104 citations · 204 across the 13 of their papers we have counts for
3 papers · 1 filter
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…
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…
Evaluating approaches for supervised semantic labeling
Natalia Ruemmele, Yuriy Tyshetskiy, Alex Collins
Relational data sources are still one of the most popular ways to store enterprise or Web data, however, the issue with relational schema is the lack of a well-defined semantic des…