Inexact Graph Matching Using Centrality Measures

Matching-centrality decomposition and the forecasting of new links in networks

Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. First we show, for a diverse set of networks, that this decomposition provides an extremely tight fit to observed networks. Consequently, the model allows very accurate prediction of missing links in partially known networks. Second, when node characteristics are known…

基于GNN的锚点嵌入用于高效精确子图匹配

GNN-based Anchor Embedding for Efficient Exact Subgraph Matching

针对现有深度学习子图匹配方法多为近似、传统精确方法效率低的问题，提出基于GNN的锚点嵌入框架GNN-AE，通过离线索引特征子图与并行匹配增长算法实现高效精确子图匹配，性能较基线最高提升1-2个数量级。