Stochastic block model: Difference between revisions

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== DARPA/MIT/AWS Graph Challenge: streaming stochastic block partition ==

GraphChallenge<ref>[http://graphchallenge.mit.edu] DARPA/MIT/AWS Graph Challenge</ref> encourages community approaches to developing new solutions for analyzing graphs and sparse data derived from social media, sensor feeds, and scientific data to enable relationships between events to be discovered as they unfold in the field. Streaming stochastic block partition is one of the challenges since 2017.

<ref>[http://graphchallenge.mit.edu/champions] DARPA/MIT/AWS Graph Challenge Champions</ref> [[Spectral clustering]] has demonstrated outstanding performance compared to the baseoriginal algorithm,and matchingeven its quality of clusters while being multiple orders of magnitude faster.improved<ref>{{cite journal

| author1 = A. J. Uppal |author2 = J. Choi |author3 = T. B. Rolinger |author4 = H. Howie Huang

| title = Faster Stochastic Block Partition Using Aggressive Initial Merging, Compressed Representation, and Parallelism Control

| journal=2021 IEEE High Performance Extreme Computing Conference (HPEC)

| date = 2021 |doi = 10.1109/HPEC49654.2021.9622836}}</ref>

base algorithm, matching its quality of clusters while being multiple orders of magnitude faster.<ref>{{cite journal

| author1 = David Zhuzhunashvili |author2 = Andrew Knyazev

| title = Preconditioned spectral clustering for stochastic block partition streaming graph challenge (Preliminary version at arXiv.)

| journal=2017 IEEE High Performance Extreme Computing Conference (HPEC)

| date = 2017 |doi = 10.1109/HPEC.2017.8091045}}</ref>