Browsing by Author "Wu, Bo"
Now showing items 1-8 of 8
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IGNNITION: A framework for fast prototyping of Graph Neural Networks
(2021)
Conference report
Open AccessRecent years have seen the vast potential of Graph Neural Networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, logistics). However, implementing a GNN prototype is still a cumbersome task that ... -
IGNNITION: Bridging the gap between graph neural networks and networking systems
(2021-11)
Article
Open AccessRecent years have seen the vast potential of graph neural networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, recommender systems). In particular, GNNs are becoming increasingly popular in ... -
IGNNITION: fast prototyping of graph neural networks for communication networks
(Association for Computing Machinery (ACM), 2021)
Conference lecture
Open AccessGraph 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 ... -
Is machine learning ready for traffic engineering optimization?
(Institute of Electrical and Electronics Engineers (IEEE), 2021)
Conference report
Open AccessTraffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization. We address this open question through a ... -
NetXplain: Real-time explainability of graph neural networks applied to computer networks
(2021)
Conference report
Open AccessRecent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle complex optimization problems. However, existing DL-based solutions are often considered as black boxes due to their high inner ... -
NetXplain: Real-time explainability of graph neural networks applied to networking
(2021-08-05)
Article
Open AccessRecent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle complex optimization problems. However, existing DL-based solutions are often considered as black boxes with high inner ... -
RouteNet-Erlang: A graph neural network for network performance evaluation
(Institute of Electrical and Electronics Engineers (IEEE), 2022)
Conference report
Open AccessNetwork modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the ... -
Towards real-time routing optimization with deep reinforcement learning: open challenges
(Institute of Electrical and Electronics Engineers (IEEE), 2021)
Conference report
Open AccessThe digital transformation is pushing the existing network technologies towards new horizons, enabling new applications (e.g., vehicular networks). As a result, the networking community has seen a noticeable increase in ...
