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@ -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},