- 1AndroidStudio降低编译版本报错_android studio sdk降低版本后很多报错
- 2多元回归预测 | Matlab基于卷积神经网络-双向门控循环单元结合注意力机制(CNN-BIGRU-Attention)回归预测，多变量输入模型_多变量输入预测回归
- 3手把手教你Android标准App的四大自动化测试法宝
- 4使用 OpenCV 进行 YOLO 对象检测_opencv识别yolo模型
- 5人工智能-基础篇03篇-模型中Encode，Decoder介绍及注意力机制介绍_encoder是什么模型
- 6最流行的开源 LLM （大语言模型）整理_开源llm
- 7python之pygame基本事件（二）_for event in pygame.event.get():
- 8基于Django+链家+Bootstrap真实数据的房源推荐/可视化系统_python房源推荐系统
- 9基于GAN的生成模型及其在深度学习中的应用_gan应用
- 10科大讯飞辟谣星火大模型套壳ChatGPT；谷歌发布AI语言模型PaLM 2；OpenAI CEO将赴美国为AI辩护丨每日大事件...
Graph Neural Networks: A Review of Methods and Applications（图神经网络：方法与应用综述）_graph neural networks: a review of methods and app
赞
踩
Graph Neural Networks: A Review of Methods and Applications
图神经网络：方法与应用综述
Jie Zhou , Ganqu Cui , Zhengyan Zhang , Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun
Abstract—Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics system, learning molecular fingerprints, predicting protein interface, and classifying diseases require that a model learns from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures, like the dependency tree of sentences and the scene graph of images, is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are connectionist models that capture the dependence of graphs via message passing between the nodes of graphs. Unlike standard
