A Novel Software Toolkit for Graph Edit Distance Computation

By Kaspar Riesen, S. Emmenegger, Horst Bunke. 2013

102 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity (2019, methodology): “We run the ground-truth GED solvers on a CPU server with 32 cores, and utilize at most 20 cores at the same time, using code from (Riesen et al., 2013).”
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs (2014, unknown): “D.2.4 [Software Engineering]: Software/Program Verification— Validation; D.4.6 [Operating Systems]: Security and Protection— Invasive software”
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (2018, methodology): “Since graphs from AIDS and LINUX are relatively small, an exponential-time exact GED computation algorithm named A* [36] is used to compute the GEDs between all the graph pairs.”
Similarity Search in Graph Databases: A Multi-Layered Indexing Approach (2017, methodology): “In our experiments, we use the state-of-the-art method [22] for exact GED verification.”