Lab-CA-SS26-resources

Resources for the Lab Computational Analytics

Julia Programming Language

Julia is a high-performance programming language for scientific computing, data analysis, and machine learning. It combines the ease of use of languages like Python with the speed of C.

For a explanation of why Julia is a great choice see Why Julia?

Installation

Installing Julia is straightforward, and you can find the instructions on the official website.

Setting up your development environment

We recommend using VS Code with the Julia extension.

Learning Julia

To learn Julia, you can refer to the following resources:

• The documentation
• Plenty of tutorials and examples
• Books and courses

Connecting with the Julia community

The Julia community is active and welcoming. You can connect with other Julia users and developers through the following channels:

• The Discourse forum
• The Slack channel
• Zulip chat

Git

We will use Git for version control and collaboration. In particular, we will use the VS Code Git extension for managing our repositories.

Reference Papers

For each project, we will refer to the following papers:

• Graph isomorphism:
  • 1dim-Weisfeiler-Leman algorithm
  • Weisfeiler-Lehman graph kernels
  • Weisfeiler-Lehman blog
  • Graph Isomorphism Network (GIN)
• Graph similarity:
  • Graph Edit Distance via Integer Linear Programming (ILP)
  • Graph Edit Distance Graph Neural Network (GED-GNN)
  • Graph Matching via Path Following (Convex-Concave Program)
  • Graph Matching with low-rank permutation representation (Kissing number)
  • Combinatorial Graph Alignment (Sequential GNNs)
• Node importance:
  • Betweenness Centrality approximation via KADABRA algorithm
  • BRAVA-GNN
  • ABCDE (for possible comparison)