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https://github.com/benedekrozemberczki/feather
The reference implementation of FEATHER from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models".
https://github.com/benedekrozemberczki/feather
data-mining deep-learning deep-neural-networks deepwalk graph graph-classification graph-convolution graph-embedding graph-kernel graph2vec machine-learning network-embedding networkx neural-network node-classification node-embedding node2vec pytorch representation-learning tensorflow
Last synced: 3 days ago
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The reference implementation of FEATHER from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models".
- Host: GitHub
- URL: https://github.com/benedekrozemberczki/feather
- Owner: benedekrozemberczki
- License: mit
- Created: 2020-02-17T17:17:04.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-03-18T12:13:01.000Z (over 1 year ago)
- Last Synced: 2024-11-08T18:46:15.718Z (9 days ago)
- Topics: data-mining, deep-learning, deep-neural-networks, deepwalk, graph, graph-classification, graph-convolution, graph-embedding, graph-kernel, graph2vec, machine-learning, network-embedding, networkx, neural-network, node-classification, node-embedding, node2vec, pytorch, representation-learning, tensorflow
- Language: Python
- Homepage:
- Size: 14.3 MB
- Stars: 45
- Watchers: 3
- Forks: 13
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
FEATHER
==========================
[](https://arxiv.org/abs/2005.07959) [](https://codebeat.co/projects/github-com-benedekrozemberczki-feather-master) [](https://github.com/benedekrozemberczki/FEATHER/archive/master.zip) [](https://twitter.com/intent/follow?screen_name=benrozemberczki)
The Python reference implementation of **FEATHER** and **FEATHER-G** from the **CIKM '20** paper **Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models.**
---------------------
### Abstract
In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales. We introduce FEATHER, a computationally efficient algorithm to calculate a specific variant of these characteristic functions where the probability weights of the characteristic function are defined as the transition probabilities of random walks. We argue that features extracted by this procedure are useful for node level machine learning tasks. We discuss the pooling of these node representations, resulting in compact descriptors of graphs that can serve as features for graph classification algorithms. We analytically prove that FEATHER describes isomorphic graphs with the same representation and exhibits robustness to data corruption. Using the node feature characteristic functions we define parametric models where evaluation points of the functions are learned parameters of supervised classifiers. Experiments on real world large datasets show that our proposed algorithm creates high quality representations, performs transfer learning efficiently, exhibits robustness to hyperparameter changes, and scales linearly with the input size.
This repository provides the reference implementation for FEATHER as described in the paper:
> Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models.
> [Benedek Rozemberczki](http://homepages.inf.ed.ac.uk/s1668259/) and [Rik Sarkar](https://homepages.inf.ed.ac.uk/rsarkar/).
> CIKM, 2020.
The datasets are also available on [SNAP](http://snap.stanford.edu/).
The model is now also available in the package [Karate Club](https://github.com/benedekrozemberczki/karateclub).
### Table of Contents
1. [Citing](#citing)
2. [Requirements](#requirements)
3. [Options](#options)
4. [Examples](#examples)
### Citing
If you find FEATHER useful in your research, please consider citing the following paper:
```bibtex
@inproceedings{feather,
title={{Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models}},
author={Benedek Rozemberczki and Rik Sarkar},
year={2020},
pages = {1325–1334},
booktitle={Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20)},
organization={ACM},
}
```
### Requirements
The codebase is implemented in Python 3.5.2. package versions used for development are just below.
```
networkx 2.4
tqdm 4.28.1
numpy 1.15.4
pandas 0.23.4
texttable 1.5.0
scipy 1.1.0
argparse 1.1.0
```
### Input
#### Node level
The code takes an input graph in a csv file. Every row indicates an edge between two nodes separated by a comma. The first row is a header. Nodes should be indexed starting with 0.
The feature matrix is **dense** it is assumed that it is stored as csv with comma separators. It has a header, and rows are separated by node identifiers (increasing). It should look like this:
| **Feature 1** | **Feature 2** | **Feature 3** | **Feature 4** |
| --- | --- | --- |--- |
| 3 |0 |1.37 |1 |
| 1 |1 |2.54 |-11 |
| 2 |0 |1.08 |-12 |
| 1 |1 |1.22 |-4 |
| ... |... |... |... |
| 5 |0 |2.47 |21 |
#### Graph level
The graphs are stored in a JSON file where keys are graph identifiers and values are edge lists. Graph identifiers are consecutive and start with 0. Each individual graph has nodes which are indexed starting with 0. We assume that graphs are connected.
```javascript
{ 0: [[0, 1], [1, 2], [2, 3]],
1: [[0, 1], [1, 2], [2, 0]],
...
n: [[0, 1], [1, 2]]}
```
### Options
Learning the embedding is handled by the `src/main.py` script which provides the following command line arguments.
#### Input and output options
```
--graph-input STR Input edge list csv. Default is `input/edges/ER_edges.csv`.
--feature-input STR Input features csv. Default is `input/features/ER_features.csv`.
--graphs STR Input graphs json. Default is `input/graphs/ER_graphs.json`.
--output STR Embedding output path. Default is `output/ER_node_embedding.csv`.
```
#### Model options
```
--model-type STR FEATHER or FEATHER-G model. Default is `FEATHER`.
--eval-points INT Number of evaluation points. Default is 25.
--order INT Matrix powers approximated. Default is 5.
--theta-max FLOAT Length of random walk per source. Default is 2.5.
```
### Examples
Training a FEATHER model.
```sh
$ python src/main.py
```
Changing the scale parameter to increase adjacency matrix powers.
```sh
$ python src/main.py --order 3
```
Decreasing the number of evaluation points.
```sh
$ python src/main.py --eval-points 25
```
Training a graph level FEATHER model with the default dataset.
```sh
$ python src/main.py --model-type FEATHER-G --output output/ER_graph_embedding.csv
```
--------------------------------------------------------------------------------
**License**
- [MIT](https://github.com/benedekrozemberczki/FEATHER/blob/master/LICENSE)
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