Search of new relations between bi-relational objects using their community information

What is it about?

Many aspects from real life with bi-relational structure can be modeled as bipartite networks. This modeling allows the use of some standard solutions for prediction and/or recommendation of new relations between these objects in such networks. Known as the link prediction task, it is a widely studied problem in network science for single graphs, networks assuming one type of interaction between vertices. For multi-layer networks, allowing more than one type of edges between vertices, the problem is not yet fully solved. Modeling the problem in this setting helps to aggregate different sources of information into one single structure and as a result to improve the quality of recommendation. In this work, we adopt existing techniques for those networks and propose alternative ways for exploiting connectivity information, which their objects share. Our method offers a good trade off between performance and efficiency comparing to other solutions based on different ideas.

Featured Image

Photo by Robynne Hu on Unsplash

Photo by Robynne Hu on Unsplash

Why is it important?

The motivation of this work comes from the importance of an application task, searching drug candidates for given biological targets. This task is an essential part of modern drug development. In this work, the problem is modeled as link prediction in a bipartite multi-layer network. Modeling the problem in this setting helps to aggregate different sources of information into one single structure and as a result to improve the quality of such prediction.

Perspectives