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, there is a need for algorithms capable of efficiently analyzing dynamic graph data. Our research aims to address the challenges posed by the need for real-time insights and scalable processing in dynamic and complex networks.

However, many dynamic algorithms are sequential, tailored towards web graphs, do not utilize reducibility, locality benefits of SCCs, overestimate affected vertices, have high overhead, do not have well optimized implementations, do not take advantage of auxiliary information, and do not gracefully tolerate soft-faults which modern architectures introduce. Our dynamic approaches for PageRank and community detection address these issues. Our work has been accepted by IPDPS workshops (4), the Euro-Par conference (1), the ICPP conference (1), and the Complex Networks conference (1). Key outputs from our work include the design of a common framework for dynamic graph algorithms, and techniques to address soft faults in dynamic algorithms.

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