341145.

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The command netivreg implements the generalized three-stage least-squares estimator developed in Estrada (2022, Causal inference in multilayered networks, PhD thesis) and the generalized method of moments estimator in Chan et al. (2024, Journal of Econometric Methods 13: 205-224) for the endogenous linear-in-means model. The two procedures use full observability of a two-layered multiplex network data structure using Stata’s new multiframes capabilities and Python integration (version 16 and above). Applications of the command include simulated data and three years’ worth of data on peer-revie

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41

HERRMANN-PILLATH, CARSTEN. "Endogenous regionalism." Journal of Institutional Economics 2, no. 3 (2006): 297–318. http://dx.doi.org/10.1017/s1744137406000427.

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For more than a decade regionalism has been on the rise in the global economy. Based on the concept of allocative efficiency, standard trade theory regards regionalism as a form of protectionism. The paper confronts this view with an institutionalist explanation and draws on recent research on the role of specific investments into international market access, uncertainty and asymmetric information in policy coordination. A distinction between regionalism and regionalist policies is proposed. Endogenous regionalism reflects the economic forces of path-dependent comparative advantage and manifes

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42

Ahsan, Mohammad, Madhu Kumari, Tajinder Singh, and Triveni Lal Pal. "Sentiment Based Information Diffusion in Online Social Networks." International Journal of Knowledge Discovery in Bioinformatics 8, no. 1 (2018): 60–74. http://dx.doi.org/10.4018/ijkdb.2018010105.

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This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e.

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43

Mozafari, Niloofar, and Ali Hamzeh. "An enriched social behavioural information diffusion model in social networks." Journal of Information Science 41, no. 3 (2015): 273–83. http://dx.doi.org/10.1177/0165551514565318.

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44

Gupta, Ishika, Prakashan Chellattan Veettil, and Stijn Speelman. "Caste, Social Networks and Variety Adoption." Journal of South Asian Development 15, no. 2 (2020): 155–83. http://dx.doi.org/10.1177/0973174120954632.

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Social networks influence technology diffusion but targeting formal leaders (institutional central nodes) may lead to distributional consequences. This paper analyzes the role of informal social networks in technology diffusion in a socially hierarchical caste-based society. Often, information flow and technology diffusion are constrained by social and economic boundaries where informal nodes such as caste play a very decisive role in everyday life. Proper targeting and dissemination of technology to the marginalized sections of society are very important for their development. We observed tha

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45

Rogowski, Jon C., and Betsy Sinclair. "Estimating the Causal Effects of Social Interaction with Endogenous Networks." Political Analysis 20, no. 3 (2012): 316–28. http://dx.doi.org/10.1093/pan/mps016.

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Identifying causal effects attributable to network membership is a key challenge in empirical studies of social networks. In this article, we examine the consequences of endogeneity for inferences about the effects of networks on network members' behavior. Using the House office lottery (in which newly elected members select their office spaces in a randomly chosen order) as an instrumental variable to estimate the causal impact of legislative networks on roll call behavior and cosponsorship decisions in the 105th–112th Houses, we find no evidence that office proximity affects patterns of legi

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46

Bedayo, Mikel, Ana Mauleon, and Vincent Vannetelbosch. "Bargaining in endogenous trading networks." Mathematical Social Sciences 80 (March 2016): 70–82. http://dx.doi.org/10.1016/j.mathsocsci.2016.02.007.

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47

Zhang, Hangjing, Yan Chen, and H. Vicky Zhao. "Evolutionary information dynamics over social networks: a review." International Journal of Crowd Science 4, no. 1 (2020): 45–59. http://dx.doi.org/10.1108/ijcs-09-2019-0026.

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Purpose The purpose of this paper is to have a review on the analysis of information diffusion based on evolutionary game theory. People now get used to interact over social networks, and one of the most important functions of social networks is information sharing. Understanding the mechanisms of the information diffusion over social networks is critical to various applications including online advertisement and rumor control. Design/methodology/approach It has been shown that the graphical evolutionary game theory (EGT) is a very efficient method to study this problem. Findings By applying E

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48

Zhang, Chuangchuang, Wenlin Cheng, Fuliang Li, and Xingwei Wang. "Hypergraph-Based Influence Maximization in Online Social Networks." Mathematics 12, no. 17 (2024): 2769. http://dx.doi.org/10.3390/math12172769.

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Influence maximization in online social networks is used to select a set of influential seed nodes to maximize the influence spread under a given diffusion model. However, most existing proposals have huge computational costs and only consider the dyadic influence relationship between two nodes, ignoring the higher-order influence relationships among multiple nodes. It limits the applicability and accuracy of existing influence diffusion models in real complex online social networks. To this end, in this paper, we present a novel information diffusion model by introducing hypergraph theory to

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49

Arnaboldi, Valerio, Marco Conti, Andrea Passarella, and Robin I. M. Dunbar. "Online Social Networks and information diffusion: The role of ego networks." Online Social Networks and Media 1 (June 2017): 44–55. http://dx.doi.org/10.1016/j.osnem.2017.04.001.

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KIM, Kwanho, Jae-Yoon JUNG, and Jonghun PARK. "Discovery of Information Diffusion Process in Social Networks." IEICE Transactions on Information and Systems E95.D, no. 5 (2012): 1539–42. http://dx.doi.org/10.1587/transinf.e95.d.1539.

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