Giter Site home page Giter Site logo

pairnorm's People

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar

pairnorm's Issues

What's the insight for scale-and-center mode?

I found there is another mode, scale-and-center, which hasn't been mentioned in the paper.

It looks a little bit weird to me that SCS mode first scales and then centers the representations, where the mean is computed based on the statistics before scaling. Could you explain the insight behind that?

Equation (6)?

Thank you for your great methodology!
Anyway, where is the code for actually optimizing equation (6)?

Thank you :)

SGC add PN effect

I don’t use pairnorm, I use SGC to run 50 layers, and the effect can reach about 73%. Why is it a little bit different from the report in the paper?

What's the split of CoauthorCS?

Dear authors,
I'm confused with the split of CoauthorCS, "we randomly split all nodes into train/val/test as 3%/10%/87%".Dose it mean that we sample nodes consdering the label distribution, like the setting in cora, 20 nodes per class, or just totally randomly pick up the nodes in the whole set?I'm looking forward to your reply.It will help me a lot!

Questions on getting the results shown in the table

Hi Lingxiao,

Thank you for the great code. I am new to this area. So I would like to apologize first, considering that my questions might be trivial.

I wonder how to get the results shown in Table 2 of the paper. For example, for GCN-PN with 10 layers and 100% missing rate on Cora, I run the following command:
python main.py --data cora --model DeepGCN --nlayer 10 --missing_rate 100 --norm_mode PN-SI --residual 0
Instead of getting the acc of 0.731 shown in the paper, I obtained the following results:
Test set results: loss 1.084, acc 0.637.
I also found the same issue for other items in the table.

There might be something wrong in my experimental settings, and I would greatly appreciate it if you could help me. Thank you in advance.

Best,
Yongcheng

The difference between Pairnorm and Batchnorm.

Hi,
Really nice work! After reading your paper, I have a question about the difference between Pairnorm and Batchnorm, especially under the inductive setting. Could you please provide some insights?

Thank you!

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.