Knowledge Dissemination Model Based on Graph Neural Network

DOI

10.2991/978-2-494069-31-2_370How to use a DOI?

Keywords

Basic Knowledge propagation model; Semi supervised learning; GCN model; Graph neural network

Abstract

Explore the application of GCN model to the prediction of knowledge transmission model. Teaching abstract into neural network, neural network model parameters education meaning, the model in the frequency domain space, design different convolution kernels, finally by GCN model of common teaching model, discusses several typical teaching model, the results of quantitative and qualitative analysis and comparison of practical teaching results, the rationality of the application of evaluation and scientific. The essence of different teaching organization modes of teachers is to adjust the graph neural network to predict the teaching effect through the model. Combined with the complexity of education and teaching process, the results of artificial intelligence algorithm are in line with the reality of education within the scope of assumptions, which is conducive to knowledge dissemination. Innovation will be applied to the education of the general consumption model. Without a once and for all teaching method, teachers should fully establish various teaching information platforms, cooperate with teaching and reasonable interaction, scientifically organize classroom teaching, establish learning groups composed of students who have mastered some knowledge, and speed up the efficiency of knowledge dissemination.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wei-ming Liu
PY  - 2022
DA  - 2022/12/29
TI  - Knowledge Dissemination Model Based on Graph Neural Network
BT  - Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022)
PB  - Atlantis Press
SP  - 3157
EP  - 3163
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-494069-31-2_370
DO  - 10.2991/978-2-494069-31-2_370
ID  - Liu2022
ER  -