Streaming Graph Challenge: Stochastic Block Partition
Edward Kao, Vijay Gadepally, Michael Hurley, Michael Jones, Jeremy, Kepner, Sanjeev Mohindra, Paul Monticciolo, Albert Reuther, Siddharth Samsi,, William Song, Diane Staheli, Steven Smith

TL;DR
This paper introduces a scalable streaming graph partition challenge using a Bayesian inference-based algorithm that outperforms traditional methods and supports parallel computation, with open-source tools and datasets available.
Contribution
It presents a novel graph partition challenge with a Bayesian algorithm that is scalable, parallelizable, and addresses limitations of existing approaches.
Findings
Algorithm has sub-quadratic complexity
Parallelization strategies improve performance
Open source code and datasets provided
Abstract
An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive benchmarks and challenges have proven to be an effective means to advance state-of-the-art performance and foster community collaboration. This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity. The algorithm employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs. This strong foundation enables the algorithm to address limitations of well-known graph partition approaches such as modularity…
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