Unverified50% confidenceFactExact time
The random-edge baseline (keeping node features and model hyperparameters unchanged, only randomly rewiring edges) performs better than the basic neural network but clearly worse than the GNN that retains the true connection relationships.
1
Sources
50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedGraph 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.70% similarUnverified神经网络输出连续概率分布而符号推理引擎需要离散结构化输入,两者接口设计(神经-符号接口问题)至今没有公认最优解67% similarUnverified卷积神经网络的核心创新在于权重共享与局部连接机制,通过滑动卷积核共享同一组参数降低模型参数量67% similarUnverifiedNeural networks have highly nonlinear and distributed representations: the same concept may be spread across a large number of neurons, and the same neuron may also participate in multiple concepts.67% similarUnverifiedNeural networks are more like something that is 'grown' rather than 'designed,' fine-tuned from a random state by providing data and learning algorithms.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1101057API
curl https://kongchang.com/api/v1/knowledge/claims/1101057MCP
get_claim(id=1101057)