Paper: https://arxiv.org/abs/2310.00149
Authors: Hao Liu, Jiarui Feng, Lecheng Kong, Ningyue Liang, Dacheng Tao, Yixin Chen, Muhan Zhang
OFA is a general Graph Classification Framework that can solves a wide range of graph classification tasks with a single model and a single set of parameters. The tasks are cross-domain (e.g. citation network, molecular graph,...) and cross-tasks (e.g. few-shot, zero-shot, graph-level, node-leve,...)
OFA use natural languages to describe all graphs, and use a LLM to embed all description in the same embedding space, which enable cross-domain training using a single model.
OFA propose a prompting paradiagm that all task information are converted to prompt graph. So subsequence model is able to read tasks information and predict relavent target accordingly, without having to adjust model parameters and architecture. Hence, a single model can be cross-task.
OFA curated a list of graph datasets from a different sources and domains and describe nodes/edges in the graphs with a systematical decription protocol. We thank previous works including, OGB, GIMLET, MoleculeNet, GraphLLM, and villmow for providing wonderful raw graph/text data that make our work possible.
To install requirement for the project using conda:
conda env create -f environment.yml
For joint end-to-end experiments on all collected dataset, run
python run_cdm.py --override e2e_all_config.yaml
All arguments can be changed by space separated values such as
python run_cdm.py --override e2e_all_config.yaml num_layers 7 batch_size 512 dropout 0.15 JK none
Users can modify the task_names variable in ./e2e_all_config.yaml to control which datasets are included during training. The length of task_names, d_multiple, and d_min_ratio should be the same. They can also be specified in command line arguments by comma separated values.
e.g.
python run_cdm.py task_names cora_link,arxiv d_multiple 1,1 d_min_ratio 1,1
OFA-ind can be specified by
python run_cdm.py task_names cora_link d_multiple 1 d_min_ratio 1
To run the few-shot and zero-shot experiments
python run_fs.py batch_size 30 num_epochs 30