From 67a43d1f1e78838d4e5f54938a9d456e396307d3 Mon Sep 17 00:00:00 2001 From: Louis Date: Tue, 15 Jul 2025 10:48:34 +0200 Subject: [PATCH] Adding Hamilton ref --- references.bib | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/references.bib b/references.bib index cc848d9..d6b0ae5 100644 --- a/references.bib +++ b/references.bib @@ -565,6 +565,24 @@ Read\_Status\_Date: 2025-05-14T20:18:00.025Z}, file = {/home/louis/snap/zotero-snap/common/Zotero/storage/PPHP33Z9/Govaert et Nadif - 2010 - Latent Block Model for Contingency Table.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/UT8TARCX/govaert2010.pdf.pdf} } +@online{hamiltonInductiveRepresentationLearning2018, + title = {Inductive {{Representation Learning}} on {{Large Graphs}}}, + author = {Hamilton, William L. and Ying, Rex and Leskovec, Jure}, + date = {2018-09-10}, + eprint = {1706.02216}, + eprinttype = {arXiv}, + eprintclass = {cs}, + doi = {10.48550/arXiv.1706.02216}, + url = {http://arxiv.org/abs/1706.02216}, + urldate = {2025-07-01}, + abstract = {Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.}, + pubstate = {prepublished}, + keywords = {Computer Science - Machine Learning,Computer Science - Social and Information Networks,Statistics - Machine Learning}, + annotation = {Read\_Status: New\\ +Read\_Status\_Date: 2025-07-01T13:24:50.464Z}, + file = {/home/louis/snap/zotero-snap/common/Zotero/storage/5IKLA8LH/Hamilton et al. - 2018 - Inductive Representation Learning on Large Graphs.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/4X2VY4XN/1706.html} +} + @article{hoffLatentSpaceApproaches2002, title = {Latent {{Space Approaches}} to {{Social Network Analysis}}}, author = {Hoff, Peter D and Raftery, Adrian E and Handcock, Mark S},