Unverified50% confidenceFactExact time
The upper bound on the expressive power of GNNs is closely related to the graph isomorphism problem, and the Weisfeiler-Leman (WL) graph isomorphism test provides a theoretical framework for characterizing the expressive power of GNNs.
1
Sources
50%
Confidence
Long-term
Relevance
9/18/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedGNoME基于图神经网络（GNN），通过学习Materials Project等密度泛函理论（DFT）数据库中已知晶体结构稳定性规律进行预测69% similarUnverifiedGraph Neural Networks (GNNs) can directly handle non-Euclidean data such as molecular structures, knowledge graphs, and social networks by performing message passing and aggregation on graph structures.67% similarUnverifiedResearchers use graph neural networks (GNN) to learn neutronics simulations of breeding blankets, enabling sub-second prediction of TBR values for arbitrary combinations of geometric parameters with prediction errors controlled within 1%.64% similarUnverified早期目标检测模型如YOLO、Faster R-CNN只能识别预定义类别，而GLIP、Grounding DINO等模型推动了理解任意自然语言指令的视觉grounding进步63% similarUnverifiedBuilding a dynamically evolving code knowledge graph requires combining multiple AI technologies such as graph neural networks (GNNs), temporal analysis, and causal inference.63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/932549API
curl https://kongchang.com/api/v1/knowledge/claims/932549MCP
get_claim(id=932549)