Mustafa Yasir Presents Project Work at the 3rd Annual Workshop on Graph Learning Benchmarks at KDD 2023
Mustafa Yasir, a former Warwick Department of Computer Science student who graduated in Summer 2023, wrote up and presented an academic paper on the work carried out as part of his third year project. The paper was accepted to the 3rd Annual Workshop on Graph Learning Benchmarks at KDD 2023, and was presented in California by Mustafa.
Mustafa's third year project idea, supervised by Dr Long Tran-Thanh and titled 'Extending the Graph Generation Models of GraphWorld', started whilst he was interning at Google last summer. Mustafa contacted some researchers at the company working in the Graph ML space, to ask for any relevant project ideas. He bumped into a team who had just published GraphWorld: a tool to change the way Graph Neural Networks are benchmarked, by creating synthetic graph datasets through graph generation models – as opposed to using real-world datasets that are limited in their generalisability and present a major issue facing the field of Graph Learning.
However, since GraphWorld only used a single graph generation model in this process, Mustafa integrated two additional models with the system, ran large-scale GNN benchmarking experiments with these models and published his code to Google’s official GraphWorld repository. The project provides a significant advancement to researchers across the field looking to benchmark models and guide the development of new architectures.
Dr Long Tran-Thanh commented:
What Mustafa and the GraphWorld team has been working on is very important for the machine learning and AI research communities. In particular, there has been a vocal criticism against the whole field that most models are trained on the same public datasets (e.g., ImageNet, MNIST, etc), therefore are not diverse enough. One way to mitigate this issue is to generate realistically looking synthetic data. This need is especially of importance in within the graph learning community. GraphWorld’s aim is to address this exact problem by creating a powerful and convenient tool that can generate a diverse set of graphs, ranging from large social network-style graphs to molecule-inspired ones. Joining this project with the Google researchers is a huge opportunity for Warwick students to participate in a very impactful project.
DIMAP Theory Day 2022
On December 12, 2022, we held the DIMAP Theory Day 2022. This event highlighted recent, exciting advances in the field of Algorithms and Complexity and provided means to facilitate interactions within the algorithms research community in the UK. The event was supported by the Centre for Discrete Mathematics and its Applications (DIMAP) and UKRI. We plan to hold further events in this series on a regular basis.
See more details at the DIMAP Theory Day 2022 page
The University of Warwick will be hosting the Seventh Workshop on Algebraic Complexity Theory (WACT) from March 27 to March 31, 2023.
Algebraic Complexity Theory is a vibrant field that has been seeing a tremendous amount of activity in the recent years. Its classical questions have been interwoven with deep questions from algebraic geometry, invariant theory, and representation theory. Researchers study a wide range of interlinked topics: arithmetic circuit lower bounds, algorithmic algebra, algorithmic invariant theory, geometric complexity theory, tensor rank, polynomial identity testing, and polynomial reconstruction, to name a few. The workshop brings together experts from different parts of this rich field to discuss the current state of the art, discover new connections, and set the directions for the future.
SC22 Best Visualization Award Win for the Full Aero-Engine Compressor Visualization by Warwick Researchers
Numerical simulations and visualizations developed by researchers from the High Performance and Scientific Computing (HPSC) group at Warwick’s Department of Computer Science in collaboration with Rolls-Royce, PPCU Hungary and Universities of Surrey and Birmingham has won the award for the best Visualization in the Scientific Visualization and Data Analytics Showcase at the 2022 Supercomputing (SC) Conference, held in Dallas TX. SC is the premier international conference on supercomputing providing a major forum for presenting the highest level of accomplishments in high-performance computing, networking, storage, and analysis. It is held annually in the US and attended by over 10000 attendees from all over the world.
Full Aero-Engine Compressor Visualization Selected as Finalists for the SciVis Showcase at the Supercomputing 2022 Conference
Numerical simulations and visualizations developed by researchers from the High Performance and Scientific Computing (HPSC) group led by Dr. Gihan Mudalige at Warwick’s Department of Computer Science in collaboration with Rolls-Royce, PPCU Hungary and Universities of Surrey and Birmingham have been selected as one of the six finalists for the Scientific Visualization and Data Analytics Showcase at the 2022 Supercomputing (SC) Conference, held in Dallas TX. SC is the premier international conference on supercomputing providing a major forum for presenting the highest level of accomplishments in high-performance computing, networking, storage, and analysis. It is held annually in the US and attended by over 10000 attendees from all over the world. A video regarding the work can be found here.
Best Student Paper Award at ITCS 2022
We are delighted to announce that Peter Kiss, a PhD student in the Theory and Foundations Research Division, has won the Best Student Paper Award at the Innovations in Theoretical Computer Science (ITCS) 2022 conference for his single-author paper on "Deterministic Dynamic Matching in Worst-Case Update Time". Computing a maximum matching in a graph is one of the most fundamental problems in design and analysis of algorithms. The paper makes important progress on this problem in a setting where the input graph is changing over time via a sequence updates, and one wishes to maintain a large matching efficiently in such a dynamic graph. Along the way, the paper develops a general purpose technique for converting any dynamic algorithm with amortised update time into one with worst-case update time, provided the initial algorithm is able to handle a more general form of batch updates.