Mining patterns in a dynamic attributed graph has received more and more attention recently. However, it is a complex task because both graph topology and attributes values of each vertex can change over time. In this work, we focus on the discovery of frequent sequential subgraph evolutions (FSSE) in such a graph. These FSSE patterns rep- resent frequent evolutions of general sets of connected vertices’ attribute values. A novel algorithm, named FSSEMiner, is proposed to mine FSSE patterns. This algorithm is based on a new strategy (graph addition) to guarantee mining efficiency. Experiments performed on benchmark and real-world datasets show the interest of our approach and its scalability.
We run two experiments with the ffollowing three objectives :
- to evaluate the efficiency of our proposed algorithm to mine the datasets by comparing its performances in terms of the number of attributes, the number of timestamps and the number of edges and vertices
- to evaluate the accuracy and pertinence of the results
To execute this code, you need :
- A c++ envrionnement using g++
- Openmp installed
To compile the code, run the following command :
g++ -g "/your/path/to/Source.cpp" -fopenmp -o "/your/path/to/Source.out"Then execute Source.out.