Merge branch 'ECML' of

Author: LucasPotin98

README.md

@@ -9,6 +9,12 @@ Pang is free software: you can redistribute it and/or modify it under the terms
 # Description
 Pang is an algorithm which represents and classifies a collection of graphs according to their frequent patterns (subgraphs).
 
+**Content**
+* [Organization](#organization)
+* [Installation](#installation)
+* [Usage](#usage)
+* [Dependencies](#dependencies)
+* [References](#references)
 
 # Organization
 This repository is composed of the following elements:
@@ -45,7 +51,7 @@ In order to run the script that reproduces our ECML PKDD experiments, you also n
 
 
 ## Data
-Third, you need to set up the data to which you want to apply Pang. This can be the dataset from our paper, in which you will need to unzip several archives, or your own data, in which case they need to be respect the appropriate format. In both cases, see cf. Section [Use](#use).
+Third, you need to set up the data to which you want to apply Pang. This can be the dataset from our paper, in which you will need to unzip several archives, or your own data, in which case they need to be respect the appropriate format. In both cases, see cf. Section [Usage](#usage).
 
 # Use
 We provide two scripts to use Pang:
@@ -133,20 +139,24 @@ Tested with `SPMF` version 2.54, which implements gSpan [[Y'02](#references)] (t
 
 For the ECML PKDD assessment, we use the following algorithms for the sake of comparison:
 
-* The `WL` and `WLOA` algorithms are included in the `Grakel` library, documentation available [here](https://ysig.github.io/GraKeL/0.1a8/benchmarks.html)
-* `Graph2Vec` is included in the `karateclub` library, documentation available [here](https://karateclub.readthedocs.io/en/latest/)
-* `DGCNN` is included in the `stellargraph` library, documentation available [here](https://stellargraph.readthedocs.io/en/stable/).
-* We use the implementation of `CORK` from Marisa Thoma. This implementation is available in the `CORKcpp.zip` archive, from [here](http://www.dbs.ifi.lmu.de/~thoma/pub/sam2010/sam2010.zip)
+* The `WL` [[S'11](#references)] and `WL_OA` [[K'16](#references)] algorithms are included in the `Grakel` library, documentation available [here](https://ysig.github.io/GraKeL/0.1a8/benchmarks.html)
+* `Graph2Vec` [[N'17](#references)] is included in the `karateclub` library, documentation available [here](https://karateclub.readthedocs.io/en/latest/)
+* `DGCNN` [[Z'18](#references)] is included in the `stellargraph` library, documentation available [here](https://stellargraph.readthedocs.io/en/stable/).
+* We use the implementation of `CORK` [[T'09](#references)] from Marisa Thoma. This implementation is available in the `CORKcpp.zip` archive.
 
 
 # References
+* **[C'04]** L. P. Cordella, P. Foggia, C. Sansone, M. Vento. *A (sub)graph isomorphism algorithm for matching large graphs*, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(10):1367-1372, 2004. DOI: [10.1109/tpami.2004.75](https://doi.org/10.1109/tpami.2004.75)
 * **[D'91]** A. S. Debnath, R. L. Lopez, G. Debnath, A. Shusterman, C. Hansch. *Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity*, Journal of Medicinal Chemistry 34(2):786–797, 1991. DOI: [10.1021/jm00106a046](https://doi.org/10.1021/jm00106a046)
 * **[D'03]** P. D. Dobson, A. J. Doig. *Distinguishing enzyme structures from non-enzymes without alignments*, Journal of Molecular Biology 330(4):771–783, 2003. DOI: [10.1016/S0022-2836(03)00628-4](https://doi.org/10.1016/S0022-2836(03)00628-4)
 * **[H'14']** M. Houbraken, S. Demeyer, T. Michoel, P. Audenaert, D. Colle, M. Pickavet. *The Index-Based Subgraph Matching Algorithm with General Symmetries (ISMAGS): Exploiting Symmetry for Faster Subgraph Enumeration*, PLoS ONE 9(5):e97896, 2014. DOI: [10.1371/journal.pone.0097896](https://doi.org/10.1371/journal.pone.0097896).
+* **[K'16]** N. M. Kriege, P. L. Giscard, R. Wilson. *On Valid Optimal Assignment Kernels and Applications to Graph Classification*, 30th International Conference on Neural Information Processing Systems, pp. 1623–1631, 2016. URL: [here](https://proceedings.neurips.cc/paper_files/paper/2016/hash/0efe32849d230d7f53049ddc4a4b0c60-Abstract.html)
+* **[N'17]** A. Narayanan, M. Chandramohan, R. Venkatesan, L. Chen, Y. Liu, S. Jaiswal. *graph2vec: Learning Distributed Representations of Graphs*, 13th International Workshop on Mining and Learning with Graphs, p. 21, 2017. URL: [here](https://arxiv.org/abs/1707.05005)
 * **[P'22]** L. Potin, V. Labatut, R. Figueiredo, C. Largeron, P.-H. Morand. *FOPPA: A database of French Open Public Procurement Award notices*, Technical Report, Avignon University, 2022.  [⟨hal-03796734⟩](https://hal.archives-ouvertes.fr/hal-03796734)
+* **[S'11]** N. Shervashidze, P. Schweitzer, E. J. van Leeuwen, K. Mehlhorn, K. M. Borgwardt. *Weisfeiler-Lehman Graph Kernels*, Journal of Machine Learning Research 12:2539–2561, 2011. URL: [here](https://dl.acm.org/citation.cfm?id=2078187)
 * **[S'21]** Z. Shaul, S. Naaz. *cgSpan: Closed Graph-Based Substructure Pattern Mining, IEEE International Conference on Big Data, pp. 4989-4998, 2021. DOI: [10.1109/bigdata52589.2021.9671995](https://doi.org/10.1109/bigdata52589.2021.9671995)
 * **[T'03]** H. Toivonen, A. Srinivasan, R. D. King, S. Kramer, C. Helma. *Statistical evaluation of the predictive toxicology challenge 2000-2001*, Bioinformatics 19(10):1183–1193, 2003. DOI: [10.1093/bioinformatics/btg130](https://doi.org/10.1093/bioinformatics/btg130)
+* **[T'09]** M. Thoma, H. Cheng, A. Gretton, J. Han, H.-P. Kriegel, A. Smola, S. Le, P. S. Yu, X. Yan, K. Borgwardt. *Near-optimal supervised feature selection among frequent subgraphs*, SIAM International Conference on Data Mining, pp. 1076-1087, 2009. DOI: [10.1137/1.9781611972795.92](http://doi.org/10.1137/1.9781611972795.92)
 * **[W'06]** N. Wale, G. Karypis. *Comparison of descriptor spaces for chemical compound retrieval and classification*, 6th International Conference on Data Mining, pp. 678–689, 2006. DOI: [10.1007/s10115-007-0103-5](https://doi.org/10.1007/s10115-007-0103-5)
 * **[Y'02]** X. Yan, J. Han. *gSpan: Graph-based substructure pattern mining*, IEEE International Conference on Data Mining, pp.721-724, 2002. DOI: [10.1109/ICDM.2002.1184038](https://doi.org/10.1109/ICDM.2002.1184038)
-* ** [C'04]** L. P. Cordella, P. Foggia, C. Sansone, M. Vento. *A (sub)graph isomorphism algorithm for matching large graphs*, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(10):1367-1372, 2004. DOI: [10.1109/tpami.2004.75](https://doi.org/10.1109/tpami.2004.75)
-* 
+* **[Z'18]** D. Zhang, J. Yin, X. Zhu, C. Zhang. *Network Representation Learning: A Survey*, IEEE Transactions on Big Data 6(1):3–28, 2018. DOI: [10.1109/tbdata.2018.2850013](http://doi.org/10.1109/tbdata.2018.2850013)