Stochastic block model: Difference between revisions

Revision as of 16:34, 21 July 2023

Revision as of 05:59, 22 July 2023

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== Extensions to signed graphs ==

Signed graphs allow for both favorable and adverse relationships and serve as a common model choice for various data analysis applications, e.g., correlation clustering. The stochastic block model can be trivially extended to signed graphs by assigning both positive and negative edge weights or equivalently using a difference of adjacency matrices of two stochastic block models.

<ref>{{cite journalbook

| author1=Alyson Fox |author2=Geoffrey Sanders |author3=Andrew Knyazev

| title =2018 IEEE High Performance extreme Computing Conference (HPEC) |chapter=Investigation of Spectral Clustering for Signed Graph Matrix Representations | date = 2018 |pages=1–7 |doi = 10.1109/HPEC.2018.8547575|osti=1476177 |isbn=978-1-5386-5989-2 |s2cid=54443034 }}</ref>

| journal=2018 IEEE High Performance Extreme Computing Conference (HPEC)

| date = 2018 |pages=1–7 |doi = 10.1109/HPEC.2018.8547575|osti=1476177 |isbn=978-1-5386-5989-2 |s2cid=54443034 }}</ref>

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

GraphChallenge<ref>[http://graphchallenge.mit.edu] {{Webarchive|url=https://web.archive.org/web/20230204160402/http://graphchallenge.mit.edu/ |date=2023-02-04 }} 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] {{Webarchive|url=https://web.archive.org/web/20230204160403/http://graphchallenge.mit.edu/champions |date=2023-02-04 }} DARPA/MIT/AWS Graph Challenge Champions</ref> [[Spectral clustering]] has demonstrated outstanding performance compared to the original and even improved<ref>{{cite journalbook

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

| title = 2021 IEEE High Performance Extreme Computing Conference (HPEC) |chapter = Faster Stochastic Block Partition Using Aggressive Initial Merging, Compressed Representation, and Parallelism Control | date = 2021 |pages = 1–7 |doi = 10.1109/HPEC49654.2021.9622836|isbn = 978-1-6654-2369-4 |s2cid = 244780210 }}</ref>

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

| date = 2021 |pages = 1–7 |doi = 10.1109/HPEC49654.2021.9622836|isbn = 978-1-6654-2369-4 |s2cid = 244780210 }}</ref>

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

| author1 = David Zhuzhunashvili |author2 = Andrew Knyazev

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|s2cid = 19781504

}}</ref>

<ref>{{cite journalbook

| author1 = Lisa Durbeck |author2 = Peter Athanas

|title journal=2018 2020 IEEE High Performance Extreme Computing Conference (HPEC)

| titlechapter = Incremental Streaming Graph Partitioning

| journal=2020 IEEE High Performance Extreme Computing Conference (HPEC)

| date = 2020 |pages = 1–8

|doi = 10.1109/HPEC43674.2020.9286181|isbn = 978-1-7281-9219-2