Session
MS4F - High Performance Graph Analytics
Session Chairs
Event TypeMinisymposium
Computational Methods and Applied Mathematics
TimeTuesday, June 416:00 - 18:00 CEST
LocationHG D 1.2
DescriptionEstimating the structure, partitioning, and analyzing graphs, are all critical tasks in a plethora of applications. Problems in domains such as image processing, social network analysis, and classification via neural networks, are often formulated as being graph-based. Simultaneously, graph analytic methods are traditionally an important subtask that enables the parallelization or the complexity reduction of the entire algorithmic workflow. This minisymposium samples recent advances in methods intended for graphs emerging from large-scale data, with a focus on performant, efficient, and scalable algorithms.
Presentations
16:00 - 16:30 CEST | Parallel Algorithms for Dynamic Graph Clustering | |
16:30 - 17:00 CEST | Faster Local Motif Clustering via Maximum Flows | |
17:00 - 17:30 CEST | Algebraic Programming for Graph & Machine Learning | |
17:30 - 18:00 CEST | 3S in Distributed Graph Neural Networks: Sparse Communication, Sampling, and Scalability |