RALE

Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph

We present the datasets and code for our paper "Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph", which is published in AAAI-2021.

1. Description for each file

datasets:
	- two datasets: Amazon and Email

model:
	- paramsConfigPython : parameters setting file
	- mainEntry.py : the main entry of the model
	- modelTraining.py : the model training file 
	- amazon_reddit_data_generator.py : online pipeline dataset generator for Amazon and Reddit
	- email_data_generator.py : online pipeline dataset generator for Email dataset
	- amazon_reddit_maml.py : the main model for Amazon and Reddit
	- email_maml.py : the main model for Email
	- self_attentionModel.py : position-aware self-attention
	- preprocessPre.py : the offline random walk and segments sampling part
	- preprocessTrain.py : offline pipeline generation for training
	- preprocessVal.py : offline pipeline generation for validation
	- preprocessTest.py : offline pipeline generation for test
	- prepareDatasetMatch2.py : the main functions of offline pipeline generation
	- processTools.py : some tool functions

RALE-AAAI-Appendix.pdf: Appendix file.

2. Requirements (Environment)

python-3.6.5
tensorflow-1.13.1

3. How to run

• (1) First configure the parameters in file paramsConfigPython

  --- precomputation (offline) ---

• (2) Run 'python3 preprocessPre.py', to random walk sample paths and generate the segments

• (3) Run 'python3 preprocessTrain.py', to generate the offline pipeline for training

• (4) Run 'python3 preprocessVal.py', to generate the offline pipeline for validation

• (5) Run 'python3 preprocessTest.py', to generate the offline pipeline for test

  --- model training and evaluation (online) ---

• (6) Run 'python3 mainEntry.py' for training and validation, here we should set 'ifTrain = True' (meaning for training) in file paramsConfigPython before running this command

• (7) Run 'python3 mainEntry.py' for test, here we should set 'ifTrain = False' (meaning for test) in file paramsConfigPython before running this command

Note that, if you have already prepared the pipeline for the model (namely, the steps 1, 2, 3, 4 and 5 are finished), the prepared pipeline would be saved on the disk (may occupy some space, according to the graph size). Then, you can directly run steps 6 and 7 repeatly without further data preparation.

4. Datasets

We include the Amazon and Email data in folder 'datasets'; while Reddit is too large so you can download it from link http://snap.stanford.edu/graphsage/, then process it into our format. And you can also prepare your own datasets. The data format should be as follows,

• (1) two files and a folder for class splits should be prepared: graph.node to describe the nodes, graph.edge to describe the edges, and folder datasets-splits to describe the class splits;
• (2) node file ( graph.node )
  • The first row is the number of nodes + tab + the number of features
  • In the following rows, each row represents a node: the first column is the node_id, the second column is the label_id of current node, and the third to the last columns are the features of this node. All these columns should be split by tabs.
• (3) edge file ( graph.edge )
  • Each row is a directed edge, for example : 'a tab b' means a->b. We can add another line 'b tab a' to represent this is a bidirection edge. All these values should be split by tabs.
• (4) datasets-splits:
  • All the classes should be split into train, val and test (each has its own file, e.g., file 'train-class-0', 'val-class-0' and 'test-class-0' are the 0-th split of classes for train, val and test, respectively).
  • For example, in file 'test-class-0', each line is one class id, and all the test classes (novel classes) are in this file.

5. Note

• The different distribution of the different classes splits (to form the training, validation and test set) may result in high standard deviation. Thus, for each data split, please run several times and average the results. The final result (node classification accuracy) could be achieved by further averaging the results of all the 5 splits.

• We use different main models for Amazon and Reddit (both of them share the same one), as well as Email. The main difference of them lies in the files of amazon_reddit_maml.py and email_maml.py. In particular, on Amazon and Reddit datasets, we use a one-layer GNN to calculate the node embeddings for further usage (such as to further calculate the path embeddings, etc.), and you can view lines 227 and 247 in file amazon_reddit_maml.py; on Email dataset, we use a two-layer GNN to calculate the node embeddings for further usage, and please check lines 236-237, and 259-260 in file email_maml.py. For your own usage, you can try these two models both (or either), since they mainly differ in the number of GNN layers for node embedding calculation.

6. Acknowledgments

This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-RP-2018-001).

7. Cite

@inproceedings{liu2021relative,
	title = {Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph},
	author = {Liu, Zemin and Fang, Yuan and Liu, Chenghao and Hoi, Steven CH},
	booktitle = {Proceedings of the Thirty-Fifth Conference on Association for the Advancement of Artificial Intelligence (AAAI)},
	year = {2021}
}