A Scalable Framework for Modeling Competitive Diffusion in Social Networks

Broecheler, Matthias; Shakarian, Paulo; Subrahmanian, V. S.

doi:10.1109/socialcom.2010.49

“…Our work differs from the work of [19] in several ways. First, our study deals only with the spread of knowledge, as opposed to the spread of opinion, which as defined could depend on several bits of knowledge (or equivalently, pieces of knowledge) [20].…”

Section: Related Workmentioning

confidence: 79%

“…Consequently, questions focusing on the diffusion of conflicting knowledge in a network are rarely addressed. A notable exception is the work of [19], who describes a tool which allows users to specify conflicting opinions within a social network. The tool then predicts the most likely outcome of a scenario where agents must choose to side with one or more of these opinions (e.g.…”

Section: Related Workmentioning

confidence: 99%

Simulating Diffusion with Conflicting Knowledge

Joseph

¹

,

Carley

²

2012

SSRN Journal

0

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assistance on the ideas presented in this work.Keywords: Agent-based modeling, Diffusion Processes, Decision-making. AbstractA simulation model is developed and used to better understand the diffusion of conflicting knowledge in a social network. Within the simulation, correct and incorrect knowledge diffuse simultaneously, however, simulation agents are not explicitly made aware that contradictory information exists. Instead, they may become aware of this by obtaining both correct and incorrect knowledge of the same piece of information. The agent must then determine what knowledge to accept. We implement various models of how people react to conflicting knowledge, which we refer to as intrapersonal conflict resolution strategies (ICRS). We analyze these ICRS with respect to the speed and breadth of correct and incorrect knowledge spread and how often agents find themselves holding conflicting knowledge. We also consider the effect of varying the odds of incorrect knowledge existing in the system, the network structure and the differentials in trust between agents. We find evidence that our model is consistent with many real-world notions of diffusion. Consequently, we make tentative conclusions about the effect of our various experimental conditions on the spread of conflicting knowledge in a social network. We note that conflicting knowledge spreads more slowly and persists longer than knowledge which cannot be conflicted.

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“…In addition, we are interested in understanding how the way in which agents make decisions affects the flow of information, as opposed to considering what opinion is most likely to be held by the majority of agents. Finally, we use simulation to generate results, whereas [19] treat the issue as an optimization problem. Consequently, our focus is much more heavily tied to understanding the flow of information.…”

Section: Related Workmentioning

confidence: 99%

Simulating Diffusion with Conflicting Knowledge

Joseph

¹

,

Carley

²

2012

SSRN Journal

0

View full text Add to dashboard Cite

assistance on the ideas presented in this work.Keywords: Agent-based modeling, Diffusion Processes, Decision-making. AbstractA simulation model is developed and used to better understand the diffusion of conflicting knowledge in a social network. Within the simulation, correct and incorrect knowledge diffuse simultaneously, however, simulation agents are not explicitly made aware that contradictory information exists. Instead, they may become aware of this by obtaining both correct and incorrect knowledge of the same piece of information. The agent must then determine what knowledge to accept. We implement various models of how people react to conflicting knowledge, which we refer to as intrapersonal conflict resolution strategies (ICRS). We analyze these ICRS with respect to the speed and breadth of correct and incorrect knowledge spread and how often agents find themselves holding conflicting knowledge. We also consider the effect of varying the odds of incorrect knowledge existing in the system, the network structure and the differentials in trust between agents. We find evidence that our model is consistent with many real-world notions of diffusion. Consequently, we make tentative conclusions about the effect of our various experimental conditions on the spread of conflicting knowledge in a social network. We note that conflicting knowledge spreads more slowly and persists longer than knowledge which cannot be conflicted.

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“…Individuals receive information in a continuous stream over time, merging small pieces of information at different spatial locations and then conveying them to the masses over OSNs. Several mathematical models have been proposed to model the distribution of information in OSNs, but none of them is comprehensive [17]. The heterogeneity of user interactions and mobility concerning links, the dynamic structure of OSNs, and the merging of information over time and space have opened a new era of research on information diffusion and user behaviors in OSNs.…”

Section: Introductionmentioning

confidence: 99%

Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social Networks

Ullah

¹

,

Lee

²

2016

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User interactions in online social networks (OSNs) enable the spread of information and enhance the information dissemination process, but at the same time they exacerbate the information overload problem. In this paper, we propose a social content recommendation method based on spatial-temporal aware controlled information diffusion modeling in OSNs. Users interact more frequently when they are close to each other geographically, have similar behaviors, and fall into similar demographic categories. Considering these facts, we propose multicriteria-based social ties relationship and temporal-aware probabilistic information diffusion modeling for controlled information spread maximization in OSNs. The proposed social ties relationship modeling takes into account user spatial information, content trust, opinion similarity, and demographics. We suggest a ranking algorithm that considers the user ties strength with friends and friends-of-friends to rank users in OSNs and select highly influential injection nodes. These nodes are able to improve social content recommendations, minimize information diffusion time, and maximize information spread. Furthermore, the proposed temporal-aware probabilistic diffusion process categorizes the nodes and diffuses the recommended content to only those users who are highly influential and can enhance information dissemination. The experimental results show the effectiveness of the proposed scheme.

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A Scalable Framework for Modeling Competitive Diffusion in Social Networks

Cited by 28 publications

References 17 publications

Simulating Diffusion with Conflicting Knowledge

Simulating Diffusion with Conflicting Knowledge

Simulating Diffusion with Conflicting Knowledge

Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social Networks

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