Large-scale agent-based simulations of online social networks

Murić, Goran; Tregubov, Alexey; Blythe, Jim; Abeliuk, Andrés; Choudhary, Divya; Lerman, Kristina; Ferrara, Emilio

doi:10.1007/s10458-022-09565-7

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Even though only eigenvectors V 2 of most recent shift operator S 2 are explicitly stated in figure (17), all shift operators-particularly their eigenspace alignment-have an impact on x (2) . The GFT coefficients…”

Section: Stochastic Gradient-based Graph Neural Networkmentioning

confidence: 99%

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Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

Sonti,

Mulpuri Santhi Sri,

Pamulapati

2024

Concurrency and Computation

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SummaryIn recent years, social network analysis has received a lot of interest. A critical area of research in this field is link prediction. Link prediction is researched for other forms of social networks. Still, because social link networks (SLNs) change over time and depend on the discussed topics, this network has unique difficulties. Recent studies have focused on three main issues: extending link prediction to a dynamic environment, forecasting formation, and destroying network linkages that change over time. Although it is a challenging issue, deep learning (DL) techniques have been demonstrated to increase prediction accuracy significantly. This research proposes a novel approach to link correlation for social networks based on DL architectures in feature vector prediction and classification. Here the input data has been processed for smoothening and normalization with noise removal. Then, the feature vector was predicted using a dynamically structured convolutional radial basis neural network for this data. The expected feature vector has been classified using a stochastic gradient‐based graph neural network. The experimental analysis is carried out for various social network data in terms of accuracy of 98%, precision of 85%, recall of 86%, F‐1 score of 75%, AUC of 72%, and RMSE of 76%.

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Section: Stochastic Gradient-based Graph Neural Networkmentioning

confidence: 99%

“…Since then, this publication has become a standard reference for link prediction researchers. In their article, Behera and Das 17 investigated a supervised ML strategy for predicting missing links. They have created several ML models to capture topological information related to linkages and nodes in the network.…”

Section: Review Of Literaturementioning

confidence: 99%

Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

Sonti,

Mulpuri Santhi Sri,

Pamulapati

2024

Concurrency and Computation

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“…Agent platforms for cognitive and social simulations allow the preparation of small and large agent-based models that can be used to understand the complexities of human behavior and social interactions. The social simulations can be additionally advanced by the incorporation of machine learning techniques [121]. As such, in recent years, it has become more popular to incorporate AI training into various agent-based applications.…”

Section: Cognitive Social and Affective Agent Platformsmentioning

confidence: 99%

Overview of Software Agent Platforms Available in 2023

et al. 2023

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Agent-based computing remains an active field of research with the goal of building (semi-)autonomous software for dynamic ecosystems. Today, this task should be realized using dedicated, specialized frameworks. Over almost 40 years, multiple agent platforms have been developed. While many of them have been “abandoned”, others remain active, and new ones are constantly being released. This contribution presents a historical perspective on the domain and an up-to-date review of the existing agent platforms. It aims to serve as a reference point for anyone interested in developing agent systems. Therefore, the main characteristics of the included agent platforms are summarized, and selected links to projects where they have been used are provided. Furthermore, the described platforms are divided into general-purpose platforms and those targeting specific application domains. The focus of the contribution is on platforms that can be judged as being under active development. Information about “historical platforms” and platforms with an unclear status is included in a dedicated website accompanying this work.

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“…Garibay et al ( 2020 ) proposed DeepAgent that can simulate social dynamics in a multi-resolution setting at the user, community, population, and content levels. Murić et al ( 2022 ) developed an ABM that provides support for simulations of cognitive behavior and shared state across multiple compute nodes. They augmented the agents' decision-making process by employing machine learning models that predict the probability of interaction between an agent and a particular resource.…”

Section: Introductionmentioning

confidence: 99%

Modeling information diffusion in social media: data-driven observations

et al. 2023

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Accurately modeling information diffusion within and across social media platforms has many practical applications, such as estimating the size of the audience exposed to a particular narrative or testing intervention techniques for addressing misinformation. However, it turns out that real data reveal phenomena that pose significant challenges to modeling: events in the physical world affect in varying ways conversations on different social media platforms; coordinated influence campaigns may swing discussions in unexpected directions; a platform's algorithms direct who sees which message, which affects in opaque ways how information spreads. This article describes our research efforts in the SocialSim program of the Defense Advanced Research Projects Agency. As formulated by DARPA, the intent of the SocialSim research program was “to develop innovative technologies for high-fidelity computational simulation of online social behavior ... [focused] specifically on information spread and evolution.” In this article we document lessons we learned over the 4+ years of the recently concluded project. Our hope is that an accounting of our experience may prove useful to other researchers should they attempt a related project.

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Large-scale agent-based simulations of online social networks

Cited by 7 publications

References 21 publications

Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

Overview of Software Agent Platforms Available in 2023

Modeling information diffusion in social media: data-driven observations

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