OpenANE/README.md

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 OpenNE project 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 the paper's title in a citation network and user profiles in a social network. PNE methods that only consider structural information cannot make use of attribute information which may further improve the quality of node embedding.

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: 1) by calling PNE methods; and 2) by assigning ones as the attribute for all nodes and then calling ANE methods (but some ANE methods may fail).

Methods (todo... Chengbin)

ABRW, SAGE-GCN, SAGE-Mean, ASNE, TADW, AANE, DeepWalk, Node2Vec, LINE, GraRep, AttrPure, AttrComb,

Requirements (todo... Zeyu; double check)

pip install -r requirements.txt

Python 3.6 or above is required due to the new print(f' ') feature

Usages

To obtain your node embeddings and evaluate them by classification downstream tasks

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

To have an intuitive feeling of node embeddings (todo... Zeyu if possible; need tf installed)

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

Parameters

the meaning of each parameter

please see main.py

searching optimal value of parameter (todo... Chengbin)

ABRW SAGE-GCN

Testing

Parameter Setting

Currently, we use the default parameter

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

citeseer:

method	AUC	Micro-F1	Macro-F1	Time
aane	0.8889	0.7067	0.6295	36.99
abrw	0.9342	0.7287	0.6705	64.32
asne	0.8293	0.5275	0.4736	70.89
attrcomb	0.8756	0.7077	0.6592	99.19
attrpure	0.8684	0.6922	0.6525	0.99
deepwalk	0.7203	0.5681	0.5205	93.14
grarep	0.8501	0.5200	0.4656	16.48
line	0.6340	0.3959	0.3503	242.59
node2vec	0.6588	0.5931	0.5493	27.60
sagegcn	0.8953	0.6016	0.5247	444.50
sagemean	0.8772	0.6391	0.5606	371.74
tadw	0.8984	0.7337	0.6866	14.39

cora:

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

mit:

method	AUC	Micro-F1	Macro-F1	Time
aane	0.6586	0.3742	0.0730	83.49
abrw	0.9068	0.7981	0.2286	113.41
asne	0.6596	0.3041	0.0681	901.18
attrcomb	0.8548	0.8016	0.2223	125.82
attrpure	0.6464	0.3497	0.0707	1.19
deepwalk	0.9190	0.7997	0.2295	173.70
grarep	0.8983	0.7695	0.1901	51.55
line	0.7836	0.7415	0.1857	6335.43
node2vec	0.9088	0.8085	0.2356	465.54
sagegcn	0.8005	0.6057	0.1451	12462.35
sagemean	0.7525	0.5796	0.1279	10534.44

Datasets (todo...Chengbin)

We provide Cora for ... and other datasets e.g. Facebook_MIT, refer to 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)

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.

Recommended References (todo... Chengbin)