03-summary.tex

The world is in the midst of an unprecedented growth of interconnected data, and graph processing systems are expected to play a vital role. Conventional graph algorithms designed for static graphs struggle to efficiently handle the continuous changes and updates that occur within these networks. As these networks grow in complexity, the need for algorithms capable of efficiently analyzing dynamic graph data is increasingly crucial.

My PhD thesis focuses on ``Time and Space Efficient Social Network Algorithms for Static and Dynamic Graphs." I have worked on fast loading of graphs (text format) into memory using memory-mapping, outperforming state-of-the-art. I have also designed fast and memory-efficient algorithms for community detection utilizing (auto-generated) SIMD instructions via the Godbolt online compiler, and on GPUs using CUDA. Our algorithms outperform NVIDIA's cuGraph, while running on the CPU. Our work has been accepted by IPDPS workshops (4), Euro-Par workshops (3), the Euro-Par conference (1), the ICPP conference (1), and the Complex Networks conference (1). Other key outputs include fast algorithms for link prediction, the design of a common framework for dynamic graph algorithms, and techniques to address soft faults in dynamic algorithms.