Quantifying privacy leakage in graph embedding

V Duddu, A Boutet, V Shejwalkar - MobiQuitous 2020-17th EAI …, 2020 - dl.acm.org
Graph embeddings have been proposed to map graph data to low dimensional space for
downstream processing (eg, node classification or link prediction). With the increasing …
被引用次数：122 相关文章

Quantifying Privacy Leakage in Graph Embedding

V Duddu, A Boutet, V Shejwalkar - Mobiquitous 2020-17th EAI …, 2020 - inria.hal.science
Graph embeddings have been proposed to map graph data to low dimensional space for
downstream processing (eg, node classification or link prediction). With the increasing …

Quantifying Privacy Leakage in Graph Embedding

V Duddu, A Boutet, V Shejwalkar - Mobiquitous 2020-17th EAI …, 2020 - hal.science
Graph embeddings have been proposed to map graph data to low dimensional space for
downstream processing (eg, node classification or link prediction). With the increasing …
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[PDF][PDF] Quantifying Privacy Leakage in Graph Embedding

V Duddu, A Boutet, V Shejwalkar - scholar.archive.org
Graph embeddings have been proposed to map graph data to low dimensional space for
downstream processing (eg, node classification or link prediction). With the increasing …

Quantifying Privacy Leakage in Graph Embedding

V Duddu, A Boutet, V Shejwalkar - arXiv e-prints, 2020 - ui.adsabs.harvard.edu
Graph embeddings have been proposed to map graph data to low dimensional space for
downstream processing (eg, node classification or link prediction). With the increasing …

Quantifying Privacy Leakage in Graph Embedding

V Duddu, A Boutet, V Shejwalkar - arXiv preprint arXiv:2010.00906, 2020 - arxiv.org
Graph embeddings have been proposed to map graph data to low dimensional space for
downstream processing (eg, node classification or link prediction). With the increasing …