# Topic3：Model Innovation

## Motivation：
* Model innovation combining traditional models and neural networks is a research hotspot.
* Deep probability model innovation: through the combination of neural network and probability model, the model can better help decision-making.
* Graph neural network: The neural network is combined with the traditional graph structure, oriented to cognitive reasoning and future trends.

## Target：
* Complete probability sampling library and probability inference (learning the probability distribution of the overall sample through known samples) algorithm library.
* Design new algorithms for dynamically changing heterogeneous graphs (different feature dimensions and different information aggregation methods)
* Trillion distributed graph data storage, segmentation and sampling

## Method：
​We expect the applicant can conduct model innovation research based on MindSpore, and hope to get your valuable suggestions to MindSpore in the process. We will do our best to improve the capabilities of the MindSpore framework and  provide you with the most powerful technical support.

## How To Join：
* Submit an issue/PR based on community discussion for consultation or claim on related topics
* Submit your proposal to us by email wang1@huawei.com