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Journal articles on the topic 'Endogenous diffusion social networks'

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Author: Grafiati

Published: 14 March 2023

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1

Goldberg, Amir, and Sarah K. Stein. "Beyond Social Contagion: Associative Diffusion and the Emergence of Cultural Variation." American Sociological Review 83, no. 5 (September 14, 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 contagion models require the assumption that networks are segregated to explain cultural variation. We show, in contrast, that the endogenous emergence of cultural differentiation can be entirely attributable to social cognition and does not require a segregated network or a preexisting division into groups. Moreover, we show that prevailing assumptions about the effects of network topology do not hold when diffusion is associative.

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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 (January 2005): 937–76. http://dx.doi.org/10.1086/426555.

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3

Pavan, Elena. "Embedding Digital Communications Within Collective Action Networks: A Multidimensional Network Approach." Mobilization: An International Quarterly 19, no. 4 (December 1, 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 interactions. After discussing the necessity and the implications of considering communication technologies as endogenous to social networks of collective action, we introduce multidimensional networks (MDNs) as a suitable perspective to advance the application of a relational approach to the study of collective action, thus meeting the challenges posed by the diffusion of interactive and networking digital media.

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4

Huang, Hung-Chun, and Hsin-Yu Shih. "Exploring the structure of international technology diffusion." Foresight 16, no. 3 (June 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. Countries in the global network act interdependently, and heavily influence one another. Design/methodology/approach – This study utilizes social network analysis to investigate the structural configuration of international technology diffusion. This investigation uses a sample of 42 countries over the period from 1997 to 2008. The data set contains two categories: bilateral trade flow and aggregate R&D expenditure. Meanwhile, this study uses block model analysis to reveal a network structure, which can precisely illustrate a global network configuration. Findings – The findings not only illustrate the pattern change of diffusion from a cascade-like to radial-like structure, but also present the structural configuration of technologically advanced countries and their competitive positions. Practical implications – In the shift to a diffusive structure, time and space are represented in new ways. Therefore, radial-like diffusion structure can provide some technological development approaches for countries interested in exogenous effects for technological growth and managing their international relation. Originality/value – This study is the first to use a multilateral perspective and longitudinal data to examine a cross-country network structure, to provide an understanding of the nature of globalization, its conceptualization and how influence and effects are transmitted through the interconnectedness of international technology diffusion.

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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 (June 28, 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 different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.

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6

Wejnert, Barbara. "Diffusion, Development, and Democracy, 1800-1999." American Sociological Review 70, no. 1 (February 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 via forces at work globally and within the region in which a country resides; this set may be referred to as diffusion processes. This study provides the first systematic comparison of these two sets of variables. When assessed alone, development indicators are robust predictors of democracy, but their predictive power fades with the inclusion of diffusion variables. In particular, diffusion predictors of spatial proximity and networks are robust predictors of democratic growth in both the world and across all regions. The results demonstrate that regional patterns in democratization are evident, and hence world analyses are only the first approximation to understanding democratic growth. Finally, this study introduces an application of Multilevel Regression Models to studies on democratization. Such models fit observed data on world democratization better than the simple regression models used in most previous studies.

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7

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 (December 17, 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 where the diffusion of attitudes depends on the weights and the evolution of weights is formulated as a “learning mechanism” (weight updates depend on the previous values of both weights and attitudes). As a result, the co-evolution is intrinsic/endogenous. We simulate representative scenarios of conflict in 4 real organizational networks. In order to formulate structural balance in directed networks, we extended Heider’s definition of balance considering directed triangles. The evolution of balance involves two stages: first, negative links pop up disorderly and destroy balance, but after some time, as new negative links are formed, a “new” balance is re-established. This “new” balance is emerging concurrently with the polarization of attitudes or domination of one attitude. Moreover, same-minded agents are positively linked and different-minded agents are negatively-linked. This macroscopic self-organization of the system is due only to agent-to-agent interactions, involving feedbacks on weight updates at the local microscopic level.

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8

Xie, Xiaoyi, and Peiji Shi. "Dynamic Evolution and Collaborative Development Model of Urban Agglomeration in Hexi Corridor from the Perspective of Economic Flow." Land 12, no. 2 (January 18, 2023): 274. http://dx.doi.org/10.3390/land12020274.

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Under the green goals of the carbon peak and carbon neutrality, understanding how to develop the economy with high quality is an important issue facing regional development. Based on the years 2000, 2010, and 2020, this paper studies the industrial function connection path and economic network characteristics of the Hexi Corridor through an urban flow model, dominant flow analysis, modified gravity model, and social network analysis method, and puts forward an economic synergistic development model. It is of great significance to strengthen the urban connection in the Hexi Corridor and give full play to the overall competitive advantage. The results are as follows. (1) The overall function of the urban agglomeration is weak, the outward function of manufacturing is outstanding, the complementary network is highly complicated and evolving, and the environment and public service and tourism industry have apparent advantages. (2) The backbone correlation axes of the “three industries” show the characteristics of a closed triangular connection, dual-core linkage development, and multi-center multi-axis interaction. (3) The economic network has a greater agglomeration effect than diffusion effect, with prominent grouping characteristics, forming a network structure of “one man, three vices, and many nodes” and a significant spatial proximity effect. (4) Based on geographical proximity, the “one axis, four circles, multiple points, and multiple channels” synergistic development model, which breaks administrative barriers, becomes the endogenous driving force for the evolution of the economic network.

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9

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

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10

Christozov, Dimitar, and Stefka Toleva-Stoimenova. "Knowledge Diffusion via Social Networks." International Journal of Digital Literacy and Digital Competence 4, no. 2 (April 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 provides. It also argues for needs of “social network literacy” for successful and fruitful use of technology in solving the knowledge acquiring problem.

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11

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

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12

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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13

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

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14

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 is too infrequent or because tests are highly inaccurate). This is also the case where the choice of relaxing social distancing generates the largest negative externalities. Simulations on a real-world network suggest that crowding out is more likely to occur when, in the absence of interventions, face-to-face contacts are perceived to carry relatively high risk.

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15

Valente, Thomas W., and George G. Vega Yon. "Diffusion/Contagion Processes on Social Networks." Health Education & Behavior 47, no. 2 (February 24, 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 selected seeds for comparison. Three network structures are modeled: (1) random, (2) small-world, and (3) scale-free. Four threshold distributions are modeled: (1) normal; (2) uniform; (3) beta 7,14; and (4) beta 1,2; all of which have a mean threshold of 33%, with different variances. The results show that seeding with nodes high on in-degree centrality and/or inverse constraint has faster and more widespread diffusion. Random networks had faster and higher prevalence of diffusion than scale-free ones, but not different from small-world ones. Compared with the normal threshold distribution, the uniform one had faster diffusion and the beta 7,14 distribution had slower diffusion. Most significantly, the threshold distribution standard deviation was associated with rate and prevalence such that higher threshold standard deviations accelerated diffusion and increased prevalence. These results underscore factors that health educators and public health advocates should consider when developing interventions or trying to understand the potential for behavior change.

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16

HU, HAI-BO, CANG-HAI LI, and QING-YING MIAO. "OPINION DIFFUSION ON MULTILAYER SOCIAL NETWORKS." Advances in Complex Systems 20, no. 06n07 (September 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 hold the same opinion, even if the agents only exist on a certain layer, their opinions will diffuse to the entire multilayer networks. This paper not only characterizes the influence of multilayer network topology and agent attribute on opinion diffusion in a holistic way, but also demonstrates the importance of coupling agents which play an indispensable role in some social and economic situations.

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17

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

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18

Kreindler, G. E., and H. P. Young. "Rapid innovation diffusion in social networks." Proceedings of the National Academy of Sciences 111, Supplement_3 (July 14, 2014): 10881–88. http://dx.doi.org/10.1073/pnas.1400842111.

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19

Hu, Haibo. "Competing opinion diffusion on social networks." Royal Society Open Science 4, no. 11 (November 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 can be quantitatively characterized. We analytically obtain the probability that each opinion will ultimately pervade the whole society when there are no committed people in networks, and the final proportion of each opinion at the steady state when there are committed people in networks. The results of numerical simulations show good agreement with those obtained through an analytical approach. This study provides insight into the collective influence of individual attributes, local social networks and global media on opinion diffusion, and contributes to a comprehensive understanding of competing diffusion behaviours in the real world.

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20

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

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21

Fogli, Alessandra, and Laura Veldkamp. "Germs, Social Networks, and Growth." Review of Economic Studies 88, no. 3 (April 2, 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 ideas and disease. Low-diffusion networks have evolved in countries where disease is prevalent because limited connectivity protects residents from epidemics. But a low-diffusion network in a low-disease environment compromises the diffusion of good ideas. In general, social networks have evolved to fit their economic and epidemiological environment. Trying to change networks in one country to mimic those in a higher-income country may well be counterproductive.

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22

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

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23

Kim, Minkyoung, David Newth, and Peter Christen. "Modeling Dynamics of Diffusion Across Heterogeneous Social Networks: News Diffusion in Social Media." Entropy 15, no. 12 (October 8, 2013): 4215–42. http://dx.doi.org/10.3390/e15104215.

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24

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

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25

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

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26

Ok, Jungseul, Youngmi Jin, Jinwoo Shin, and Yung Yi. "On maximizing diffusion speed in social networks." ACM SIGMETRICS Performance Evaluation Review 42, no. 1 (June 20, 2014): 301–13. http://dx.doi.org/10.1145/2637364.2591991.

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27

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

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28

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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29

Christoff, Zoé, and Pavel Naumov. "Diffusion in social networks with recalcitrant agents." Journal of Logic and Computation 29, no. 1 (December 21, 2018): 53–70. http://dx.doi.org/10.1093/logcom/exy037.

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30

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 (May 1, 2016): 1292–304. http://dx.doi.org/10.1109/tmc.2015.2451624.

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31

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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32

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

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33

Li, Pei, Yini Zhang, Fengcai Qiao, and Hui Wang. "Information diffusion in structured online social networks." Modern Physics Letters B 29, no. 13 (May 18, 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 the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

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34

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 (August 3, 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 develop several other models considering the social network’s degree distribution coupled with the assumption of linear influence by neighboring adopters in the diffusion process. Through some evaluation on synthetic data, this paper shows that degree distribution actually changes during the diffusion process. We therefore introduce a multi-stage diffusion model to cope with variable degree distribution. By conducting experiments on both synthetic and real datasets, we show that our proposed diffusion models can recover the diffusion parameters from the observed diffusion data, which allows us to model diffusion with high accuracy.

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Mozafari, Niloofar, and Ali Hamzeh. "An enriched social behavioural information diffusion model in social networks." Journal of Information Science 41, no. 3 (February 10, 2015): 273–83. http://dx.doi.org/10.1177/0165551514565318.

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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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37

Sharma, Devyani, and Robin Dodsworth. "Language Variation and Social Networks." Annual Review of Linguistics 6, no. 1 (January 14, 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 ideologically mediated cultural norms—for instance, norms of gender, class, and ethnicity. Furthermore, not all linguistic variables diffuse in the same way through these structures; social indexicality has a mediating effect. We review prevailing methodologies, theories, and conclusions of this body of work and look ahead to emerging technological advances and more integrated theoretical approaches.

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38

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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39

Lazzati, Natalia. "Codiffusion of Technologies in Social Networks." American Economic Journal: Microeconomics 12, no. 4 (November 1, 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? (JEL D83, O33, Z13)

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40

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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41

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 (April 28, 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. We also demonstrate the effective weekly advertising budget allocations corresponding to the types of movies. We find that the difference of weekly advertising budget allocations gives greater impact on the diffusion with the growth of social networks. This paper provides firm’s managers with important suggestions for diffusion strategy considering the impact of social networks.

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42

Foroozani, Ahmad, and Morteza Ebrahimi. "Nonlinear anomalous information diffusion model in social networks." Communications in Nonlinear Science and Numerical Simulation 103 (December 2021): 106019. http://dx.doi.org/10.1016/j.cnsns.2021.106019.

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43

Arieli, Itai, Yakov Babichenko, Ron Peretz, and H. Peyton Young. "The Speed of Innovation Diffusion in Social Networks." Econometrica 88, no. 2 (2020): 569–94. http://dx.doi.org/10.3982/ecta17007.

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New ways of doing things often get started through the actions of a few innovators, then diffuse rapidly as more and more people come into contact with prior adopters in their social network. Much of the literature focuses on the speed of diffusion as a function of the network topology. In practice, the topology may not be known with any precision, and it is constantly in flux as links are formed and severed. Here, we establish an upper bound on the expected waiting time until a given proportion of the population has adopted that holds independently of the network structure. Kreindler and Young (2014) demonstrated such a bound for regular networks when agents choose between two options: the innovation and the status quo. Our bound holds for directed and undirected networks of arbitrary size and degree distribution, and for multiple competing innovations with different payoffs.

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Liu, Hongli, Yun Xie, Haibo Hu, and Zhigao Chen. "Affinity based information diffusion model in social networks." International Journal of Modern Physics C 25, no. 05 (March 11, 2014): 1440004. http://dx.doi.org/10.1142/s012918311440004x.

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There is a widespread intuitive sense that people prefer participating in spreading the information in which they are interested. The affinity of people with information disseminated can affect the information propagation in social networks. In this paper, we propose an information diffusion model incorporating the mechanism of affinity of people with information which considers the fitness of affinity values of people with affinity threshold of the information. We find that the final size of information diffusion is affected by affinity threshold of the information, average degree of the network and the probability of people's losing their interest in the information. We also explore the effects of other factors on information spreading by numerical simulations and find that the probabilities of people's questioning and confirming the information can affect the propagation speed, but not the final scope.

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Liu, Jiaqi, Luoyi Fu, Zhe Liu, Xiao-Yang Liu, and Xinbing Wang. "Interest-Aware Information Diffusion in Evolving Social Networks." IEEE Transactions on Wireless Communications 17, no. 7 (July 2018): 4593–606. http://dx.doi.org/10.1109/twc.2018.2827984.

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Dhamal, Swapnil, K. J. Prabuchandran, and Y. Narahari. "Information Diffusion in Social Networks in Two Phases." IEEE Transactions on Network Science and Engineering 3, no. 4 (October 1, 2016): 197–210. http://dx.doi.org/10.1109/tnse.2016.2610838.

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Matsubara, Yasuko, Yasushi Sakurai, B. Aditya Prakash, Lei Li, and Christos Faloutsos. "Nonlinear Dynamics of Information Diffusion in Social Networks." ACM Transactions on the Web 11, no. 2 (May 12, 2017): 1–40. http://dx.doi.org/10.1145/3057741.

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Xiong, Fei, Yun Liu, and Hai-Feng Zhang. "Multi-source information diffusion in online social networks." Journal of Statistical Mechanics: Theory and Experiment 2015, no. 7 (July 8, 2015): P07008. http://dx.doi.org/10.1088/1742-5468/2015/07/p07008.

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Fazeli, Arastoo, Amir Ajorlou, and Ali Jadbabaie. "Competitive Diffusion in Social Networks: Quality or Seeding?" IEEE Transactions on Control of Network Systems 4, no. 3 (September 2017): 665–75. http://dx.doi.org/10.1109/tcns.2016.2553364.

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Zhang, Xi, Yuan Su, Siyu Qu, Sihong Xie, Binxing Fang, and Philip S. Yu. "IAD: Interaction-Aware Diffusion Framework in Social Networks." IEEE Transactions on Knowledge and Data Engineering 31, no. 7 (July 1, 2019): 1341–54. http://dx.doi.org/10.1109/tkde.2018.2857492.

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