Bayesian inference of diffusion networks with unknown infection times

Shaghaghian, Shohreh; Coates, Mark

doi:10.1109/ssp.2016.7551716

Cited by 5 publications

(13 citation statements)

References 15 publications

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“…Bayesian approach to infer a graph topology from diffusion observations has been proposed where the infection time is not directly observed [35], but rather the state of each node (susceptible or infected) is a latent variable affecting the statistics of the signal which is observed at each node.…”

Section: B Physically-motivated Modelsmentioning

confidence: 99%

Learning Graphs From Data: A Signal Representation Perspective

Dong

Thanou

Rabbat

et al. 2019

IEEE Signal Process. Mag.

343

222

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The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus desirable to infer or learn a graph topology from the data. In this tutorial overview, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective. We further emphasize the conceptual similarities and differences between classical and GSP-based graph inference methods, and highlight the potential advantage of the latter in a number of theoretical and practical scenarios. We conclude with several open issues and challenges that are keys to the design of future signal processing and machine learning algorithms for learning graphs from data.

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Section: B Physically-motivated Modelsmentioning

confidence: 99%

Learning Graphs From Data: A Signal Representation Perspective

Dong

Thanou

Rabbat

et al. 2019

IEEE Signal Process. Mag.

343

222

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show abstract

“…, and assess the underlying diffusion process based on these samples. We first describe the batch inference approach based on Gibbs Sampling (GS) that we proposed in [16]. We then extend this framework by considering the cases where no infection time is detected in the interval under study.…”

Section: System Modelmentioning

confidence: 99%

“…. According to (16), if node i has not become infected by the end of block b − 1, the probability that it becomes infected by node l ∈ π i b at some time…”

Section: A Transition Distributionmentioning

confidence: 99%

“…In this paper, we improve upon these existing methods by proposing an approach to simultaneously infer the structure and cascade trace of a diffusion process when the infection times are completely unavailable. The batch inference approach presented in this paper (see Section IV) was introduced in an earlier conference paper [16], but here we include more extensive experimental results and develop an online version of the inference algorithm.…”

Section: B Network Inference Without Perfect Cascade Observationmentioning

confidence: 99%

“…Hence, we resort to Monte Carlo Markov Chain (MCMC) methods to generate samples from the posterior distribution, f (z b , t b , α b |d B 1:b ), and assess the underlying diffusion process based on these samples. We first describe the batch inference approach based on Gibbs Sampling (GS) that we proposed in [16]. We then extend this framework by considering the cases where no infection time is detected in the interval under study.…”

Section: System Modelmentioning

confidence: 99%

See 2 more Smart Citations

Online Bayesian Inference of Diffusion Networks

Shaghaghian

Coates

2017

IEEE Trans. on Signal and Inf. Process. over Networks

Self Cite

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Abstract-Understanding the process by which a contagion disseminates throughout a network is of great importance in many real world applications. The required sophistication of the inference approach depends on the type of information we want to extract as well as the number of observations that are available to us. We analyze scenarios in which not only the underlying network structure (parental relationships and link strengths) needs to be detected, but also the infection times must be estimated. We assume that our only observation of the diffusion process is a set of time series, one for each node of the network, which exhibit changepoints when an infection occurs. After formulating a model to describe the contagion, and selecting appropriate prior distributions, we seek to find the set of model parameters that best explains our observations. Modeling the problem in a Bayesian framework, we exploit Monte Carlo Markov Chain, Sequential Monte Carlo, and time series analysis techniques to develop batch and online inference algorithms. We evaluate the performance of our proposed algorithms via numerical simulations of synthetic network contagions and analysis of real-world datasets.

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Recommender System-Based Diffusion Inferring for Open Social Networks

Yang

Dong

Chen

et al. 2020

IEEE Trans. Comput. Soc. Syst.

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Open social network (OSN) plays a more significant role in information propagation through the rapid developing of information technology. Since information diffusion is an essential process happens in OSN, it has been studied in many researches. Several models have been proposed to infer the diffusion process and reproduce diffusion network. However, these methods have two critical problems, 1) ignoring the effects of user social characteristics, 2) inaccuracy resulted from calculating the influence of different features independently. To address these limitations, a diffusion inferring method based on recommender system (DIM-SPTF) was proposed. DIM-SPTF method considers the propagation process between users as the recommendation process of information and employs recommender system to infer the propagation relationship. Through determining the propagation relations among all users in the observed topic data set, information diffusion network can be finally obtained. Experimental results show that DIM-SPTF leads to improvements in performance compared with state-of-the-art methods.

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Bayesian inference of diffusion networks with unknown infection times

Cited by 5 publications

References 15 publications

Learning Graphs From Data: A Signal Representation Perspective

Learning Graphs From Data: A Signal Representation Perspective

Online Bayesian Inference of Diffusion Networks

Recommender System-Based Diffusion Inferring for Open Social Networks

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