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This project aims to develop and share fast frequent subgraph mining and graph learning algorithms. Currently we release the frequent subgraph mining package FFSM and later we will include new functions for graph regression and classification package
Approximate Subgraph Matching Algorithm for Dependency Graphs
The subgraph matching problem (subgraphisomorphism) is NP-complete. Previously, we designed
an exact subgraph matching (ESM) algorithm for dependency graphs using a backtracking approach
(http://esmalgorithm.sourceforge.net). We further designed an approximate subgraph matching (ASM)
algorithm that is capable of detecting approximate subgraph matching based on a subgraph
distance.
Exact Subgraph Matching Algorithm for Dependency Graphs
The subgraph matching problem (subgraphisomorphism) is NP-complete. We designed a simple exact subgraph matching (ESM) algorithm for dependency graphs using a backtracking approach. The total worst-case algorithm complexity is O(n^2 * k^n) where n is the number of vertices and k is the vertex degree.
We have demonstrated the successful usage of our algorithm in three biomedical relation and event extraction applications: BioNLP 2011 shared tasks on event extraction, Protein-Residue association detection and Protein-Protein interaction identification.
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