add GLEM model, TAGDataset and example of GLEM

CHANGELOG.md

@@ -7,6 +7,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added `nn.models.GLEM` ([#9662](https://github.com/pyg-team/pytorch_geometric/pull/9662))
+- Added `TAGDataset` ([#9662](https://github.com/pyg-team/pytorch_geometric/pull/9662))
 - Consolidated `examples/ogbn_{papers_100m,products_gat,products_sage}.py` into `examples/ogbn_train.py` ([#9467](https://github.com/pyg-team/pytorch_geometric/pull/9467))
 
 ### Changed

examples/llm/README.md

@@ -1,5 +1,10 @@
 # Examples for Co-training LLMs and GNNs
 
-| Example                              | Description                                                                                                                                                 |
-| ------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [`g_retriever.py`](./g_retriever.py) | Example for Retrieval-Augmented Generation (RAG) w/ GNN+LLM by co-training `LLAMA2` with `GAT` for answering questions based on knowledge graph information |
+| Example                              | Description                                                                                                                                                                                    |
+| ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [`g_retriever.py`](./g_retriever.py) | Example for Retrieval-Augmented Generation (RAG) w/ GNN+LLM by co-training `LLAMA2` with `GAT` for answering questions based on knowledge graph information                                    |
+| [`glem.py`](./glem.py)               | Example for [GLEM](https://arxiv.org/abs/2210.14709), a GNN+LLM co-training model via variational Expectation-Maximization (EM) framework on node classification tasks to achieve SOTA results |
+
+## Run GLEM for getting SOTA result on ogbn-products dataset
+
+`python glem.py`