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# OpenANE: The first Open source framework specialized in Attributed Network Embedding (ANE) # 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](https://github.com/thunlp/OpenNE) that integrates several PNE methods under one unified framework. 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 [OpenNE](https://github.com/thunlp/OpenNE) that mainly integrates PNE methods under one unified framework.
<br> 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. <br> 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 Authors: Chengbin HOU chengbin.hou10@foxmail.com & Zeyu DONG 2018
@ -7,7 +7,7 @@ Authors: Chengbin HOU chengbin.hou10@foxmail.com & Zeyu DONG 2018
## Motivation ## 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. 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.
<br> 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. <br> 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 as node attributes and then calling ANE methods. Therefore, to some extent, ANE methods can be regarded as a generalization of PNE methods.
## Methods ## Methods
[ABRW](https://github.com/houchengbin/ABRW), [ABRW](https://github.com/houchengbin/ABRW),
@ -22,7 +22,7 @@ In many real-world scenarios, a network often comes with node attributes such as
[GraRep](https://github.com/thunlp/OpenNE), [GraRep](https://github.com/thunlp/OpenNE),
AttrPure, AttrPure,
AttrComb AttrComb
<br> Note: all NE methods in this framework are unsupervised, and so do NOT require any labels during embedding phase. <br> Note: all NE methods in this framework are unsupervised, and so does not require any label during embedding phase.
**For more details of each method, please have a look at our paper https://arxiv.org/abs/1811.11728** **For more details of each method, please have a look at our paper https://arxiv.org/abs/1811.11728**
<br> And if you find ABRW or this framework is useful for your research, please consider citing it. <br> And if you find ABRW or this framework is useful for your research, please consider citing it.
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| tadw | 0.9005 | 0.8383 | 0.8255 | 10.73 | | tadw | 0.9005 | 0.8383 | 0.8255 | 10.73 |
#### 2D Visualization task #### 2D Visualization task
![Cora vis](https://github.com/houchengbin/OpenANE/blob/master/log/vis.jpg) <br> ![Cora vis](https://github.com/houchengbin/OpenANE/blob/master/log/vis.jpg) <br>
<br> Steps: Cora -> NE method -> node embeddings -> PCA -> 2D vis <br> Steps: Cora -> NE method -> node embeddings -> PCA -> 2D vis
<br> The different colors indicate different ground truth labels. <br> The different colors indicate different ground truth labels.
## Other Datasets ## Other Datasets
More well-prepared (attributed) network datasets are available at [NetEmb-Datasets](https://github.com/houchengbin/NetEmb-datasets) More well-prepared (attributed) network datasets are available at [NetEmb-Datasets](https://github.com/houchengbin/NetEmb-Datasets)
### Your Own Dataset ### Your Own Dataset
*------ FILE for structural information (each row) ------* *--------------- Structural Info (each row) --------------------*
<br> adjlist: node_id1 node_id2 node_id3 adjlist: node_id1 node_id2 node_id3 ... (neighbors of node_id1)
<br> or edgelist: node_id1 node_id2 weight(optional) or edgelist: node_id1 node_id2 weight (weight is optional)
<br> *------ FILE for attribute information (each row) ------* *--------------- Attribute Info (each row) ---------------------*
<br> node_id1 attr1 attr2 ... attrM node_id1 attr1 attr2 ...
<br> *------ FILE for label information (each row) ------* *--------------- Label Info (each row) -------------------------*
<br> node_id1 label(s) node_id1 label1 label2 ...
### Parameter Tuning ### Parameter Tuning
For different dataset, one may need to search the optimal parameters instead of taking the default parameters. 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. For the meaning and suggestion of each parameter, please see main.py.
## Want to contribute ## Contribution
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. We highly welcome and appreciate your contribution 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 ## References