Spotlight

Take a look at this month's iCeNSA spotlight! A monthly segment in which we interview an iCeNSA professor from a different discipline and learn a little bit more about their background, research interests, and current projects. Read More

Biosystems

The Complex Networks Lab lead by Prof. T. Milenkovic from Computer Science and Engineering at Notre Dame develops novel computational methods for network analysis and uses them to study cellular functioning and molecular causes of disease from biological networks, in hope to suggest novel candidates for therapeutic intervention. Importantly, the methods are also applicable to other types of real-world networks, e.g., social or technological networks. There are several on-going projects in the lab, as follows.Read More

Environment/Climate

The network structure and evolution over time provide interesting insights into the behavior of the earth system. For example, ocean climate indicators extracted from the networks have proven to be good predictors of climate variables over land. Currently, we are studying the dynamic behavior and stability of the network over timeRead More

Healthcare

Faced by enormous health care costs and an unsustainable system, more efficient medical practices are needed. This motivates the shift toward preventative and individualized medicine, both of which require a better understanding of disease interactions and the underlying genetic and biological influences.
Using disease ontologies and genetic information, we construct disease networks that capture the co-morbities among diseases, at the phenotype as well as the genetic level, enabling a better understanding as well as predictability.Read More

Human Interaction Networks

Link prediction is the task of predicting previously unobserved relationships between entities. There are many exciting applications of this particular area of network science. Most research in the area of link prediction has been restricted to scoring based on a single measure within network topologies. Our work is developing a powerful new measure and placing existing measures in the context of a machine learning task. We are also casting the problem as a high class imbalance task.Read More

Infrastructure/Transport

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