The overall goal of this workshop is to bring together a multidisciplinary audience to discuss the algorithms, analysis, and learning on networks with a focus on helping solve real scientific problems. We broadly use the term “networks” to refer to sparsely connected datasets that are often modeled as graphs, hypergraphs, and simplicial complexes. The workshop is interested in the full pipeline that involves the creation, processing, and analysis of such datasets. 

This invite-only workshop will bring experts from a variety of scientific domains together with computational scientists, applied mathematicians, computer scientists, and the developers of computational tools and machine learning approaches that target network structured data to share recent advances, to brainstorm about potential new paths forward, and to develop new collaborations.  

The workshop will encompass research on topics including:

• algorithms for graph algorithms and analysis

• geometric and topological algorithms

• topological data analysis (TDA) for science 

• higher-order methods such as hypergraphs and simplicial complices

• graph learning such as graph neural networks (GNNs) and equivariant neural networks (ENNs)

• applications of network algorithms for scientific problems 

• applications of graph learning for scientific problems 

• high-performance algorithms, software, frameworks

• data structures and low-level optimizations for network algorithms and graph learning

• parallelism and accelerators for network algorithms, graph learning, TDA