With the advent of easy means of gathering and storing data, we are able to acquire relational knowledge about data. This allows us to model very rich detail, and consequently learning approaches have to evolve to take advantage of this structure.
My work in this space falls under two categories - learning about the structure of the relations (or networks), and learning with data enriched by a network structure. My group has developed algorithms for studying several network properties and we have extensively collaborated with other researchers in applying these to novel domains. While the earlier work was somewhat serendipitous in nature, lately, I have been focused on the use of hypergraphs to model multi-way relations and devising approaches for studying properties of these networks. Our work in this space has appeared in top venues like ICDE, IJCAI, AAMAS, SIGMOD, JAIR, WINE, SDM, etc.
Our earlier work on the computation of network centrality by suitably defined network games has enabled efficient computations of complex centrality measures on very large graphs. Our group has also published work on distributed implementations of the same on massive graphs and an open source library was recently eleased under GPL3.
Our model of the Indian Railway network using hypergraphs was the first of its kind as is our work on centrality measures for hypergraphs. Among other measures, we have extended notions of Shapley values of network games to the hypergraph setting, and have developed a new notion of modularity for hypergraphs. Our recent work on learning to discover social network structure through graph embeddings and RL is among the first few works to address this problem. This paper was a best paper runner up at the AAMAS 2020 conference.