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Distributed Computation of Large-scale Graph Problems
Nanyang Technological University.
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.ORCID iD: 0000-0003-4468-2675
University of Houston.
National University of Singapore.
2015 (English)In: SODA '15 Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, Society for Industrial and Applied Mathematics, 2015, 391-410 p.Conference paper (Refereed)
Abstract [en]

Motivated by the increasing need for fast distributed processing of large-scale graphs such as the Web graph and various social networks, we study a number of fundamental graph problems in the message-passing model, where we have k machines that jointly perform computation on an arbitrary n-node (typically, nk) input graph. The graph is assumed to be randomly partitioned among the k ≥ 2 machines (a common implementation in many real world systems). The communication is point-to-point, and the goal is to minimize the time complexity i.e., the number of communication rounds, of solving various fundamental graph problems.

We present lower bounds that quantify the fundamental time limitations of distributively solving graph problems. We first show a lower bound of Ω(n/k) rounds for computing a spanning tree (ST) of the input graph. This result also implies the same bound for other fundamental problems such as computing a minimum spanning tree (MST), breadth-first tree (BFS), and shortest paths tree (SPT). We also show an Ω(n/k2) lower bound for connectivity ST verification and other related problems. Our lower bounds develop and use new bounds in random-partition communication complexity.

To complement our lower bounds, we also give algorithms for various fundamental graph problems, e.g., PageRank, MST, connectivity, ST verification, shortest paths, cuts, spanners, covering problems, densest subgraph, subgraph isomorphism, finding triangles, etc. We show that problems such as PageRank, MST, connectivity, and graph covering can be solved in Õ(n/k) time (the notation Õ hides polylog(n) factors and an additive polylog(n) term); this shows that one can achieve almost linear (in k) speedup, whereas for shortest paths, we present algorithms that run in Õ(n/Ok) time (for (1 + ε)-factor approximation) and in Õ(n/k) time (for O(log n)-factor approximation) respectively.

Our results are a step towards understanding the complexity of distributively solving large-scale graph problems.

Place, publisher, year, edition, pages
Society for Industrial and Applied Mathematics, 2015. 391-410 p.
National Category
Computer Science
URN: urn:nbn:se:kth:diva-165842OAI: diva2:808907
Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) January 4-6, 2015

QC 20150520

Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2015-05-20Bibliographically approved

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Na Nongkai, Danupon
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