From d0cd5ec6e939559e162c782c44a1883d6f65e14d Mon Sep 17 00:00:00 2001 From: Vincent Labatut Date: Tue, 7 Jan 2025 22:43:30 +0100 Subject: [PATCH] Update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 70d9aa6..0a6e345 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ SWGE is free software: you can redistribute it and/or modify it under the terms * **Lab site:** http://lia.univ-avignon.fr/ * **GitHub repo:** https://github.com/CompNet/SWGE -* **Data:** https://doi.org/10.5281/zenodo.13851362 +* **Data:** https://doi.org/10.5281/zenodo.13851361 * **Contact:** Noé Cécillon , Vincent Labatut ----------------------------------------------------------------------- @@ -37,7 +37,7 @@ In addition, these scripts reproduce the experiments described in our paper [[CL ## Data -The scripts are meant to be applied to a corpus of three datasets constituted of signed networks annotated for graph classification. Because of GitHub's file size limit, we include only a few graphs from each dataset in the `data` folder. The full datasets can be downloaded from [Zenodo](https://doi.org/10.5281/zenodo.13851362). Place the downloaded graphs directly into the corresponding folders in `data`. +The scripts are meant to be applied to a corpus of three datasets constituted of signed networks annotated for graph classification. Because of GitHub's file size limit, we include only a few graphs from each dataset in the `data` folder. The full datasets can be downloaded from [Zenodo](https://doi.org/10.5281/zenodo.13851361). Place the downloaded graphs directly into the corresponding folders in `data`. ## Organization @@ -70,7 +70,7 @@ After running the previous scripts, you can perform the classification by runnin ## References -* **[CLDA'24]** N. Cécillon, V. Labatut, R. Dufour, N. Arınık: *Whole-Graph Representation Learning For the Classification of Signed Networks*, IEEE Access (in press), 2024. DOI: [10.1109/ACCESS.2024.3472474](https://dx.doi.org/10.1109/ACCESS.2024.3472474) [⟨hal-04712854⟩](https://hal.archives-ouvertes.fr/hal-04712854) +* **[CLDA'24]** N. Cécillon, V. Labatut, R. Dufour, N. Arınık: *Whole-Graph Representation Learning For the Classification of Signed Networks*, IEEE Access 12:151303-151316, 2024. DOI: [10.1109/ACCESS.2024.3472474](https://dx.doi.org/10.1109/ACCESS.2024.3472474) [⟨hal-04712854⟩](https://hal.archives-ouvertes.fr/hal-04712854) * **[NCVC'17]** A. Narayanan, M. Chandramohan, R. Venkatesan, L. Chen, Y. Liu, and S. Jaiswal: *graph2vec: Learning distributed representations of graphs*, International Workshop on Mining and Learning with Graphs, 2017. URL: [http://www.mlgworkshop.org/2017/paper/MLG2017_paper_21.pdf] * **[DMT'18]** T. Derr, Y. Ma, and J. Tang: *Signed graph convolutional network*, 18th International Conference on Data Mining, 2018, p.929-934. DOI: [10.1109/ICDM.2018.00113](https://doi.org/10.1109/ICDM.2018.00113). * **[WTAC'17]** S. Wang, J. Tang, C. Aggarwal, Y. Chang, and H. Liu. *Signed network embedding in social media*. 17th SIAM International Conference on Data Mining, 2017, p.327-335. DOI: [10.1137/1.9781611974973.37](https://doi.org/10.1137/1.9781611974973.37).