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Code and Datasets for Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

This is the code repo accompanying our paper "Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights."

We implement the following graph foundation model building blocks.

• Graph prompt models (OneForAll, Prodigy)
• GraphLLM (LLaGA)
• Graph Self-supervised learning (GraphMAE, BGRL, DGI, and so on)
• Link prediction-specific models, including BUDDY and SEAL

We support the following two scenarios.

• Co-training: Pre-training on a set of datasets and testing on the same ones
• Pre-training: Pre-training on a set of datasets and testing on unseen ones

Install

pip install -r requirements.txt

Datasets

We follow OneForAll's way of managing the datasets. We support the following datasets.

The processed file versions can be achieved from the following link.

Structures of the processed files:

• cache_data_{llm encoder name} (for example, minilm)
  • dataset_name
    • processed
      • data.pt
      • geometric_data_processed.pt
      • pre_filter.pt
      • pre_transform.pt
      • texts.pkl

geometric_data_processed.pt is the core storage object, and node_text_feat stores the processed node features. data.pt contains the index file used to query the attributes stored in geometric_data_processed.pt. A comprehensive introduction of each column can be found in OneForAll's repo.

To prepare the data, it's okay to generate all raw files yourself (run oneforall for 1 epoch, including all datasets). I recommend you use the preprocessed files directly and unzip them to the main directory.

Code Structures

Directories

• configs: Directory for setting the task/dataset for OneForAll. Add new datasets here
• data: data utility files/generation files using the OneForAll data interface
• gp: graph utility files from the original OneForAll repo
• graphllm: utility files for LLaGA
• graphmae: utility files for graphmae
• link: utility files for BUDDY
• models: model implementations
• prodigy: prodigy files
• subgcon: utility files/data files for self-supervised learning

Main entries

• eval_pretrain_*, eval_res: main files for LLaGA
• fulllink.py: main files for GCN link prediction
• linkpred.py: main files for BUDDY/SEAL
• run_cdm: main files for OFA
• sslmain: main files for SSL
• simplerlr: main files for simpleSBERT

Reproduce the results

OneForAll

• Co-training setting: just set up a config file similar to demo/e2e_all_config.yaml
• Pre-training setting: when loading the pre-trained model, use gnn_load_path.

LLaGA

1. Use llm_train.sh to generate checkpoints
2. Use llm_eval.sh or llm_eval_link.sh to generate the answer files for node/link-level tasks. For example, bash llm_eval.sh citeseer nc ./checkpoints/llaga-mistral-7b-hf-sbert-4-hop-token-linear-cora.3-citeseer.4-pubmed.3-nc-lp-projector/ citationcross
3. Use llmres.sh to calculate the results

GCN-link

python3 fulllink.py --pre_train_datasets "cora-link" "citeseer-link" "pubmed-link" "arxiv-link" "arxiv23-link" "bookhis-link" "bookchild-link" "sportsfit-link" "products-link" "elecomp-link" "elephoto-link" --encoder gcn --num_layers 3 --num_hidden 128 --batch_size 512

BUDDY/SEAL

python3 linkpred.py --pre_train_datasets cora citeseer arxiv arxiv23 bookhis bookchild elecomp elephoto sportsfit products pubmed wikics --model BUDDY --cache_subgraph_features --max_hash_hops 3 --epochs 50

python3 linkpred.py --pre_train_datasets cora --model SEALGCN --hidden_channels 256 --num_hops 3

SSL

Check the best hyper-parameter in the paper (use cpuinf can do full-batch inference on CPU, which is faster on our environment)

python3 sslmain.py --pre_train_datasets arxiv sportsfit products --method graphmae --num_heads 4 --num_out_heads 1 --num_layers 3 --num_hidden 1024 --residual --in_drop 0.5 --attn_drop 0.5 --norm 'batchnorm' --lr 0.01 --weight_decay 1e-5 --activation 'prelu' --mask_rate 0.75 --drop_edge_rate 0 --replace_rate 0.2 --scheduler --lrtype 'cosine' --save_model --max_epoch 5 --subgraph_size 1024 --warmup --cpuinf

Prodigy

pretrain on arxiv

python experiments/run_single_experiment.py --dataset arxiv --root <root> --original_features False -ds_cap 24000 -val_cap 100 -test_cap 100 --emb_dim 256 --epochs 1 -ckpt_step 1000 -layers S2,U,M -lr 3e-4 -way 30 -shot 3 -qry 4 -eval_step 5000 -task cls_nm_sb -bs 1 -aug ND0.5,NZ0.5 -aug_test True -attr 1000 --device 0 --prefix MAG_PT_PRODIGY

test on History

python3 experiments/run_single_experiment.py --dataset bookhis --original_features True -ds_cap 300 -val_cap 300 -test_cap 300 --emb_dim 256 --epochs 1 -ckpt_step 1000 -layers S2,U,M -lr 3e-4 -way 12 -shot 3 -qry 4 -eval_step 50 -task classification  -bs 1 -aug ND0.5,NZ0.5 -aug_test True -attr 1000 --device 0 --prefix test --root <root> -pretrained <ckpt> --eval_only True

Acknowledgements

This code repo is heavily based on OneForAll(✨), BUDDY, LLaGA, GraphMAE, Prodigy, CSTAG. Thanks for their sharing!