EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy | Paper Citation Record

Observation cae599b3-5d81-4380-89da-d846e73316c4 · outbound

This paper cites Acm digital library, 2024.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Acm digital library, 2024

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Grand: Graph reconstruction from potential partial adjacency and neighborhood data

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This paper cites A text and gnn based controversy detection method on social media.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy A text and gnn based controversy detection method on social media

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Bias assessment approaches for addressing user-centered fairness in gnn-based recommender systems.Information, 14(2):131, 2023

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Dblp computer science bibliography, 2024

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This paper cites Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds

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This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Convolutional neural networks on graphs with fast localized spectral filtering

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This paper cites A gnn- based recommender system to assist the specification of metamodels and models.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy A gnn- based recommender system to assist the specification of metamodels and models

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Unresolved cited work

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This paper cites Rappor: Randomized aggregatable privacy-preserving ordinal response.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Rappor: Randomized aggregatable privacy-preserving ordinal response

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This paper cites Pyg 2.0: Scalable learning on real world graphs.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Pyg 2.0: Scalable learning on real world graphs

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Graph neural networks: a survey on the links between privacy and security

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Observation 0185784f-788e-4e3f-b43c-dfcecffa363a · outbound

This paper cites A deep graph neural network-based mechanism for social recommendations.IEEE Transactions on Industrial Informatics, 17(4):2776– 2783, 2020.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy A deep graph neural network-based mechanism for social recommendations.IEEE Transactions on Industrial Informatics, 17(4):2776– 2783, 2020

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Observation 948cd5a7-b1d1-4a4d-858c-1d9388e8e167 · outbound

This paper cites Hagberg, Daniel A.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Hagberg, Daniel A

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This paper cites Harris, K.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Harris, K

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Accurate estimation of the degree distribution of private networks

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This paper cites Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy The distribution of the flora in the alpine zone

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This paper cites Semi-supervised learning with graph learning-convolutional networks.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Semi-supervised learning with graph learning-convolutional networks

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This paper cites GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics

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This paper cites Eflec: Efficient feature-leakage correction in gnn based recommendation systems.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Eflec: Efficient feature-leakage correction in gnn based recommendation systems

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This paper cites Graph-based privacy-preserving data publication.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Graph-based privacy-preserving data publication

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This paper cites Private graph data release: A survey.ACM Computing Surveys, 55(11):1–39, 2023.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Private graph data release: A survey.ACM Computing Surveys, 55(11):1–39, 2023

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy The link prediction problem for social networks

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This paper cites Towards private learning on decen- tralized graphs with local differential privacy.IEEE Transactions on Information Forensics and Security, 17:2936–2946, 2022.

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Towards private learning on decen- tralized graphs with local differential privacy.IEEE Transactions on Information Forensics and Security, 17:2936–2946, 2022

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Muse-gnn: Learning unified gene representation from multimodal biological graph data.Advances in neural information processing systems, 36:24661–24677, 2023

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Cora dataset, 2016

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Sgnn: A graph neural network based federated learning approach by hiding structure

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy SoK: Differential Privacy on Graph-Structured Data

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Automatic differentiation in pytorch

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Pedregosa, G

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Generating synthetic decentralized social graphs with local differential privacy

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Deeprank- gnn: a graph neural network framework to learn patterns in protein–protein interfaces.Bioinformatics, 39(1):btac759, 2023

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Pitfalls of Graph Neural Network Evaluation

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Graph attention networks

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Distributionally robust graph-based recommendation system

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Linkteller: Recovering private edges from graph neural networks via influence analysis

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Unresolved cited work

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy How powerful are graph neural networks? In7th International Conference on Learning Rep- resentations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy GraphPub: Generation of Differential Privacy Graph with High Availability

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Consisrec: Enhancing gnn for social recommendation via consistent neighbor aggregation

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy A 2 s 2-gnn: Rigging gnn-based social status by adversarial attacks in signed social networks

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy PrivGraph: Differentially private graph data publication by exploiting community information

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Psgraph: Differentially private streaming graph synthesis by considering temporal dynamics.arXiv e-prints, pages arXiv–2412, 2024

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Pri-pgd: Forging privacy-preserving graph towards spectral-based graph neural network

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Privdpr: Synthetic graph publishing with deep pagerank under differential privacy

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy A survey on privacy in graph neural networks: Attacks, preservation, and applications.IEEE Transactions on Knowledge and Data Engineering, 2024

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Artificial in- telligence in bioinformatics and drug repurposing: methods and applications, 2022

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EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy Unresolved cited work

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