Online Bayesian Inference of Diffusion Networks

Shaghaghian, Shohreh; Coates, Mark

doi:10.1109/tsipn.2017.2731160

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…The problem of inferring influence network or modeling information diffusion network may be divided into multiple sub-groups considering several aspects of this problem. For example, in the assumptions of one model, the set of information cascades are fully observed [26], [27], but in some, there are missing data in the cascades [28], or the dynamics of network may change over time in some models [28], [29] whereas the other models assume a timeinvariant network [26]. Considering a set of information cascades which are infection time series of a network's nodes, some models proposed to infer the underlying network over which the information diffuses.…”

Section: -Related Workmentioning

confidence: 99%

Inferring Diffusion Network from Information Cascades using Transitive Influence

Emadi,

Rahgozar,

Oroumchian

2023

jist

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Nowadays, online social networks have a great impact on people's life and how they interact. News, sentiment, rumors, and fashion, like contagious diseases, are propagated through online social networks. When information is transmitted from one person to another in a social network, a diffusion process occurs. Each node of a network that participates in the diffusion process leaves some effects on this process, such as its transmission time. In most cases, despite the visibility of such effects of diffusion process, the structure of the network is unknown. Knowing the structure of a social network is essential for many research studies such as: such as community detection, expert finding, influence maximization, information diffusion, sentiment propagation, immunization against rumors, etc. Hence, inferring diffusion network and studying the behavior of the inferred network are considered to be important issues in social network researches. In recent years, various methods have been proposed for inferring a diffusion network. A wide range of proposed models, named parametric models, assume that the pattern of the propagation process follows a particular distribution. What's happening in the real world is very complicated and cannot easily be modeled with parametric models. Also, the models provided for large volumes of data do not have the required performance due to their high execution time. However, in this article, a nonparametric model is proposed that infers the underlying diffusion network. In the proposed model, all potential edges between the network nodes are identified using a similarity-based link prediction method. Then, a fast algorithm for graph pruning is used to reduce the number of edges. The proposed algorithm uses the transitive influence principle in social networks. The time complexity order of the proposed method is O(n 3 ). This method was evaluated for both synthesized and real datasets. Comparison of the proposed method with state-of-the-art on different network types and various models of information cascades show that the model performs better precision and decreases the execution time too.

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Section: -Related Workmentioning

confidence: 99%

Inferring Diffusion Network from Information Cascades using Transitive Influence

Emadi,

Rahgozar,

Oroumchian

2023

jist

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“…The general network inference problem can be easily extended to various settings. For example, the Bayesian inference models assume that both exact activation times and edges are unknown and required to be inferred [40,41]. Besides, the network inference can be integrated into other frameworks to accomplish certain tasks, such as clustering [42] and recommendation system [43].…”

Section: Network Inferencementioning

confidence: 99%

Latent Network Embedding via Adversarial Auto-encoders

Lei,

Shi,

Niu

2021

Preprint

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Graph auto-encoders have proved to be useful in network embedding task. However, current models only consider explicit structures and fail to explore the informative latent structures cohered in networks. To address this issue, we propose a latent network embedding model based on adversarial graph auto-encoders. Under this framework, the problem of discovering latent structures is formulated as inferring the latent ties from partial observations. A latent transmission matrix that describes the strengths of existing edges and latent ties is derived based on influence cascades sampled by simulating diffusion processes over networks. Besides, since the inference process may bring extra noises, we introduce an adversarial training that works as regularization to dislodge noises and improve the model robustness. Extensive experiments on link prediction and node classification tasks show that the proposed model achieves superior results compared with baseline models.

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“…Embar et al propose a Bayesian framework for estimating properties of the network, e.g., edge strengths as well as cascade properties, e.g., propagation trees [13]. Other Bayesian approaches of network inference aim to infer the underlying network when the exact infection time is unknown in both standard and online algorithms [29,39,40]. Additionally, some approaches based on MLE use Bayesian techniques at intermediate steps to improve results [8].…”

Section: Background and Related Workmentioning

confidence: 99%

Bayesian Inference of Network Structure From Information Cascades

Gray

Mitchell

Roughan

2020

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

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Contagion processes are strongly linked to the network structures on which they propagate, and learning these structures is essential for understanding and intervention on complex network processes such as epidemics and (mis)information propagation. However, using contagion data to infer network structure is a challenging inverse problem. In particular, it is imperative to have appropriate measures of uncertainty in network structure estimates, however these are largely ignored in most machine-learning approaches. We present a probabilistic framework that uses samples from the distribution of networks that are compatible with the dynamics observed to produce network and uncertainty estimates. We demonstrate the method using the well known independent cascade model to sample from the distribution of networks P(G) conditioned on the observation of a set of infections C. We evaluate the accuracy of the method by using the marginal probabilities of each edge in the distribution, and show the benefits of quantifying uncertainty to improve estimates and understanding, particularly with small amounts of data.

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Online Bayesian Inference of Diffusion Networks

Cited by 9 publications

References 23 publications

Inferring Diffusion Network from Information Cascades using Transitive Influence

Inferring Diffusion Network from Information Cascades using Transitive Influence

Latent Network Embedding via Adversarial Auto-encoders

Bayesian Inference of Network Structure From Information Cascades

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