5.9 KiB
OpenANE: The first Open source framework specialized in Attributed Network Embedding (ANE)
We reproduce several ANE (Attributed Network Embedding) as well as PNE (Pure Network Embedding) methods in one unified framework, where they all share the same I/O and downstream tasks. We start this project based on the excellent project OpenNE that integrates several PNE methods under one unified framework.
OpenANE not only integrates those PNE methods that consider pure structural information, but also provides the state-of-the-art ANE methods that consider both structural and attribute information during embedding.
Authors: Chengbin HOU chengbin.hou10@foxmail.com & Zeyu DONG 2018
Motivation
In many real-world scenarios, a network often comes with node attributes such as paper metadata in a citation network, user profiles in a social network, and even node degrees in any pure networks. Unfortunately, PNE methods cannot make use of attribute information that may further improve the quality of node embeddings.
From engineering perspective, by offering more APIs to handle attribute information in graph.py and utils.py, OpenANE shall be very easy to use for embedding an attributed network. Except attributed networks, OpenANE can also deal with pure networks by calling PNE methods, OR by assigning node degrees (or all-ones) as node attributes and then calling ANE methods. Therefore, to some extent, ANE methods can be regarded as a generalization of PNE methods.
Methods
ABRW,
SAGE-GCN,
SAGE-Mean,
ASNE,
TADW,
AANE,
DeepWalk,
Node2Vec,
LINE,
GraRep,
AttrPure,
AttrComb
Note: all NE methods in this framework are unsupervised, and so do NOT require any labels during embedding phase.
For more details of each method, please have a look at our paper https://arxiv.org/abs/1811.11728
And if you find ABRW or this framework is useful for your research, please consider citing it.
Usages
Requirements
pip install -r requirements.txt
Python 3.6.6 or above is required due to the new print(f' ') feature
To obtain node embeddings as well as evaluate the quality
python src/main.py --method abrw --emb-file emb/cora_abrw_emb --save-emb --task lp_and_nc
To have an intuitive feeling in node embeddings
python src/vis.py --emb-file emb/cora_abrw_emb --label-file data/cora/cora_label.txt
Testing (Cora)
Parameter Settings
In this testing, we use the default parameters
| AANE_lamb | AANE_maxiter | AANE_rho | ABRW_alpha | ABRW_topk | ASNE_lamb | AttrComb_mode | GraRep_kstep | LINE_negative_ratio | LINE_order | Node2Vec_p | Node2Vec_q | TADW_lamb | TADW_maxiter | batch_size | dim | dropout | epochs | label_reserved | learning_rate | link_remove | number_walks | walk_length | weight_decay | window_size | workers |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.05 | 10 | 5 | 0.8 | 30 | 1 | concat | 4 | 5 | 3 | 0.5 | 0.5 | 0.2 | 10 | 128 | 128 | 0.5 | 100 | 0.7 | 0.001 | 0.1 | 10 | 80 | 0.0001 | 10 | 24 |
Testing Results
Link Prediction and Node Classification tasks:
| method | AUC | Micro-F1 | Macro-F1 | Time |
|---|---|---|---|---|
| aane | 0.8158 | 0.7263 | 0.6904 | 26.48 |
| abrw | 0.9290 | 0.8721 | 0.8603 | 48.94 |
| asne | 0.7842 | 0.6076 | 0.5649 | 69.67 |
| attrcomb | 0.9111 | 0.8444 | 0.8284 | 60.32 |
| attrpure | 0.7857 | 0.7349 | 0.7039 | 0.49 |
| deepwalk | 0.8499 | 0.8100 | 0.8021 | 75.15 |
| grarep | 0.8936 | 0.7669 | 0.7607 | 10.31 |
| line | 0.6945 | 0.5873 | 0.5645 | 259.02 |
| node2vec | 0.7938 | 0.7977 | 0.7858 | 29.42 |
| sagegcn | 0.8929 | 0.7780 | 0.7622 | 207.49 |
| sagemean | 0.8882 | 0.8057 | 0.7902 | 183.65 |
| tadw | 0.9005 | 0.8383 | 0.8255 | 10.73 |
2D Visualization task
Steps: Cora -> NE method -> node embeddings -> PCA -> 2D vis
The different colors indicate different ground truth labels.
Other Datasets
More well-prepared (attributed) network datasets are available at NetEmb-Datasets
Your Own Dataset
------ FILE for structural information (each row) ------
adjlist: node_id1 node_id2 node_id3
or edgelist: node_id1 node_id2 weight(optional)
------ FILE for attribute information (each row) ------
node_id1 attr1 attr2 ... attrM
------ FILE for label information (each row) ------
node_id1 label(s)
Parameter Tuning
For different dataset, one may need to search the optimal parameters instead of taking the default parameters. For the meaning and suggestion of each parameter, please see main.py.
Want to contribute
We highly welcome and appreciate your contributions on fixing bugs, reproducing new ANE methods, etc. And we hope this OpenANE framework would become influential on both academic research and industrial usage.
References
todo...