most citedNetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data

24 citations · 24 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2021★ 24 cited

NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data

I-Chung Hsieh, Cheng-Te Li

Recent advances in protecting node privacy on graph data and attacking graph neural networks (GNNs) gain much attention. The eye does not bring these two essential tasks together y…

cs.SI2021

CoANE: Modeling Context Co-occurrence for Attributed Network Embedding

I-Chung Hsieh, Cheng-Te Li

Attributed network embedding (ANE) is to learn low-dimensional vectors so that not only the network structure but also node attributes can be preserved in the embedding space. Exis…