Ryan A. Rossi

Graphlet decomposition: framework, algorithms, and applications

From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks. While graphlets have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and efficient framework for computing the frequencies of these subgraph patterns. However, existing methods are not scalable to large networks with billions of nodes and edges. In this paper, we propose a fast, efficient, and parallel framework as well as a family of algorithms for counting k-node graphlets. The proposed framework leverages a number of theoretical combinatorial arguments that allow us to obtain significant improvement on the scalability of graphlet counting. For each edge, we count a few graphlets and obtain the exact counts of others in constant time using the combinatorial arguments. On a large collection of

Estimation of local subgraph counts

300+

Parallel collective factorization for modeling large heterogeneous networks

300+ networks from a variety of domains, our graphlet counting strategies are on average

Leveraging Multiple GPUs and CPUs for Graphlet Counting in Large Networks

460{times }

NetworkRepository: A Graph Data Repository with Visual Interactive Analytics.

460× faster than existing methods. This brings new opportunities to investigate the use of graphlets on much larger networks and newer applications as we show in the experiments. To the best of our knowledge, this paper provides the largest graphlet computations to date.

Toward Interactive Relational Learning

Scientific data repositories have historically made data widely accessible to the scientific community, and have led to better research through comparisons, reproducibility, as well as further discoveries and insights. Despite the growing importance and utilization of data repositories in many scientific disciplines, the design of existing data repositories has not changed for decades. In this paper, we revisit the current design and envision interactive data repositories, which not only make data accessible, but also provide techniques for interactive data exploration, mining, and visualization in an easy, intuitive, and free-flowing manner.