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Error propagation-driven graph neural network with implicit layers
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DOI：10.1016/j.patcog.2026.114763.png)
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Abstract
En 中文
<ul class="list">
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="d1e1367">
Analyze system stability and perturbation propagation dynamics.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="d1e1372">
Propose constrained non-homogeneous ODE-based IGNN.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="d1e1377">
Provide theoretical guarantees on stability and robustness bounds.
</div></span></li>
</ul>
Keywords:
Graph neural networks
Implicit neural networks
Semi-supervised learning
Optimization-inspired models
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W