Dynamic Network Alignment

I was privileged to attend the University of Notre Dame’s Data Intensive Scientific Computing Program, where I worked on Dynamic Network Alignment. At the Complex Networks (CoNe) Lab, I got to study under Prof. Tijana Milenkovic to understand the complex interactions in Social and Biological Networks. We applied Graph Theory approaches to add to the field of Network Alignment.

We worked on our presentation skills as a final project by filming videos explaining our summer research.

Our study defines network alignment as an injective (one-to-one) mapping between the compared networks. Network alignment can only be approximated as it falls into the category of NP-hard problems. Existing (approximate) network alignment methods allow for studying only static networks. However, most real-world complex systems are dynamic. With the recent increase of temporal network data available about such dynamic systems, it only seems natural to use the time information in network alignment. Hence, we aim to generalize an existing state-of-the-art method for aligning static networks to their dynamic counterpart to allow for aligning dynamic networks.