Learning Dynamic Preference Structure Embedding From Temporal Networks

Tongya, Zheng,; Feng, Zunlei; Wang, Yu; Shen, Chengchao; Song, Mingli; Wang, Xingen; Wang, Xinyu; Chen, Chun; Xu, Hao

doi:10.1109/ickg52313.2021.00059

Cited by 2 publications

(2 citation statements)

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“…Formally, a graph consisting of n nodes can be represented as G = (A, X), where A ∈ R n×n is adjacency matrix and X ∈ R n×d is the node feature matrix. To efficiently aggregate node features with adjacency information, graph neural networks (GNNs) [12]- [17] are developed to learn more powerful representations by considering topological structure information. Given a graph consisting of n nodes, GNNs generally follow a message-passing architecture:…”

Section: A Graph Neural Networkmentioning

confidence: 99%

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Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System

Chen¹,

Liu²,

Yu³

et al. 2022

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The real-time transient stability assessment (TSA) plays a critical role in the secure operation of the power system. Although the classic numerical integration method, i.e. timedomain simulation (TDS), has been widely used in industry practice, it is inevitably trapped in a high computational complexity due to the high latitude sophistication of the power system. In this work, a data-driven power system estimation method is proposed to quickly predict the stability of the power system before TDS reaches the end of simulating time windows, which can reduce the average simulation time of stability assessment without loss of accuracy. As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system. Motivated by observing the distribution information of crucial active power and reactive power on the power system's bus nodes, we thus propose a distribution-aware learning (DAL) module to explore an informative graph representation vector for describing the status of a power system. Then, TSA is re-defined as a binary classification task, and the stability of the system is determined directly from the resulting graph representation without numerical integration. Finally, we apply our method to the online TSA task. The case studies on the IEEE 39-bus system and Polish 2383-bus system demonstrate the effectiveness of our proposed method. The code is available at https://github.com/kxchern/dkepool-tsa

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Section: A Graph Neural Networkmentioning

confidence: 99%

“…The propagation function M can be implemented in various manners [12]- [17]. The recursive neighborhood aggregation scheme enables the node features to absorb the structure information.…”

Section: A Graph Neural Networkmentioning

confidence: 99%