activity
20162026
most citedFew-shot link prediction via graph neural networks for Covid-19 drug-repurposing

33 citations · 124 across the 30 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.IR2020★ 14 cited

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…

cs.LG2020★ 33 cited

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…

cs.LG2020

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…

cs.LG2020

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…

eess.SP2020

Efficient and Stable Graph Scattering Transforms via Pruning

Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis

Graph convolutional networks (GCNs) have well-documented performance in various graph learning tasks, but their analysis is still at its infancy. Graph scattering transforms (GSTs)…