AI@Clemson

It was great discussing AI and text mining at Clemson University research  symposium on AI.

Welcome new students!

Two new students, Zirou Qiu (MSc) and Korey Palmer (senior undergrad) are joining our research group.

Relaxation-Based Coarsening for Multilevel Hypergraph Partitioning

Accepted paper at SIAM Multiscale Modeling and Simulations

Ruslan Shaydulin, Jie Chen, Ilya Safro "Relaxation-Based Coarsening for Multilevel Hypergraph Partitioning", 2019, preprint at arXiv:1710.06552

Multilevel partitioning methods that are inspired by principles of multiscaling are the most powerful practical hypergraph partitioning solvers. Hypergraph partitioning has many applications in disciplines ranging from scientific computing to data science. In this paper we introduce the concept of algebraic distance on hypergraphs and demonstrate its use as an algorithmic component in the coarsening stage of multilevel hypergraph partitioning solvers. The algebraic distance is a vertex distance measure that extends hyperedge weights for capturing the local connectivity of vertices which is critical for hypergraph coarsening schemes. The practical effectiveness of the proposed measure and corresponding coarsening scheme is demonstrated through extensive computational experiments on a diverse set of problems. Finally, we propose a benchmark of hypergraph partitioning problems to compare the quality of other solvers.

Thesis defense

Congratulations to Varsha Chauhan for successfully defending her MSc thesis on planar graph generation! 

Travel awards

Congratulations to Justin Sybrandt and Ruslan Shaydulin for receiving travel awards to present their papers at IEEE Big Data 2018 and APS 2018!

Thesis defense

Congratulations to Dr. Hayato Ushijima-Mwesigwa for successfully defending his Ph.D. thesis "Models for Networks with Consumable Resources"!

Community detection on NISQ devices

Accepted paper at at 3rd International Workshop on Post Moore's Era 2018

Supercomputing (PMES 2018)

Ruslan Shaydulin, Haayto Ushijima-Mwesigwa, Ilya Safro, Susan Mniszewski, Yuri Alexeev "Community Detection Across Emerging Quantum Architectures", preprint at arXiv:1810.07765, 2018

Can we predict crimes in Chicago?

Our paper is accepted at IEEE Big Data 2018

Saroj K. Dash, I. Safro, Ravisutha S. Srinivasamurthy "Spatio-temporal prediction of crimes using network analytic approach", preprint at arXiv:1808.06241, 2018

It is quite evident that majority of the population lives in urban area today than in any time of the human history. This trend seems to increase in coming years. Studies say that nearly 80.7% of total population in USA stays in urban area. By 2030 nearly 60% of the population in the world will live in or move to cities. With the increase in urban population, it is important to keep an eye on criminal activities. By doing so, governments can enforce intelligent policing systems and hence many government agencies and local authorities have made the crime data publicly available. In this paper, we analyze Chicago city crime data fused with other social information sources using network analytic techniques to predict criminal activity for the next year. We observe that as we add more layers of data which represent different aspects of the society, the quality of prediction is improved. Our prediction models not just predict total number of crimes for the whole Chicago city, rather they predict number of crimes for all types of crimes and for different regions in City of Chicago.

Two papers accepted at IEEE Big Data 2018

Sybrandt, Carrabba, Herzog, Safro "Are Abstracts Enough for Hypothesis Generation?", arXiv:1804.05942

Sybrandt, Shtutman, Safro "Large-Scale Validation of Hypothesis Generation Systems via Candidate Ranking", arXiv:1802.03793