@@ -6,18 +6,18 @@ Gmatch4py is a library dedicated to graph matching. Graph structure are stored i
...
@@ -6,18 +6,18 @@ Gmatch4py is a library dedicated to graph matching. Graph structure are stored i
* DeltaCon and DeltaCon0 (*debug needed*) [1]
* DeltaCon and DeltaCon0 (*debug needed*) [1]
* Vertex Ranking (*debug needed*) [2]
* Vertex Ranking (*debug needed*) [2]
* Vertex Edge Overlap [3]
* Vertex Edge Overlap [2
* Graph kernels
* Graph kernels
* Random Walk Kernel (*debug needed*) [4]
* Random Walk Kernel (*debug needed*) [3]
* Geometrical
* Geometrical
* K-Step
* K-Step
* Shortest Path Kernel [4]
* Shortest Path Kernel [3]
* Weisfeiler-Lehman Kernel [5]
* Weisfeiler-Lehman Kernel [4]
* Subtree Kernel
* Subtree Kernel
* Edge Kernel
* Edge Kernel
* Subtree Geo Kernel [new]
* Subtree Geo Kernel [new]
* Edge Geo Kernel [new]
* Edge Geo Kernel [new]
* Graph Edit Distance [6]
* Graph Edit Distance [5]
* Approximated Graph Edit Distance
* Approximated Graph Edit Distance
* Hausdorff Graph Edit Distance
* Hausdorff Graph Edit Distance
* Bipartite Graph Edit Distance
* Bipartite Graph Edit Distance
...
@@ -27,11 +27,10 @@ Gmatch4py is a library dedicated to graph matching. Graph structure are stored i
...
@@ -27,11 +27,10 @@ Gmatch4py is a library dedicated to graph matching. Graph structure are stored i
## Publications associated
## Publications associated
* [1] Koutra, D., Vogelstein, J. T., & Faloutsos, C. (2013, May). Deltacon: A principled massive-graph similarity function. In Proceedings of the 2013 SIAM International Conference on Data Mining (pp. 162-170). Society for Industrial and Applied Mathematics.
* [1] Koutra, D., Vogelstein, J. T., & Faloutsos, C. (2013, May). Deltacon: A principled massive-graph similarity function. In Proceedings of the 2013 SIAM International Conference on Data Mining (pp. 162-170). Society for Industrial and Applied Mathematics.
* [2] ICDM 2014
* [2] Papadimitriou, P., Dasdan, A., & Garcia-Molina, H. (2010). Web graph similarity for anomaly detection. Journal of Internet Services and Applications, 1(1), 19-30.
* [3] Papadimitriou, P., Dasdan, A., & Garcia-Molina, H. (2010). Web graph similarity for anomaly detection. Journal of Internet Services and Applications, 1(1), 19-30.
* [3] Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., & Borgwardt, K. M. (2010). Graph kernels. Journal of Machine Learning Research, 11(Apr), 1201-1242.
* [4] Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., & Borgwardt, K. M. (2010). Graph kernels. Journal of Machine Learning Research, 11(Apr), 1201-1242.
* [4] Shervashidze, N., Schweitzer, P., Leeuwen, E. J. V., Mehlhorn, K., & Borgwardt, K. M. (2011). Weisfeiler-lehman graph kernels. Journal of Machine Learning Research, 12(Sep), 2539-2561.
* [5] Shervashidze, N., Schweitzer, P., Leeuwen, E. J. V., Mehlhorn, K., & Borgwardt, K. M. (2011). Weisfeiler-lehman graph kernels. Journal of Machine Learning Research, 12(Sep), 2539-2561.
* [5] Fischer, A., Riesen, K., & Bunke, H. (2017). Improved quadratic time approximation of graph edit distance by combining Hausdorff matching and greedy assignment. Pattern Recognition Letters, 87, 55-62.
* [6] Fischer, A., Riesen, K., & Bunke, H. (2017). Improved quadratic time approximation of graph edit distance by combining Hausdorff matching and greedy assignment. Pattern Recognition Letters, 87, 55-62.
