# Challenges

GraphChallenge seeks input from diverse communities to develop graph challenges that take the best of what has been learned from groundbreaking efforts such as GraphAnalysis, Graph500, FireHose, MiniTri, and GraphBLAS to create a new set of challenges to move the community forward.

**New! Anonymized Network Sensing Graph Challenge** This challenge constructs and analyzes anonymized traffic matrices from network packet capture (PCAP) data to enable open community-based approaches to protecting networks.

- Specification: slides, paper, example serial parse code, example serial analyze code, example data sets

**Sparse Deep Neural Network Graph Challenge** This challenge performs neural network inference on a variety of sparse deep neural networks.

- Specification: slides, paper, example serial code, example data sets

**Static Graph Challenge: Subgraph Isomorphism** This challenge seeks to identify a given sub-graph in a larger graph.

- Specification: slides, paper, example serial code, example data sets, Amazon instructions

**Streaming Graph Challenge: Stochastic Block Partition** This challenge seeks to identify optimal blocks (or clusters) in a larger graph.

- Specification: slides, paper, example serial code, example data sets, Amazon instructions

Note on static versus streaming graph challenges. In static processing, given a large graph G the goal is to evaluate a function f(G). In stateless streaming, given an additional smaller graph g, the goal is to evaluate the function f(g). In stateful streaming, the goal is to evaluate a function f(G + g). Stateful streaming is the focus of the streaming graph challenge.

**Pre-Challenge: PageRank Pipeline** This challenge is meant to test some of the specification and reference code approaches that might be used in subsequent challenges. The community is encouraged to examine this specification and code and provide feedback as to how it can be improved for subsequent challenges.

Please use the following archival citations for the Graph Challenge when using the datasets and/or implementations made available via this Challenge:

*Sparse Deep Neural Network Graph Challenge*, Jeremy Kepner, Simon Alford, Vijay Gadepally, Michael Jones, Lauren Milechin, Ryan Robinett, Sid Samsi, IEEE HPEC, 2019

*Static Graph Challenge: Subgraph Isomorphism, *Siddharth Samsi, Vijay Gadepally,* *Michael Hurley, Michael Jones, Edward Kao, Sanjeev Mohindra, Paul Monticciolo, Albert Reuther, Steven Smith, William Song, Diane Staheli, Jeremy Kepner, IEEE HPEC, 2017

*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, IEEE HPEC, 2017

*PageRank Pipeline Benchmark: Proposal for a Holistic System Benchmark for Big-Data Platforms*, Patrick Dreher, Chansup Byun, Chris Hill, Vijay Gadepally, Bradley Kuszmaul, Jeremy Kepner, IEEE IPDPSW, 2016