Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleMarch 2024
A time series and deep fusion framework for rotating machinery fault diagnosis
Engineering Applications of Artificial Intelligence (EAAI), Volume 128, Issue Chttps://doi.org/10.1016/j.engappai.2023.107456AbstractFor shafting rotation equipment, in the fault diagnosis based on vibration analysis, the sampling signal is a waveform of a short moment, which is quasi-static information at the corresponding moment. A single quasi-static piece of information ...
- research-articleNovember 2023
An Efficient Distributed Graph Engine for Deep Learning on Graphs
SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and AnalysisPages 922–931https://doi.org/10.1145/3624062.3624169Traditional graph-processing algorithms have been widely used in Graph Neural Networks (GNNs). This combination has shown state-of-the-art performance in many real-world network mining tasks. Current approaches to graph processing in deep learning face ...
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training
SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and AnalysisArticle No.: 39, Pages 1–12https://doi.org/10.1145/3581784.3607056Memory-based Temporal Graph Neural Networks are powerful tools in dynamic graph representation learning and have demonstrated superior performance in many real-world applications. However, their node memory favors smaller batch sizes to capture more ...
- research-articleDecember 2022
Throughput optimization in heterogeneous MIMO networks: a GNN-based approach
GNNet '22: Proceedings of the 1st International Workshop on Graph Neural NetworkingPages 42–47https://doi.org/10.1145/3565473.3569191With the development of 5G and IoT networks, Device-to-Device (D2D) communication has become a major paradigm in wireless communication. Most existing approaches for D2D resource allocation are usually time consuming and demand a high computational ...
- research-articleAugust 2022
Research on Stability Analysis and Guaranteed Performance Control of Networked Control System in Nuclear Power Plants
2022 IEEE International Conference on Mechatronics and Automation (ICMA)Pages 1393–1397https://doi.org/10.1109/ICMA54519.2022.9856147Steam generator is one of the important equipment of nuclear power unit, it is a highly complex system, the difficulty of its water level control is the phenomenon of false water level and wide range of parameter changes, which brings many difficulties to ...
TGL: a general framework for temporal GNN training on billion-scale graphs
Proceedings of the VLDB Endowment (PVLDB), Volume 15, Issue 8Pages 1572–1580https://doi.org/10.14778/3529337.3529342Many real world graphs contain time domain information. Temporal Graph Neural Networks capture temporal information as well as structural and contextual information in the generated dynamic node embeddings. Researchers have shown that these embeddings ...
- short-paperOctober 2021
SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementPages 3667–3671https://doi.org/10.1145/3459637.3482177Temporal Knowledge Graphs store events in the form of subjects, relations, objects, and timestamps which are often represented by dynamic heterogeneous graphs. Event forecasting is a critical and challenging task in Temporal Knowledge Graph reasoning ...
- research-articleMay 2021
Accelerating large scale real-time GNN inference using channel pruning
Proceedings of the VLDB Endowment (PVLDB), Volume 14, Issue 9Pages 1597–1605https://doi.org/10.14778/3461535.3461547Graph Neural Networks (GNNs) are proven to be powerful models to generate node embedding for downstream applications. However, due to the high computation complexity of GNN inference, it is hard to deploy GNNs for large-scale or real-time applications. ...