诱导子图同构问题
子图同构问题
联营
可扩展性
计算机科学
图因式分解
图形
理论计算机科学
因子临界图
一般化
人工智能
数学
折线图
数据库
电压图
数学分析
作者
Shweta Ann Jacob,Paul Louis,Amirali Salehi‐Abari
标识
DOI:10.1145/3583780.3615227
摘要
Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph (e.g., identifying rare diseases given a collection of phenotypes). Graph neural network (GNN) solutions for node, link, and graph tasks fail to perform well on subgraph classification as they do not capture the external topology of the subgraph (i.e., how the subgraph is located within the larger graph). The current state-of-the-art models address this shortcoming through either labeling tricks or multiple message-passing channels, which are computationally expensive and not scalable to large graphs. To address the scalability issue while maintaining generalization, we propose Stochastic Subgraph Neighborhood Pooling (SSNP), which jointly aggregates the subgraph and its neighborhood (i.e., external topology) information while removing the need for any computationally expensive operations (e.g. labeling tricks). Our extensive experiments demonstrate that SSNP outperforms or is comparable to state-of-the-art methods while being up to 13x faster in runtime.
科研通智能强力驱动
Strongly Powered by AbleSci AI