Unveiling global interactive patterns across graphs: Towards interpretable graph neural networks
Graph Neural Networks (GNNs) have emerged as a prominent framework for graph mining,
leading to significant advances across various domains. Stemmed from the node-wise …
leading to significant advances across various domains. Stemmed from the node-wise …
Improving expressivity of gnns with subgraph-specific factor embedded normalization
Graph Neural Networks~(GNNs) have emerged as a powerful category of learning
architecture for handling graph-structured data. However, existing GNNs typically ignore …
architecture for handling graph-structured data. However, existing GNNs typically ignore …
LDSPool: Latent Dirichlet structure pooling with hierarchical graph context representation
In graph data analysis, particularly the graph classification task, a discriminative graph-level
representation is significant to improve classification performance. For the performance …
representation is significant to improve classification performance. For the performance …
Message-passing selection: Towards interpretable gnns for graph classification
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed
MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various …
MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various …
[PDF][PDF] EM-TSA: An ensemble machine learning-based transient stability assessment approach for operation of power systems
J Shen - Math. Biosci. Eng., 2023 - aimspress.com
The transient stability of power systems plays the key role in their smooth operation, which is
influenced by many working condition factors. To automatically evaluate transient stability …
influenced by many working condition factors. To automatically evaluate transient stability …