Challenge-kdn October 2020

challenge-kdn@knowledgedefinednetworking.org

2 participants
3 discussions

End of the evaluation phase and provisional ranking

by José Suárez-Varela

Dear participants of the Graph Neural Networking challenge 2020, First of all, thank you very much again on behalf of the BNN organizing team for participating in this challenge. We hope you enjoyed it a lot. Also, we expect it was a good opportunity for some of you to get introduced to the topics of network modeling and Graph Neural Networks. Once the evaluation phase has finished, we are glad to announce a provisional ranking with all the teams that submitted solutions for the challenge. You can find it at this link: https://bnn.upc.edu/challenge2020 We are very happy to see that you have made a really active participation. For the top-5 teams, please expect a separate email with detailed instructions on how to prepare the required documentation and the code to reproduce your solutions. For your interest, from the BNN team we have also prepared a possible solution for this challenge. Particularly, we have implemented a GNN model that introduces the queue entity as part of the internal GNN architecture. This enables to effectively model the impact of various queuing policies on delay. For more information, we have posted a short paper describing the solution and including some evaluation results on other datasets than those used in the challenge (https://arxiv.org/abs/2010.06686). Best regards, José Suárez-Varela Barcelona Neural Networking center Universitat Politècnica de catalunya

4 years, 12 months

End of the evaluation phase

by José Suárez-Varela

Dear participants, I would like to remind you that the end of the evaluation phase is approaching. You can make submissions until *October 15th at 23:59:59 (GMT+2)*. Once the evaluation finishes, we will post in our website a ranking (on Oct 16th) with all the teams that submitted solutions and their scores. Also, we will send instructions to the top-3 teams with details on how to prepare the documentation and the code to reproduce the training of their solutions. Best regards, José Suárez-Varela Barcelona Neural Networking center Universitat Politècnica de Catalunya

5 years

Correction in the instructions for the evaluation phase

by José Suárez-Varela

Dear participants, We found a small error in the document with the instructions for the evaluation phase. Particularly, to generate the output CSV format with your solutions you can use the following pseudo-code: String = “” For node_src in range (19): For node_dst in range (19): *If node_src != node_dst:* String += str(delay[node_src, node_dst])+”;” # New sample, new line String +=”\n” This skips the node pairs where the source and destination match, thus resulting in 342 src-dst delay values (i.e., 19 sources x 18 destinations = 342 values). Please find attached a new version of the instruction document fixing this error. Also, to help you validate the format of your submission files, I attach a ZIP file with the expected format when applying a solution to our toy validation dataset (https://github.com/knowledgedefinednetworking/RouteNet-challenge/tree/maste…). Note that in this case the results correspond to a 17-node network, so we have 17x16=272 src-dst delay values per line. However, with the final test dataset we should expect 342 values per line. *I would like to take this opportunity to encourage you to register on our evaluation platform in advance to avoid potential problems before the evaluation phase starts **(October 1st)**.* Best regards, José Suárez-Varela Barcelona Neural Networking center Universitat Politècnica de Catalunya

5 years

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Challenge-kdn October 2020