scholarly journals Information diffusion in networks through social learning

2015 ◽  
Vol 10 (3) ◽  
pp. 807-851 ◽  
Author(s):  
Ilan Lobel ◽  
Evan Sadler
Keyword(s):  
Social Learning ◽  
Information Diffusion ◽  
Diffusion In Networks

Diffusion in Networks

2016 ◽  
pp. 478-503 ◽  
Author(s):  
P. J. Lamberson
Keyword(s):  
Social Learning ◽  
Network Structure ◽  
Stochastic Dominance ◽  
Degree Distribution ◽  
Future Research ◽  
Threshold Models ◽  
Epidemiological Models ◽  
Spreading Process ◽  
Dominance Relations ◽  
Diffusion In Networks

This chapter examines models of diffusion in networks, and specifically how the topology of the network impacts the spreading process. The chapter begins by discussing epidemiological models and how stochastic dominance relations can be used to understand the effect of the degree distribution of the network. The chapter then turns to more sophisticated models of social influence, including threshold models and models of social learning. A key insight that emerges from the collection of models discussed is that not only does network structure matter, but how the network matters depends on the way in which agents influence one another. Network features that facilitate contagion under one model of influence can inhibit diffusion in another. The chapter concludes with thoughts on directions for future research.

scholarly journals Quantum Probabilistic Models Using Feynman Diagram Rules for Better Understanding the Information Diffusion Dynamics in Online Social Networks

2020 ◽  
Vol 34 (10) ◽  
pp. 13730-13731
Author(s):  
Ece C. Mutlu
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Complex Systems ◽  
Bayesian Networks ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Probabilistic Models ◽  
Human Interaction ◽  
Multi Agent Systems ◽  
Diffusion Dynamics

This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.

scholarly journals Productivity Effects of Information Diffusion in Networks

2007 ◽  
Author(s):  
Sinan Aral ◽  
Erik Brynjolfsson ◽  
Marshall W. Van Alstyne
Keyword(s):  
Information Diffusion ◽  
Diffusion In Networks

scholarly journals Bottleneck links, essential intermediaries, and competing paths of diffusion in networks

2021 ◽  
Vol 16 (3) ◽  
pp. 1017-1053
Author(s):  
Mihai Manea
Keyword(s):  
Intellectual Property ◽  
Intellectual Property Rights ◽  
Equivalence Relation ◽  
Network Architecture ◽  
Information Diffusion ◽  
Equivalence Classes ◽  
Information Goods ◽  
Dense Networks ◽  
Diffusion Paths ◽  
Diffusion In Networks

We investigate how information goods are priced and diffused over links in a network. A new equivalence relation between nodes captures the effects of network architecture and locations of sellers on the division of profits, and characterizes the topology of competing (and potentially overlapping) diffusion paths. Sellers indirectly appropriate profits over intermediation chains from buyers in their equivalence classes. Links within the same class constitute bottlenecks for information diffusion and confer monopoly power. Links that bridge distinct classes are redundant for diffusion and generate competition among sellers. In dense networks, competition limits the scope of indirect appropriability and intellectual property rights foster innovation.

A cognitive approach to cumulative technological culture is useful and necessary but only if it also applies to other species

2020 ◽  
Vol 43 ◽  
Author(s):  
Thibaud Gruber
Keyword(s):  
Social Learning ◽  
Cognitive Processes ◽  
Learning Processes ◽  
Cognitive Approach ◽  
Technological Culture ◽  
Social Learning Processes

Abstract The debate on cumulative technological culture (CTC) is dominated by social-learning discussions, at the expense of other cognitive processes, leading to flawed circular arguments. I welcome the authors' approach to decouple CTC from social-learning processes without minimizing their impact. Yet, this model will only be informative to understand the evolution of CTC if tested in other cultural species.

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