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Found 4 papers, 2 papers with code
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Translational research requires data at multiple scales of biological organization.
KG-Hub -- Building and Exchanging Biological Knowledge Graphs
Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking.
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs.
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding
The development of Graph Representation Learning methods for heterogeneous graphs is fundamental in several real-world applications, since in several contexts graphs are characterized by different types of nodes and edges.
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