Determining Source–Destination Connectivity in Uncertain Networks: Modeling and Solutions

Fu, Luoyi; Fu, Xinzhe; Zhang, Xu; Peng, Qianyang; Wang, Xinbing; Lu, Songwu

doi:10.1109/tnet.2017.2725905

Cited by 26 publications

(15 citation statements)

References 32 publications

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“…Recently, efficient distributed algorithms have been developed for reliability estimation [10,47]. Some orthogonal directions include finding one "good" possible world [33,37], considering the most probable path [9,26], as well as adaptive edge testing [13][14][15] and crowdsourcing [31] for reducing uncertainty. As discussed earlier in Sections 2.2 -2.7, in this work we focus on sampling and indexing based sequential algorithms for s-t reliability estimation.…”

Section: Other Related Workmentioning

confidence: 99%

“…In this paper, we shall focus on sequential algorithms for the fundamental s-t reliability query. Notice that we would not consider distributed algorithms [10,47], other simplified versions of the s-t reliability problem [9,26,33,37], neither the reduction of uncertainty of a graph (e.g., by crowdsourcing) before s-t reliability estimation [13][14][15]31]. If a method was designed for a specific kind of reliability query (e.g.…”

Section: Introductionmentioning

confidence: 99%

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An in-depth comparison of s-t reliability algorithms over uncertain graphs

Ke¹,

Khan²,

Quan³

2019

Proc. VLDB Endow.

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Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging applications, and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is the s-t reliability, which measures the probability that a target node t is reachable from a source node s in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence.Due to the inherent complexity of the s-t reliability estimation problem (#P-hard), various sampling and indexing based efficient algorithms were proposed in the literature. However, since they have not been thoroughly compared with each other, it is not clear whether the later algorithm outperforms the earlier ones. More importantly, the comparison framework, datasets, and metrics were often not consistent (e.g., different convergence criteria were employed to find the optimal number of samples) across these works. We address this serious concern by re-implementing six state-ofthe-art s-t reliability estimation methods in a common system and code base, using several medium and large-scale, real-world graph datasets, identical evaluation metrics, and query workloads.Through our systematic and in-depth analysis of experimental results, we report surprising findings, such as many follow-up algorithms can actually be several orders of magnitude inefficient, less accurate, and more memory intensive compared to the ones that were proposed earlier. We conclude by discussing our recommendations on the road ahead. Reliability in uncertain graphsTheory Polynomial-time upper/lower bounds [5,7,8,16,27,35] Representative possible worlds [33,37] Simplified version of reliability problem Most reliable path [9,22,26] Algorithm Sequential Distributed [10, 47] Fundamental s-t reliability query Other queries: top-k query[43], distance-constrained query [20], conditional reliability [23], probabilistic road network query [19], influence maximization [21], etc. Uncertainty reduction: adaptive edge testing [13-15], crowdsourcing [31] Estimator Monte Carlo (MC) sampling [12] Variant of MC: Lazy propagation [30] Recursive estimator: RHH [20], RSS [28] Index scheme BFS Sharing [45] ProbTree [32]Our Focus Figure 2: The broad spectrum of reliability problem over uncertain graphs sive samplings [20,28] and shared possible worlds [45], as well as other indexing methods [32]. With the wide range of algorithms available for estimating the s-t reliability over uncertain graphs, there is an urgent need to realize their trade-offs, and to employ the best algorithm for a given scenario.As depicted in Figure 1, we find serious concerns in the existing experimental comparisons of state-of-the-art reliability estimation algorithms over uncertain graphs. (1) There is no prior work that compared all state-of-the-art methods with each other. It is, therefore, difficult to draw a general conclusion on the superiority and trade-offs of different methods.(2) As shown in Figure 1, with the exceptio...

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Section: Other Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An in-depth comparison of s-t reliability algorithms over uncertain graphs

Ke¹,

Khan²,

Quan³

2019

Proc. VLDB Endow.

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“…In the contexts where the network topology is not known, or only partially known we should first estimate the network topology. This difficult and interesting task is out of the scope of this paper; recently, solutions were proposed, for example, by Farajtabar et al [18], Fu et al [19], [20] and Gomez-Rodriguez et al [22]. (A.2) We assume that, when a node is a sensor, it reveals its state (healthy or infected).…”

Section: What We Assume (A1)mentioning

confidence: 99%

A General Framework for Sensor Placement in Source Localization

Spinelli

Celis

Thiran

2019

IEEE Trans. Netw. Sci. Eng.

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When an epidemic spreads in a given network of individuals or communities, can we detect its source using only the information provided by a small set of nodes? We propose a general framework that incorporates two dimensions. First, we can either rely exclusively on a set of selected nodes (i.e., sensors) which always reveal their state independently of any particular epidemic (these are called static), or we can add some sensors (called dynamic) as an epidemic spreads, depending on which additional information is required. Second, the method can either localizes the source after an epidemic has spread through the entire network (offline), or while the epidemic is ongoing (online). We empirically study the performance of offline and online localization both with and without dynamic sensors. Our analysis shows that, by using dynamic sensors, the number of sensors necessary to localize the source is reduced by up to a factor of 10 and that, even with high-variance transmission delays, the source can be localized by using fewer than 5% of the nodes as sensors.

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“…In this work, we shall focus on sequential algorithms for the fundamental s-t reliability query 1 . Notice that we would not consider distributed algorithms [42,196], other simplified versions of the s-t reliability problem [41,104,158], neither the reduction of uncertainty of a graph (e.g., by crowdsourcing) before s-t reliability estimation [64][65][66]119]. However, our work can benefit the aforementioned studies in many aspects: (1) The distributed algorithms are usually designed based on the fundamental sequential algorithms involved in our paper, e.g., [196] distributed the procedure of possible world sample generation, which is exactly the distributed adoption of basic algorithm in [63].…”

Section: Chapter 2 Experiments and Analyses: S-t Reliability Algorithms In Uncertain Graphsmentioning

confidence: 99%

“…Some orthogonal directions include finding Chapter 2. Experiments and Analyses: s-t Reliability Algorithms in Uncertain Graphs one "good" possible world [137,158], considering the most probable path [41,104], as well as adaptive edge testing [64][65][66] and crowdsourcing [119] for reducing uncertainty. As discussed earlier in Sections 2.2.1 -2.2.6, in this work we focus on sampling and indexing based sequential algorithms for s-t reliability estimation.…”

Section: Other Related Workmentioning

confidence: 99%

Querying and mining complex graphs : uncertainty and multi-relations

Ke¹

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Graphs are important data representations that can ubiquitously model objects and their relations in a wide diversity of real-word applications. Effective and efficient graph analytics provide users with a deeper understanding of what is behind the data, thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. Nowadays, besides the rapid growth in the volume of graphs, the inherent complexity on graphs has attracted great attention from data management community. This thesis will therefore focus on querying and mining uncertain and multi-relation graphs. Uncertain graphs can characterize the inherent uncertainty in the data due to many reasons, including noisy measurements, inference and prediction models, and explicit manipulations. For example, in a road network, each road junction can be represented as a node, and each road segment can serve as an edge with a blocking (e.g., traffic jam) probability. Meanwhile, the multi-relation graphs, where multiple edges may This thesis contains material from 4 papers published in the following peer-reviewed journal(s) / from papers accepted at conferences in which I am listed as an author.

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Determining Source–Destination Connectivity in Uncertain Networks: Modeling and Solutions

Cited by 26 publications

References 32 publications

An in-depth comparison of s-t reliability algorithms over uncertain graphs

An in-depth comparison of s-t reliability algorithms over uncertain graphs

A General Framework for Sensor Placement in Source Localization

Querying and mining complex graphs : uncertainty and multi-relations

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