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Found 6 papers, 0 papers with code
Performing Co-Membership Attacks Against Deep Generative Models
We conduct extensive experiments on a variety of datasets and generative models showing that: our attacker network outperforms prior membership attacks; co-membership attacks can be substantially more powerful than single attacks; and VAEs are more susceptible to membership attacks compared to GANs.
Topology Based Scalable Graph Kernels
We propose a new graph kernel for graph classification and comparison using Ollivier Ricci curvature.
Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph
In this paper, we describe an embedding-based entity recommendation framework for Wikipedia that organizes Wikipedia into a collection of graphs layered on top of each other, learns complementary entity representations from their topology and content, and combines them with a lightweight learning-to-rank approach to recommend related entities on Wikipedia.
Ricci-GNN: Defending Against Structural Attacks Through a Geometric Approach
We propose a novel GNN defense algorithm against structural attacks that maliciously modify graph topology.
An Analysis Of Entire Space Multi-Task Models For Post-Click Conversion Prediction
Industrial recommender systems are frequently tasked with approximating probabilities for multiple, often closely related, user actions.
