Scalable Processing of Massive Uncertain Graph Data: A Simultaneous Processing Approach

Zou, Zhaonian; Li, Faming; Li, Jianzhong; Li, Yingshu

doi:10.1109/icde.2017.70

Cited by 10 publications

(13 citation statements)

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“…Further research on reliability emphasized on polynomialtime upper/lower bounds to reliability problems [5,7,8,16,27,35]. 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.…”

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.

View full text Add to dashboard Cite

show abstract

“…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%

“…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]. We provide the trade-offs about the accuracy, efficiency and memory usages about these algorithms, which can help the future researchers to know them well, and design the proper distributed version.…”

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

confidence: 99%

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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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