Bridging Theory and Practice in Parallel Subgraph Computations
, MIT CSAIL
Date: Wednesday, April 20, 2022
Time: 4:00 PM to 5:00 PM Note: all times are in the Eastern Time Zone
Event Type: Seminar
Room Description: https://mit.zoom.us/meeting/register/tJMtdOqgrTwsGNV_k0Nk6JjLMk-G6GFzTRyk
Host: Julian Shun, MIT CSAIL
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Speaker URL: http://web.mit.edu/jeshi/www/
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TALK: Bridging Theory and Practice in Parallel Subgraph Computations
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Abstract: Discovering dense substructures in graphs is a fundamental topic in graph mining, and has been studied across many areas including computational biology, spam and fraud detection, and large-scale network analysis. In particular, k-cliques are the core building blocks of dense substructures, and finding k-cliques in a graph is a classic problem with a long history of study in both theory and practice. In this work, we present new parallel algorithms for k-clique counting and listing, which we then use in new parallel subgraph decomposition algorithms that capture higher-order structures in networks. Specifically, we design new parallel algorithms for the k-clique densest subgraph problem and the nucleus decomposition problem, which generalizes the notions of k-cores and k-trusses.
We show that our parallel k-clique counting / listing algorithm has polylogarithmic span (parallel time) and is work-efficient (matches the work of the best sequential algorithm) for sparse graphs. We also design two new parallel work-efficient algorithms for approximating the k-clique densest subgraph. Furthermore, we present a novel work-efficient parallel algorithm with low span for the nucleus decomposition problem, which significantly improves upon the only existing parallel nucleus decomposition algorithm (Sariyuce et al., PVLDB 2018). In addition to our theoretical results, we implement these algorithms, and we introduce several new practical optimizations, including tradeoffs between performance and space complexity, and a new multi-level hash table structure to store information on cliques space-efficiently and a technique for traversing this structure cache-efficiently. We show that our implementations are fast in practice and outperform the existing state-of-the-art implementations.
Based on joint work with Laxman Dhulipala and Julian Shun.
https://arxiv.org/abs/2002.10047 (ACDA 2021)
https://arxiv.org/abs/2111.10980 (VLDB 2022)
Bio: Jessica Shi is a 4th-year PhD student at MIT in the EECS department, where she is advised by Julian Shun. She is also a Student Researcher at Google on the Graph Mining team, where she is mentored by Jakub Łącki. Jessica’s current research interests include developing shared-memory parallel graph algorithms with provable theoretical guarantees and efficient scalable implementations, with a focus on subgraph decomposition and clustering algorithms. She is supported by a 2018 NSF Graduate Research Fellowship, and she has previously received her S.M. in computer science from MIT, and her A.B. in mathematics from Princeton University.
Algorithms & Theory, Programming Languages & Software
Created by Julian J. Shun at Wednesday, April 13, 2022 at 1:01 AM.