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Graph Neural Network Study Tutorial

 

Stanford CS224W Tutorials

The  Stanford CS224W course has collected a set of graph machine learning tutorial blog posts, fully realized with PyG. Students worked on projects spanning all kinds of tasks, model architectures and applications. All tutorials also link to a Colab with the code in the tutorial for you to follow along with as you read it!

PyTorch Geometric Tutorial Project

The  PyTorch Geometric Tutorial project provides video tutorials and  Colab notebooks for a variety of different methods in PyG:

1. Introduction [ YouTube,  Colab]

2. PyTorch basics [ YouTube,  Colab]

3. Graph Attention Networks (GATs) [ YouTube,  Colab]

4. Spectral Graph Convolutional Layers [ YouTube,  Colab]

5. Aggregation Functions in GNNs [ YouTube,  Colab]

6. (Variational) Graph Autoencoders (GAE and VGAE) [ YouTube,  Colab]

7. Adversarially Regularized Graph Autoencoders (ARGA and ARGVA) [ YouTube,  Colab]

8. Graph Generation [ YouTube]

9. Recurrent Graph Neural Networks [ YouTube,  Colab (Part 1),  Colab (Part 2)]

10. DeepWalk and Node2Vec [ YouTube (Theory),  YouTube (Practice),  Colab]

11. Edge analysis [ YouTube,  Colab (Link Prediction),  Colab (Label Prediction)]

12. Data handling in PyG (Part 1) [ YouTube,  Colab]

13. Data handling in PyG (Part 2) [ YouTube,  Colab]

14. MetaPath2vec [ YouTube,  Colab]

15. Graph pooling (DiffPool) [ YouTube,  Colab]

text summarise dataset

**Paper:**

https://arxiv.org/abs/1908.08345

**Dataset:**

1) the CNN/DailyMail news highlights dataset: somewhat Extractive

- News Articles & Related Highlights: Provides a brief overview of articles

- Input document: limited to 512 tokens

- https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail

2) the New York Times Annotated Corpus (NYT): somewhat Extractive

- Contains 110,540 articles with abstract summaries

- Input document : limited to 800 tokens

- https://research.google/resources/datasets/ny-times-annotated-corpus/

3) XSum: Abstractive

- 226,711 news articles answering the question of ‘What is this articles about?’ + one-sentence summaries

- Input document: limited to 512 tokens

- https://github.com/google-research-datasets/xsum_hallucination_annotations

Introduction to Large Language Models (G-LLM-I)

 Introduction to Large Language Models (G-LLM-I)

Here are the assembled readings on large language models:

1. NLP's ImageNet moment has arrived
2. Google Cloud supercharges NLP with large language models
3. LaMDA: our breakthrough conversation technology
4. Language Models are Few-Shot Learners
5. PaLM-E: An embodied multimodal language model
6. Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance
7. PaLM API & MakerSuite: an approachable way to start prototyping and building generative AI applications
8. The Power of Scale for Parameter-Efficient Prompt Tuning
9. Google Research, 2022 & beyond: Language models
10. Accelerating text generation with Confident Adaptive Language Modeling (CALM)
11. Solving a machine-learning mystery

And here are the assembled readings on generative AI:

1. Ask a Techspert: What is generative AI?
2. Build new generative AI powered search & conversational experiences with Gen App Builder
3. What is generative AI?
4. Google Research, 2022 & beyond: Generative models
5. Building the most open and innovative AI ecosystem
6. Generative AI is here. Who Should Control It?
7. [Stanford U & Google’s Generative Agents Produce Believable Proxies of Human Behaviors](https://syncedreview.com/2023/04/12/stanford-u-googles-generative-agents-produce-belie

All Readings: Introduction to Generative AI (G-GENAI-I)

All Readings: Introduction to Generative AI (G-GENAI-I)
Here are the assembled readings on generative AI:

●  Ask a Techspert: What is generative AI? https://blog.google/inside-google/googlers/ask-a-techspert/what-is-generative-ai/

●  Build new generative AI powered search & conversational experiences with Gen App Builder:
https://cloud.google.com/blog/products/ai-machine-learning/create-generative-apps-in-
minutes-with-gen-app-builder

●  What is generative AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai

●  Google Research, 2022 & beyond: Generative models: https://ai.googleblog.com/2023/01/google-research-2022-beyond-language.html#Gener ativeModels

●  Building the most open and innovative AI ecosystem: https://cloud.google.com/blog/products/ai-machine-learning/building-an-open-generativ e-ai-partner-ecosystem

●  Generative AI is here. Who Should Control It? https://www.nytimes.com/2022/10/21/podcasts/hard-fork-generative-artificial-intelligen ce.html

●  Stanford U & Google’s Generative Agents Produce Believable Proxies of Human Behaviors:
https://syncedreview.com/2023/04/12/stanford-u-googles-generative-agents-produce-b
elievable-proxies-of-human-behaviours/

●  Generative AI: Perspectives from Stanford HAI: https://hai.stanford.edu/sites/default/files/2023-03/Generative_AI_HAI_Perspectives.pd f

●  Generative AI at Work: https://www.nber.org/system/files/working_papers/w31161/w31161.pdf

●  The future of generative AI is niche, not generalized: https://www.technologyreview.com/2023/04/27/1072102/the-future-of-generative-ai-is- niche-not-generalized/
Here are the assembled readings on large language models:

●  NLP's ImageNet moment has arrived: https://thegradient.pub/nlp-imagenet/

●  Google Cloud supercharges NLP with large language models:
https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-supercharge
s-nlp-with-large-language-models

●  LaMDA: our breakthrough conversation technology: https://blog.google/technology/ai/lamda/

torch tensor padding example code:

 refer to code:

.

import torch

import torch.nn.functional as F

tensor = torch.randn(2, 3, 4) # Original tensor

print("Original tensor shape:", tensor.shape)

# Case 1: Pad the last dimension (dimension -1) -> resulting shape: [2, 3, 8]

padding_size = 4

padded_tensor = F.pad(tensor, (padding_size, 0)) # Add padding to the left of the last dimension

print("Case 1 tensor shape:", padded_tensor.shape)

# Case 2: Pad the second-to-last dimension (dimension -2) -> resulting shape: [2, 8, 4]

padding_size = 5

padded_tensor = F.pad(tensor, (0, 0, padding_size, 0)) # Add padding to the left of the second-to-last dimension

print("Case 2 tensor shape:", padded_tensor.shape)

# Case 3: Pad the first dimension (dimension 0) -> resulting shape: [7, 3, 4]

padding_size = 5

padded_tensor = F.pad(tensor, (0, 0, 0, 0, padding_size, 0)) # Add padding to the left of the first dimension

print("Case 3 tensor shape:", padded_tensor.shape)

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