Name
Abstract | ||
|---|---|---|
In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a powerful tool for (supervised) machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine learning setting. We discuss the theoretical background, show how to use it for supervised graph- and node classification, discuss recent extensions, and its connection to neural architectures. Moreover, we give an overview of current applications and future directions to stimulate research. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.24963/ijcai.2021/618 | IJCAI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher H. Morris | 1 | 46 | 7.42 |
| Matthias Fey | 2 | 71 | 5.17 |
| Nils Kriege | 3 | 99 | 13.11 |