Filtering Strategies for Inexact Subgraph Matching on Noisy Multiplex Networks
Alexei Kopylov, Jiejun Xu · 2019
We study the problem of detecting matching subgraphs in a large multiplex background network based on predefined subgraph templates. Our approach extends existing filtering-based subgraph matching algorithms and proposes a new set of filters leveraging the monotone function properties in the multiplex setting. This enables effective pruning of irrelevant subgraph regions and expedites the overall matching process. In addition, our approach proposes a new strategy based on maximum likelihood estimate to identify “closely matched” subgraphs that are not isomorphic to the given templates from a noisy background network. This allows us to generalize this approach to real-world networks, which are often noisy, incomplete and ambiguous. We demonstrate the effectiveness of the proposed method on a real-world multiplex network provided by the DARPA Modeling Adversarial Activity (MAA) program. Our approach obtains highly accurate subgraph matching results for both the clean and noisy versions of the network, which significantly outperforms the baseline filtering methods. Furthermore, our proposed approach is parallelizable such that it can scale up to handle large input networks.