Fault-tolerant Subgraph Matching on Aligned Networks

Thomas K. Tu, Dominic Yang

Abstract: In the context of networks curated from real world data, an important problem is that of aligning entities across multiple data sources. Methods for performing alignment are often error-prone due to lack of information and noise within the data itself. When performing subgraph matching on the resulting aligned networks, these alignment errors often lead to catastrophic results. In this paper, we propose a fault-tolerant algorithm for subgraph matching on aligned networks which takes into account potential alignment errors.