(Conference Paper) IGNNITION: fast prototyping of graph neural networks for communication networks
David Pujol-Perich ¹, José Suárez-Varela ¹, Miquel Ferriol-Galmés ¹, Bo Wu ², Shihan Xiao ², Xiangle Cheng ², Albert Cabellos-Aparicio ¹, Pere Barlet-Ros ¹
¹ Barcelona Neural Networking center, Universitat Politècnica de Catalunya, Spain
² Network Technology Lab., Huawei Technologies Co., Ltd.
SIGCOMM '21: Proceedings of the SIGCOMM '21 Poster and Demo Sessions, 2021-08-23

Graph Neural Networks (GNN) have recently exploded in the Machine Learning area as a novel technique for modeling graph-structured data. This makes them especially suitable for applications in the networking field, as communication networks inherently comprise graphs at many levels (e.g., topology, routing, user connections).

In this demo, we will present IGNNITION, an open-source framework for fast prototyping of GNNs applied to communication networks. This framework is especially designed for network engineers and/or researchers with limited background on neural network programming.

IGNNITION comprises a set of tools and functionalities that eases and accelerates the whole implementation process, from the design of a GNN model, to its training, evaluation, debugging, and integration into larger network applications. In the demo, we will show how a user can implement a complex GNN model applied to network performance modeling (RouteNet), following three simple steps.
IGNNITION: fast prototyping of graph neural networks for communication networks_1
IGNNITION: fast prototyping of graph neural networks for communication networks_2
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