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Shortest Paths and Centrality in Uncertain Networks

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Shortest Paths and Centrality in Uncertain Networks Shortest Paths and Centrality in Uncertain Networks Arkaprava Saha Ruben Brokkelkamp Yllka Velaj NTU Singapore CWI Amsterdam, The Netherlands University of Vienna, Austria [email protected] [email protected] [email protected] Arijit Khan Francesco Bonchi NTU Singapore ISI Foundation, Turin, Italy [email protected] [email protected] F ABSTRACT Path % Length %A ¹ShC ¹%ºº 1, 0.05 1, 0.05 B Computing the shortest path between a pair of nodes is a funda- G %1 : ¹B,F, Cº 2 0.0025 mental graph primitive, which has critical applications in vehicle 2, 0.15 2, 0.15 B C %2 : ¹B, G, Cº 4 0.0224 routing, finding functional pathways in biological networks, surviv- 3, 0.25 3, 0.25 ~ %3 : ¹B,~, Cº 6 0.0609 able network design, among many others. In this work, we study 4, 0.95 4, 0.95 %4 : ¹B, I, Cº 8 0.8250 shortest-path queries over uncertain networks, i.e., graphs where I every edge is associated with a probability of existence. We show that, for a given path, it is #P-hard to compute the probability of it C Figure 1: Example of paths in an uncertain graph: %A ¹ShB ¹%ºº de- being the shortest path, and we also derive other interesting prop- notes the probability that path % is the shortest path from B to C. erties highlighting the complexity of computing the Most Probable Shortest Paths (MPSPs). We thus devise sampling-based efficient finding functional pathways in biological networks. A critical appli- algorithms, with end-to-end accuracy guarantees, to compute the cation of shortest paths is the computation of betweenness centrality MPSP. As a concrete application, we show how to compute a novel [10, 19, 43, 54], a measure of importance of a node based on its concept of betweenness centrality in an uncertain graph using effectiveness in connecting pairs of other nodes via shortest paths. MPSPs. Our thorough experimental results and rich real-world In this paper, we first study the fundamental problem of com- case studies on sensor networks and brain networks validate the puting shortest-path queries in uncertain networks, then we build effectiveness, efficiency, scalability, and usefulness of our solution. over it a measure of betweeness centrality. The notion of shortest path in an uncertain graph should consider not only the length of PVLDB Reference Format: a path but also the probability of existence of all edges on the path. Arkaprava Saha, Ruben Brokkelkamp, Yllka Velaj, Arijit Khan, Specifically, given an uncertain graph G, a source node B, and a tar- and Francesco Bonchi. Shortest Paths and Centrality in Uncertain get node C, our goal is to find the path % from B to C with the highest Networks. PVLDB, 14(7): 1188-1201, 2021. probability of being the shortest path (SP), i.e., the probability with doi:10.14778/3450980.3450988 which % exists and no path shorter than % exists. We refer to such a path as the Most Probable Shortest Path (MPSP) from B to C. PVLDB Artifact Availability: The source code, data, and/or other artifacts have been made available at Example 1. Each edge in the uncertain graph in Figure 1 is anno- https://github.com/ArkaSaha/MPSP-Centrality. tated with its length and its probability of existence. For the source B and target C, although the path %1 = ¹B,F, Cº is the shortest (when 1 INTRODUCTION not considering probabilities), the one having the highest probability Uncertain networks, i.e., graphs where each edge is associated with of being the shortest path, i.e., the MPSP from B to C, is %4 = ¹B, I, Cº, a probability of existence, have received a great deal of attention which is also the longest path (when not considering probabilities). thanks to their expressivity and applicability in many real world contexts. Researchers have studied :-nearest neighbor queries [39, Computing MPSPs is useful in many applications. Road networks 52], reachability queries [31], clustering [23], sampling [48], net- are modeled as uncertain graphs because of unexpected traffic jams work design [30], and embedding [24], just to mention a few. Un- [25], where a vehicle driver may find the MPSP to the nearest certainty in a network might arise due to noisy measurements [2], gas station or restaurant. MPSPs are also useful in routing over edge imputation using inference and prediction models [1, 40], and wireless sensor networks, where links between sensor nodes have explicit manipulation of edges, e.g., for privacy purposes [7]. a probability of failure. Many applications not only require the Shortest-path queries [8, 17, 27] are one of the fundamental shortest route, but also one with a high precision [22, 33], such graph primitives with a plethora of applications, e.g., traffic routing, as being the shortest with a high probability. Brain networks are often represented as weighted uncertain graphs, where nodes are This work is licensed under the Creative Commons BY-NC-ND 4.0 International the brain regions of interest (ROIs), edges indicate potential co- License. Visit https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of this license. For any use beyond those covered by this license, obtain permission by activation between ROIs, edge distance represents physical distance emailing [email protected]. Copyright is held by the owner/author(s). Publication rights between ROIs, and edge probability indicates the strength of the licensed to the VLDB Endowment. co-activation signal [15]. Finding MPSPs between different ROIs Proceedings of the VLDB Endowment, Vol. 14, No. 7 ISSN 2150-8097. doi:10.14778/3450980.3450988 of the brain could differentiate healthy brains from those with diseases, such as autism [16, 20]. In our experiments, we present 1188 two concrete use cases of MPSPs on sensor networks (§ 5.7) and 1.2 Contributions and Roadmap brain networks (§ 5.8). We formally define the concept of the Most Probable Shortest Path (MPSP) in an uncertain graph (§ 2), prove that our problem is 1.1 Related Work #P-hard, and also derive other interesting properties highlighting Several variants of shortest-path queries over uncertain graphs the complexity of computing MPSPs (§ 2.1). We discuss an earlier have been studied in the literature. Work in [12, 13, 62] investigates baseline solution [63], together with its shortcomings (§ 2.2). In § 3, threshold-based shortest-path queries in uncertain graphs, i.e., the we propose our sampling based efficient algorithms, with end-to- problem of finding all paths having shortest-path probability larger end accuracy guarantees, to compute the MPSP. than a predefined threshold. In particular, [12, 13] consider a differ- We then focus on three important generalizations of our problem: ent uncertain graph model with correlation. The work closest to first we study top-: MPSP queries for : ¡ 1 (§ 3.2); followed by ours is probably [63], which considers MPSP queries as we do, but single-source and single-target MPSP queries (§ 3.4); then MPSPs it does not provide any hardness result or any accuracy guarantee. over uncertain multi-graphs (§ 3.5). The last one provides a general In [63], similar to [13, 62], a filtering-and-verification framework data model, since it allows one to model the uncertainty as a prob- is used, which enumerates paths between the two given nodes in ability distribution of the length of an edge: for instance, in road increasing order of length, without considering edges’ probabili- networks, it can model the probability distribution of travel times on ties, till a termination criterion is achieved. Among the candidate specific road segments. Furthermore, we study MPSP-Betweenness- paths generated, a sampling method is applied (e.g., the Luby-Karp Centrality and develop efficient sampling strategies to compute the algorithm [28]) to approximately measure each candidate path’s top-: central nodes, with theoretical quality guarantees (§ 4). probability of being the shortest path. However, it may happen that Finally, we conduct thorough experiments (§ 5) showing scalabil- the MPSP (the path we are looking for) is not one of the shortest few ity over large-scale datasets and performance improvements against paths when one does not consider probabilities (as in Example 1). In state-of-the-art methods [12, 63]. We also develop interesting case this case, a filtering-and-verification approach would have to enu- studies with sensor (§ 5.7) and brain (§ 5.8) networks. merate a large number of paths before including the real MPSP in the candidate set. Thus we ask the question: can we quickly include 2 PRELIMINARIES the MPSP in the candidate set, without requiring to enumerate all Let G = ¹+, 퐸,, , ?º be a probabilistic (or uncertain) directed graph, paths shorter than the MPSP? To address this, we combine Monte where , : 퐸 ! R≥0 defines non-negative edge length, and ? : Carlo (MC) sampling with Dijkstra’s algorithm (referred to as Dijk- 퐸 ! ¹0, 1¼ is a function that assigns a probability of existence stra+MC) from the source node. That is, when a node is reached via to each edge. Following the bulk of the literature on uncertain Dijkstra’s algorithm, its outgoing edges are sampled according to graphs [5, 26, 34, 35, 52, 59, 62, 63], we adopt the well-established their probabilities, and only the sampled edges are considered for possible world semantics and assume that edge probabilities are choosing the next node. As formally proved in § 3.3, our method independent of each other: the uncertain graph G is interpreted ≈ will need only a small number ( 20) of Dijkstra+MC runs to in- as a probability distribution over the 2j퐸 j deterministic graphs clude the MPSP in the candidate set with a high probability.
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