Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics

Sundareisan, Shashidhar; Vreeken, Jilles; Prakash, B. Aditya

doi:10.1137/1.9781611974010.47

Cited by 26 publications

(30 citation statements)

References 25 publications

(36 reference statements)

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“…e closest in the area of reconstructing epidemics over time has been done in [14] where the authors use Steiner trees to infer the propagation structure. However in most real datasets, observing the cascade from the start is di cult due to time of observation [17] and missing data issues. In that context, our work only relies on the current information available to determine the source of infections.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks

Sarkar

Alvari

Shakarian

2019

Companion Proceedings of the 2019 World Wide Web Conference

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Information di usion mechanisms based on social in uence models are mainly studied using likelihood of adoption when active neighbors expose a user to a message. e problem arises primarily from the fact that for the most part, this explicit information of who-exposed-whom among a group of active neighbors in a social network, before a susceptible node is infected is not available. In this paper, we a empt to understand the di usion process through information cascades by studying the temporal network structure of the cascades. In doing so, we accommodate the e ect of exposures from active neighbors of a node through a network pruning technique that leverages network motifs to identify potential infectors responsible for exposures from among those active neighbors. We a empt to evaluate the e ectiveness of the components used in modeling cascade dynamics and especially whether the additional e ect of the exposure information is useful. Following this model, we develop an inference algorithm namely I C , that uses parameters learned from the model and the exposure information to predict the actual parent node of each potentially susceptible user in a given cascade. Empirical evaluation on a real world dataset from Weibo social network demonstrate the signi cance of incorporating exposure information in recovering the exact parents of the exposed users at the early stages of the di usion process.

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Section: Related Workmentioning

confidence: 99%

Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks

Sarkar

Alvari

Shakarian

2019

Companion Proceedings of the 2019 World Wide Web Conference

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“…[22] studies the network completion problem, where the focus is to learn a probabilistic model that fits the observed part of a network, and then uses the model to infer missing nodes and links of the network. More specifically, [23] addresses the problem of recovering the missing infections and the source nodes of an epidemic from sampled snapshots of large graphs. The notion of graph identification is introduced in [24], which aims to infer a cleaned output network from a noisy, incomplete input graph.…”

Section: B Incomplete Graph Miningmentioning

confidence: 99%

SINE: Scalable Incomplete Network Embedding

Zhang

Yin

Zhu

2018

2018 IEEE International Conference on Data Mining (ICDM)

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Attributed network embedding aims to learn lowdimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content. Because network topology structure and node attributes often exhibit high correlation, incorporating node attribute proximity into network embedding is beneficial for learning good vector representations. In reality, large-scale networks often have incomplete/missing node content or linkages, yet existing attributed network embedding algorithms all operate under the assumption that networks are complete. Thus, their performance is vulnerable to missing data and suffers from poor scalability.In this paper, we propose a Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs. SINE formulates a probabilistic learning framework that separately models pairs of node-context and node-attribute relationships. Different from existing attributed network embedding algorithms, SINE provides greater flexibility to make the best of useful information and mitigate negative effects of missing information on representation learning. A stochastic gradient descent based online algorithm is derived to learn node representations, allowing SINE to scale up to large-scale networks with high learning efficiency. We evaluate the effectiveness and efficiency of SINE through extensive experiments on real-world networks. Experimental results confirm that SINE outperforms state-of-the-art baselines in various tasks, including node classification, node clustering, and link prediction, under settings with missing links and node attributes. SINE is also shown to be scalable and efficient on large-scale networks with millions of nodes/edges and high-dimensional node features. The source code of this paper is available at https://github.com/daokunzhang/SINE.

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“…The work closest to ours is the work by Sundareisan et al [3]. Similar to our problem formulation, they aim at recovering hidden infections under a non-temporal setting where the source nodes are unknown.…”

Section: Related Workmentioning

confidence: 99%

“…• We define the probabilistic cascade-reconstruction problem (Section III), which makes weaker assumptions compared to methods such as NetFill [3], thus offering more robustness. • To solve the cascade-reconstruction problem, we study the problem of sampling Steiner trees with a given set of terminals, and propose two algorithms with provable guarantees on the sampling distribution (Section IV).…”

Section: Introductionmentioning

confidence: 99%

Robust Cascade Reconstruction by Steiner Tree Sampling

Xiao

Aslay

Gionis

2018

2018 IEEE International Conference on Data Mining (ICDM)

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We consider a network where an infection has taken place and a subset of infected nodes has been partially observed. Our goal is to reconstruct the underlying cascade that is likely to have generated these observations. We reduce this cascadereconstruction problem to computing the marginal probability that a node is infected given the partial observations, which is a #P-hard problem. To circumvent this issue, we resort to estimating infection probabilities by generating a sample of probable cascades, which span the nodes that have already been observed to be infected, and avoid the nodes that have been observed to be uninfected. The sampling problem corresponds to sampling directed Steiner trees with a given set of terminals, which is a problem of independent interest and has received limited attention in the literature. For the latter problem we propose two novel algorithms with provable guarantees on the sampling distribution of the returned Steiner trees.The resulting method improves over state-of-the-art approaches that often make explicit assumptions about the infection-propagation model, or require additional parameters. Our method provides a more robust approach to the cascadereconstruction problem, which makes weaker assumptions about the infection model, requires fewer additional parameters, and can be used to estimate node infection probabilities. We experimentally validate the proposed reconstruction algorithm on realworld graphs with both synthetic and real cascades. We show that our method outperforms all other baseline strategies in most cases.

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Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics

Cited by 26 publications

References 25 publications

Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks

Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks

SINE: Scalable Incomplete Network Embedding

Robust Cascade Reconstruction by Steiner Tree Sampling

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