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 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 the same framework. However, OpenANE not only integrates those PNE methods from OpenNE, 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 and user profiles in a social network. PNE methods that only consider structural information 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. Of course, OpenANE can also deal with pure network by calling PNE methods, or by assigning all ones as the attributes and then calling ANE 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.

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 or above is required due to the new print(f' ') feature

To obtain node embeddings as well as evaluate the quality by default tasks

python src/main.py --method abrw --emb-file cora_abrw_emb --save-emb

To have an intuitive feeling in node embeddings

python src/viz.py --emb-file cora_abrw_emb --label-file data/cora_label

Testing (Cora)

Parameter Setting

Currently, 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 Result

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

Visualization task

2D visualization results of the node embeddings on Cora dataset;
Steps: Cora -> NE method -> node embeddings -> PCA -> 2D viz;
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 -> (the edges between (id1, id2) and (id1, id3))
OR edgelist: node_id1 node_id2 weight(optional) -> one edge (id1, id2)
FILE for attribute information (each row):
node_id1 attr1 attr2 ... attrM
FILE for label (each row):
node_id1 label(s)

Parameters 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 together, we hope this OpenANE framework would become influential on both academic research and industrial usage.

References

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