Spotting Culprits in Epidemics: How Many and Which Ones?

Prakash, B. Aditya; Vreeken, Jilles; Faloutsos, Christos

doi:10.1109/icdm.2012.136

Cited by 195 publications

(194 citation statements)

References 25 publications

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“…This is a common assumption when studying source localization (see, e.g., [28,1,26,27,22]). (A.2) We assume that, when a node is chosen as dynamic sensor, it reveals its state (healthy or infected).…”

Section: Model Assumptionsmentioning

confidence: 99%

“…Several models for how the epidemic spreads have been studied [17]. Discrete-time transmission delays were initially very common (see Assumption (B.5)) [22,27,1]. Then, to better approximate realistic settings, continuous-time transmission models with varying distributions for the transmission delays have been adopted; e.g., exponential [28,21], Gaussian [26,20,19,36] or truncated Gaussians [30].…”

Section: Related Workmentioning

confidence: 99%

“…The first source-estimator was proposed by Shah and Zaman [28] in 2009. This work, and many others that followed, rely on what is often called a complete observation of the epidemic (see Assumption (B.1) in Section 1) [37,27,32]. In these models, the source is estimated by maximum likelihood estimation (MLE).…”

Section: Related Workmentioning

confidence: 99%

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Spinelli

Celis

Thiran

2017

Proceedings of the 26th International Conference on World Wide Web

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Abstract.Source localization, the act of finding the originator of a disease or rumor in a network, has become an important problem in sociology and epidemiology. The localization is done using the infection state and time of infection of a few designated sensor nodes; however, maintaining sensors can be very costly in practice.We propose the first online approach to source localization: We deploy a priori only a small number of sensors (which reveal if they are reached by an infection) and then iteratively choose the best location to place a new sensor in order to localize the source. This approach allows for source localization with a very small number of sensors; moreover, the source can be found while the epidemic is still ongoing. Our method applies to a general network topology and performs well even with random transmission delays.

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Section: Model Assumptionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Spinelli

Celis

Thiran

2017

Proceedings of the 26th International Conference on World Wide Web

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“…Another related problem is immunization, i.e, the problem of finding the best vertices for removal to stop an epidemic, with effective immunization strategies for static and dynamic graphs [19,38,4]. Other such problems where we wish to select a subset of 'important' vertices on graphs, include 'outbreak detection' [27] and finding most-likely starting points ('culprits') of epidemics [26,33]. General Information Diffusion.…”

Section: Related Workmentioning

confidence: 99%

Fast influence-based coarsening for large networks

Purohit

Prakash

Kang

et al. 2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Given a social network, can we quickly 'zoom-out' of the graph? Is there a smaller equivalent representation of the graph that preserves its propagation characteristics? Can we group nodes together based on their influence properties? These are important problems with applications to influence analysis, epidemiology and viral marketing applications.In this paper, we first formulate a novel Graph Coarsening Problem to find a succinct representation of any graph while preserving key characteristics for diffusion processes on that graph. We then provide a fast and effective near-linear-time (in nodes and edges) algorithm coarseNet for the same. Using extensive experiments on multiple real datasets, we demonstrate the quality and scalability of coarseNet, enabling us to reduce the graph by 90% in some cases without much loss of information. Finally we also show how our method can help in diverse applications like influence maximization and detecting patterns of propagation at the level of automatically created groups on real cascade data.

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“…Email: jilles@mpi-inf.mpg.de (a) NetSleuth [18] (b) NetFill lic API. In general, as externals we seldom have access to complete cascades and even when we do, we typically analyze only small samples because of the extreme velocity of social media data.…”

Section: Introductionmentioning

confidence: 99%

Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics

Sundareisan

Vreeken

Prakash

2015

Proceedings of the 2015 SIAM International Conference on Data Mining

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Given a noisy or sampled snapshot of an infection in a large graph, can we automatically and reliably recover the truly infected yet somehow missed nodes? And, what about the seeds, the nodes from which the infection started to spread? These are important questions in diverse contexts, ranging from epidemiology to social media.In this paper, we address the problem of simultaneously recovering the missing infections and the source nodes of the epidemic given noisy data. We formulate the problem by the Minimum Description Length principle, and propose NetFill, an efficient algorithm that automatically and highly accurately identifies the number and identities of both missing nodes and the infection seed nodes.Experimental evaluation on synthetic and real datasets, including using data from information cascades over 96 million blog posts and news articles, shows that our method outperforms other baselines, scales near-linearly, and is highly effective in recovering missing nodes and sources.

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Spotting Culprits in Epidemics: How Many and Which Ones?

Cited by 195 publications

References 25 publications

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Fast influence-based coarsening for large networks

Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics

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