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UID:JyQZttv6vTPuu7JIynAsB
SUMMARY:Fusing Theory and Practice of Graph Algorithms
DTSTAMP:20260401T014400Z
DTSTART;VALUE=DATE:20250220
DTEND;VALUE=DATE:20250222
DESCRIPTION:Researchers working on graph algorithms use a broad range of di
	fferent criteria for deciding what makes an algorithm efficient. While in 
	theory the dominant benchmark is the asymptotic running time\, in practice
	 the story is more nuanced: an algorithm needs to be simple enough to be i
	mplementable\, fast on graphs of bounded size\, space efficient\, cache-fr
	iendly\, and easy to test. While many of these requirements motivate inter
	esting algorithmic questions that are highly relevant in practice\, they a
	re often overlooked by the theory community. The goal of the workshop is t
	o foster the exchange of ideas between researchers working on graph algori
	thms\, which have high practical relevance. The workshop will include over
	view talks on the various perspectives\, research talks\, an open problem 
	session\, and structured time for collaboration.\n\nThe topics of the work
	shop include fundamental data science graph algorithms (e.g.\, clustering\
	, partitioning\, graph embedding)\, graph neural networks\, and modeling d
	ata using networks (e.g. approximate nearest neighbor search). Additionall
	y\, the workshop program incorporates problems and approaches necessitated
	 by scaling graph algorithms to large datasets (e.g. parallel\, distribute
	d\, dynamic and external memory models\, as well as algorithm engineering)
	.
URL:https://icerm.brown.edu/program/hot_topics_workshop/htw-25-ftpga
LOCATION:Institute for Computational and Experimental Research in Mathemati
	cs\, Brown University
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