2024-09-12-kumar24a.md

title	abstract	openreview	software	section	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Optimization Framework for Semi-supervised Attributed Graph Coarsening	In data-intensive applications, graphs serve as foundational structures across various domains. However, the increasing size of datasets poses significant challenges to performing downstream tasks. To address this problem, techniques such as graph coarsening, condensation, and summarization have been developed to create a coarsened graph while preserving important properties of the original graph by considering both the graph matrix and the feature or attribute matrix of the original graph as inputs. However, existing graph coarsening techniques often neglect the label information during the coarsening process, which can result in a lower-quality coarsened graph and limit its suitability for downstream tasks. To overcome this limitation, we introduce the Label-Aware Graph Coarsening (LAGC) algorithm, a semi-supervised approach that incorporates the graph matrix, feature matrix, and some of the node label information to learn a coarsened graph. Our proposed formulation is a non-convex optimization problem that is efficiently solved using block successive upper bound minimization(BSUM) technique, and it is provably convergent. Our extensive results demonstrate that the LAGC algorithm outperforms the existing state-of-the-art method by a significant margin.	ehcPPdsFx4	https://github.com/Archit2000/LAGC	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	kumar24a	0	Optimization Framework for Semi-supervised Attributed Graph Coarsening	2064	2075	2064-2075	2064	false	Kumar, Manoj and Halder, Subhanu and Kane, Archit and Gupta, Ruchir and Kumar, Sandeep	given family Manoj Kumar given family Subhanu Halder given family Archit Kane given family Ruchir Gupta given family Sandeep Kumar	2024-09-12		Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence	244	inproceedings	date-parts 2024 9 12	https://raw.githubusercontent.com/mlresearch/v244/main/assets/kumar24a/kumar24a.pdf