33 citations · 78 across the 7 of their papers we have counts for
12 papers
Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
Cole Hawkins, Vassilis N. Ioannidis, Soji Adeshina +1
Consistency training is a popular method to improve deep learning models in computer vision and natural language processing. Graph neural networks (GNNs) have achieved remarkable p…
Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks
Konstantinos D. Polyzos, Costas Mavromatis, Vassilis N. Ioannidis +1
Uncovering anomalies in attributed networks has recently gained popularity due to its importance in unveiling outliers and flagging adversarial behavior in a gamut of data and netw…
COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature
Colby Wise, Vassilis N. Ioannidis, Miguel Romero Calvo +6
The coronavirus disease (COVID-19) has claimed the lives of over 350,000 people and infected more than 6 million people worldwide. Several search engines have surfaced to provide r…
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing
Vassilis N. Ioannidis, Da Zheng, George Karypis
Predicting interactions among heterogenous graph structured data has numerous applications such as knowledge graph completion, recommendation systems and drug discovery. Often time…
PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs
Vassilis N. Ioannidis, Da Zheng, George Karypis
Learning unsupervised node embeddings facilitates several downstream tasks such as node classification and link prediction. A node embedding is universal if it is designed to be us…
Tensor Graph Convolutional Networks for Multi-relational and Robust Learning
Vassilis N. Ioannidis, Antonio G. Marques, Georgios B. Giannakis
The era of "data deluge" has sparked renewed interest in graph-based learning methods and their widespread applications ranging from sociology and biology to transportation and com…