Locating multiple diffusion sources in time varying networks from sparse observations

Hu, Zhao; Shen, Zhesi; Cao, Shinan; Podobnik, Boris; Yang, Huijie; Wang, Wen Xu; Lai, Ying–Cheng

doi:10.1038/s41598-018-20033-9

Cited by 36 publications

(36 citation statements)

References 47 publications

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“…Information diffusion is a common phenomenon on social networks [1], [2]. Its modeling has many applications, such as helping to predict which user is an opinion leader [3], how much a cascade will grow [4], who are the diffusion sources [5], which user will digg a particular story [6], and so on. In this paper, we study the task of information diffusion prediction.…”

Section: Introductionmentioning

confidence: 99%

Topological Recurrent Neural Network for Diffusion Prediction

Wang

Zheng

Liu

et al. 2017

2017 IEEE International Conference on Data Mining (ICDM)

139

124

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Abstract-In this paper, we study the problem of using representation learning to assist information diffusion prediction on graphs. In particular, we aim at estimating the probability of an inactive node to be activated next in a cascade. Despite the success of recent deep learning methods for diffusion, we find that they often underexplore the cascade structure. We consider a cascade as not merely a sequence of nodes ordered by their activation time stamps; instead, it has a richer structure indicating the diffusion process over the data graph. As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure. We find it challenging to model diffusion topologies, which are dynamic directed acyclic graphs (DAGs), with the existing neural networks. Therefore, we propose a novel topological recurrent neural network, namely Topo-LSTM, for modeling dynamic DAGs. We customize Topo-LSTM for the diffusion prediction task, and show it improves the state-of-theart baselines, by 20.1%-56.6% (MAP) relatively, across multiple real-world data sets. Our code and data sets are available online 1 .

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Section: Introductionmentioning

confidence: 99%

Topological Recurrent Neural Network for Diffusion Prediction

Wang

Zheng

Liu

et al. 2017

2017 IEEE International Conference on Data Mining (ICDM)

139

124

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“…They proposed a highly efficient algorithm to identify likely sets of seed nodes given a snapshot and show that it can optimize the virus propagation ripple in a principled way by maximizing the likelihood. Zhu et al [16] proposed a new source localization algorithm, named Optimal-Jordan-Cover (OJC). The algorithm first extracts a subgraph using a candidate selection algorithm that selects source candidates based on the number of observed infected nodes in their neighborhoods.…”

Section: Related Workmentioning

confidence: 99%

Estimating the Information Source under Decaying Diffusion Rates

Woo

Choi

2019

Electronics

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Recently, as arising from online social network services such as Facebook and Twitter, people are more actively using social networks to exchange their new information. In this consideration, finding the information source becomes one of the indispensable and useful tasks in detecting a malicious agent and an influential person in the networks. A seminal work by Shah and Zaman in 2010 showed that the detection probability cannot be beyond 31% even for regular trees if time goes to infinity. From the study, extensive researches have been done for this problem, whose major interests lie in constructing an efficient estimator and providing theoretical analysis on its detection performance. However, most of the works assumed the homogeneous diffusion rate of the information, where the diffusion rate does not change at all times over the network. In practice, it is reported that information has a lifetime and it becomes less attractive over time. In this paper, we study the problem of detecting the information source when the diffusion rate decreases by the distance from the source in the network. As a result, we obtain analytical detection performance of Maximum Likelihood Estimator (MLE) and validate our theoretical findings over the regular tree, random and real-world networks.

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“…There are several works which have studied other social network problem in various perspective when observed data is incomplete. He et al [9] have proposed algorithms inferring influence function with incomplete observations, Rozenshtein et al [17], Sun et al [18], Zong et al [23] have investigated the reconstruction of cascades from partial timestamps, and Zhu et al [21,22] have proposed an algorithm which locates diffusion sources when time logs are partially observed. Some researchers have studied the graph learning without the explicit time of infection.…”

Section: Related Workmentioning

confidence: 99%

Iterative learning of graph connectivity from partially-observed cascade samples

Woo

2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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Graph learning is an inference problem of estimating connectivity of a graph from a collection of epidemic cascades, with many useful applications in the areas of online/offline social networks, p2p networks, computer security, and epidemiology. We consider a practical scenario when the information of cascade samples are partially observed in the independent cascade (IC) model. For the graph learning problem, we propose an efficient algorithm that solves a localized version of computationally-intractable maximum likelihood estimation through approximations in both temporal and spatial aspects. Our algorithm iterates the operations of recovering missing time logs and inferring graph connectivity, and thereby progressively improves the inference quality. We study the sample complexity, which is the number of required cascade samples to meet a given inference quality, and show that it is asymptotically close to a lower bound, thus near-order-optimal in terms of the number of nodes. We evaluate the performance of our algorithm using five real-world social networks, whose size ranges from 20 to 900, and demonstrate that our algorithm performs better than other competing algorithms in terms of accuracy while maintaining fast running time. CCS CONCEPTS • Networks → Online social networks; • Mathematics of computing → Maximum likelihood estimation; • Theory of computation → Sample complexity and generalization bounds.

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Locating multiple diffusion sources in time varying networks from sparse observations

Cited by 36 publications

References 47 publications

Topological Recurrent Neural Network for Diffusion Prediction

Topological Recurrent Neural Network for Diffusion Prediction

Estimating the Information Source under Decaying Diffusion Rates

Iterative learning of graph connectivity from partially-observed cascade samples

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