104 citations · 126 across the 2 of their papers we have counts for
2 papers
cs.LG2019★ 104 cited
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy +3
Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep lea…
cs.LG2019★ 22 cited
Efficient Representation Learning Using Random Walks for Dynamic Graphs
Hooman Peiro Sajjad, Andrew Docherty, Yuriy Tyshetskiy
An important part of many machine learning workflows on graphs is vertex representation learning, i.e., learning a low-dimensional vector representation for each vertex in the grap…