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.

Name	#Graphs	#Nodes	#Edges	Domains	Tasks	#classes
Cora	1	2708	10556	CS Citation	Node, Link	7
CiteSeer	1	3186	8450	CS Citation	Node, Link	6
Arxiv	1	169343	2315598	CS Citation	Node, Link	40
Arxiv23	1	46198	77726	CS Citation	Node, Link	40
History	1	41551	503180	E-commerce	Node, Link	12
Child	1	76875	2325044	E-commerce	Node, Link	24
Computers	1	87229	1256548	E-commerce	Node, Link	10
Photo	1	48362	873782	E-commerce	Node, Link	12
Sportsfit	1	173055	3020134	E-commerce	Node, Link	13
Products	1	316513	19337722	E-commerce	Node, Link	39
Amazon Ratings	1	24492	186100	E-commerce	Node, Link	5
Pubmed	1	19717	88648	Bio Citation	Node, Link	3
WikiCS	1	11701	431726	Knowledge	Node, Link	10
Tolokers	1	11758	1038000	Anomaly	Node, Link	2
DBLP	1	14376	431326	CS Citation	Node, Link	4
CheMBL	365065	26	112	Biology	Graph	1048
PCBA	437092	26	56	Biology	Graph	128
HIV	41127	26	55	Biology	Graph	2
Tox21	7831	19	39	Biology	Graph	12
Bace	1513	34	74	Biology	Graph	2
Bbbp	2039	24	52	Biology	Graph	2
Muv	93087	24	53	Biology	Graph	17
Toxcast	8575	19	39	Biology	Graph	588

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!