Bayesian Inference of Network Structure From Information Cascades

Gray, Caitlin; Mitchell, Lewis; Roughan, Matthew

doi:10.1109/tsipn.2020.2990276

Cited by 13 publications

(8 citation statements)

References 45 publications

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“…Bayesian-Inference-based Methods: [25] proposes a Bayesian inference method using Markov Chain Monte Carlo (MCMC) and the Tie No Tie (TNT) sampler for sampling from the posterior P (G♣C), with the likelihood of information propagation P (C♣G) over all directed spanning trees. In this method, the Bayesian part is based on an undirected graph.…”

Section: Convex-based-maximum-likelihood-estimationmentioning

confidence: 99%

DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property

Ramezani,

Ahadinia,

Farhadi

et al. 2024

Preprint

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The fast growth of social networks and their data access limitations in recent years have led to increasing difficulty in obtaining the complete topology of these networks. However, diffusion information over these networks is available, and many algorithms have been proposed to infer the underlying networks using this information. The previously proposed algorithms only focus on inferring more links and ignore preserving the critical topological characteristics of the underlying social networks. In this paper, we propose a novel method called DANI to infer the underlying network while preserving its structural properties. It is based on the Markov transition matrix derived from time series cascades, as well as the node-node similarity that can be observed in the cascade behavior from a structural point of view. In addition, the presented method has linear time complexity (increases linearly with the number of nodes, number of cascades, and square of the average length of cascades), and its distributed version in the MapReduce framework is also scalable. We applied the proposed approach to both real and synthetic networks. The experimental results showed that DANI has higher accuracy and lower run time while maintaining structural properties, including modular structure, degree distribution, connected components, density, and clustering coefficients, than well-known network inference methods.

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Section: Convex-based-maximum-likelihood-estimationmentioning

confidence: 99%

DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property

Ramezani,

Ahadinia,

Farhadi

et al. 2024

Preprint

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show abstract

“…To solve eq. ( 8), the Metropolis-Hastings MCMC algorithm [56] or belief propagation approach [57] can be employed, which allows us to infer the network structure.…”

Section: Andmentioning

confidence: 99%

Reconstructing networks via discrete state dynamical data: A mini-review

Ma,

Wang,

Zhang

2023

EPL

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The inference of network structure from dynamic data is one of the most challenging scientific problems in network science. To address this issue, researchers have proposed various approaches regarding different types of dynamical data. Since many real evolution processes or social phenomena can be described by discrete state dynamical systems, such as the spreading of epidemic, the evolution of opinions, and the cooperation behaviors. Therefore, network reconstruction methods driven by discrete state dynamical data were also widely studied. In this Letter, we provide a mini-review of recent progresses for reconstructing networks based on discrete state dynamical data. These studies encompass network reconstruction problems where the dynamical processes are known, as well as those where the dynamics are unknown, and extend to the reconstruction of higher-order networks. Finally, we discuss the remaining challenges in this field.

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“…2018 ; Gray et al. 2020 ), but, in any case, is one account sharing the post further evidence of a relationship? What if it is reciprocated once, or three times?…”

Section: Social Network From Social Media Datamentioning

confidence: 99%

Exploring the effect of streamed social media data variations on social network analysis

Weber

Nasim

Mitchell

et al. 2021

Soc. Netw. Anal. Min.

Self Cite

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To study the effects of online social network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present several measurement case studies showing how variations in collected OSN data affect social network analyses. To this end, we developed a systematic comparison methodology, which we applied to five pairs of parallel datasets collected from Twitter in four case studies. We found considerable differences in several of the datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.

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Bayesian Inference of Network Structure From Information Cascades

Cited by 13 publications

References 45 publications

DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property

DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property

Reconstructing networks via discrete state dynamical data: A mini-review

Exploring the effect of streamed social media data variations on social network analysis

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