Paper on adversarial machine learning & graphs accepted at KDD 2018


Our paper "Adversarial Attacks on Neural Networks for Graph Data" has been accepted as an oral/long paper at KDD 2018! In our paper we study the novel problem of adversarial machine learning for graphs, specifically considering state-of-the-art node classification approaches such as (deep) graph convolutional networks. Congratulations to my co-authors Daniel Zügner and Amir Akbarnejad!