[KDD 2020] Neural Subgraph Isomorphism Counting
Aug 13, 20205 views
In this paper, we study a new graph learning problem: learning,to count subgraph isomorphisms. Different from other traditional,graph learning problems such as node classification and link prediction, subgraph isomorphism counting is NP-complete and requires,more global inference to oversee the whole graph. To make it scalable for large-scale graphs and patterns, we propose a learning,framework that augments different representation learning architectures and iteratively attends pattern and target data graphs to,memorize intermediate states of subgraph isomorphism searching,for global counting. We develop both small graphs (,≤,1,024 subgraph isomorphisms in each) and large graphs (,≤,4,096 subgraph,isomorphisms in each) sets to evaluate different representation,and interaction modules. A mutagenic compound dataset,,MUTAG,, is also used to evaluate neural models and demonstrate the,success of transfer learning. While the learning based approach,is inexact, we are able to generalize to count large patterns and,data graphs in linear time compared to the exponential time of,the original NP-complete problem. Experimental results show that,learning based subgraph isomorphism counting can speed up the,traditional algorithm, VF2, 10-1,000 times with acceptable errors.,Domain adaptation based on fine-tuning also shows the usefulness,of our approach in real-world applications.