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Uncertain, or probabilistic, graphs have been increasingly used to representnoisy linked data in many emerging applications, and have recently attractedthe attention of the database research community. A fundamental problem onuncertain graphs is the s-t reliability, which measures the probability that atarget node t is reachable from a source node s in a probabilistic (oruncertain) graph, i.e., a graph where every edge is assigned a probability ofexistence.  Due to the inherent complexity of the s-t reliability estimation problem(#P-hard), various sampling and indexing based efficient algorithms wereproposed in the literature. However, since they have not been thoroughlycompared with each other, it is not clear whether the later algorithmoutperforms the earlier ones. More importantly, the comparison framework,datasets, and metrics were often not consistent (e.g., different convergencecriteria were employed to find the optimal number of samples) across theseworks. We address this serious concern by re-implementing six state-of-the-arts-t reliability estimation methods in a common system and code base, usingseveral medium and large-scale, real-world graph datasets, identical evaluationmetrics, and query workloads.  Through our systematic and in-depth analysis of experimental results, wereport surprising findings, such as many follow-up algorithms can actually beseveral orders of magnitude inefficient, less accurate, and more memoryintensive compared to the ones that were proposed earlier. We conclude bydiscussing our recommendations on the road ahead.

An In-Depth Comparison of s-t Reliability Algorithms over Uncertain Graphs