https://github.com/borgwardtlab/significant-subgraph-mining
Finding statistically significant subgraphs while correcting for multiple testing
https://github.com/borgwardtlab/significant-subgraph-mining
Last synced: 7 months ago
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Finding statistically significant subgraphs while correcting for multiple testing
- Host: GitHub
- URL: https://github.com/borgwardtlab/significant-subgraph-mining
- Owner: BorgwardtLab
- License: gpl-2.0
- Created: 2015-01-29T15:41:01.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2015-10-16T06:54:00.000Z (over 10 years ago)
- Last Synced: 2023-06-09T12:05:13.537Z (almost 3 years ago)
- Language: C++
- Homepage:
- Size: 299 KB
- Stars: 6
- Watchers: 2
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
#Significant Subgraph Mining with Multiple Testing Correction
The first algorithm that efficiently finds all significant subgraphs from graph databases while correcting for multiple testing.
##Summary
Given a graph database (a collection of graphs), class labels of graphs, and significance threshold α, this algorithm performs:
* efficient computation of the *corrected significance threshold* for each test that strictly controls the FWER under α
* enumeration of *all subgraphs* that are statistically significantly associated with the class membership
Two methods are implemented for computing the corrected significance threshold:
* *Tarone's testability* correction (without `-w` option, default):
* Computation is fast but FWER control is not optimal (the actual FWER is smaller than α)
* *Westfall-Young permutation* correction (with `-w` option)
* Slower than the above but optimal FWER control is achieved (the actual FWER is almost α)
* This method is called **Westfall-Young light** and its itemset mining version is [available](https://www.bsse.ethz.ch/mlcb/research/machine-learning/wylight.html)
Please see the following papers for the detailed information about this algorithm and refer them in your published research:
* For Tarone's testability correction:
* Sugiyama, M., Llinares-López, F., Kasenburg, N., Borgwardt, K. M.: **Significant Subgraph Mining with Multiple Testing Correction,** *Proceedings of the 2015 SIAM International Conference on Data Mining* (SDM2015), 199-207, 2015.
[[PDF]](http://epubs.siam.org/doi/pdf/10.1137/1.9781611974010.5)
* For Westfall-Young permutation correction:
* Llinares-López, F., Sugiyama, M., Papaxanthos, L., Borgwardt, K. M.:
**Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing,** *Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining* (KDD2015), 725-734, 2015.
[[PDF]](http://dl.acm.org/ft_gateway.cfm?id=2783363)
[GASTON](http://www.liacs.nl/~snijssen/gaston/iccs.html) is used as a frequent subgraph mining algorithm:
* Nijssen, S. and Kok, J.: **A Quickstart in Frequent Structure Mining Can
Make a Difference,** *Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining* (KDD2004), 647-652, 2004.
##Usage
###Data format
The input is composed of two files: a graph database (a list of graphs) and a list of class labels.
In a graph database, each graph is described as follows:
```
#
t #
v
e
```
This is the same as the standard format in frequent subgraph mining softwares, such as [gSpan](https://www.cs.ucsb.edu/~xyan/software/gSpan.htm) and [GASTON](http://www.liacs.nl/~snijssen/gaston/iccs.html).
For example:
```
# start
t # 1 // transaction id is 1
v 0 1 // node 0 with label 1
v 1 2 // node 1 with label 2
e 0 1 0 // edge from node 0 to 1 with label 0
```
In the class file, each line contains the class label of the corresponding graph.
The class label should be either "0" or "1".
###Compilation
To compile the program, go to the "src" directory and type
```
make
```
The "Boost" library is needed to compile it.
###Run
To run the algorithm, type
```
./sgmine -a -i -c -o
```
`` is the target FWER. It is set to be 0.05 if skipped.
###Output format
Resulting significant subgraphs are written to the file ``. The output file has the same format as the input file except for two lines: supports (start from "\# s") in minor and major classes and the *p*-value (start from "\# p") of the graph. For example:
```
# s 22 7 // support of this subgraph in minor (left) and major (right) classes
# p 1.5841e-07 // p-value of this subgraph
t 1
v 0 0
v 1 9
v 2 9
e 0 1 0
e 1 2 0
```
###Argument list
`-w` : Perform Westfall-Young permutation correction if specified
`-m ` : Maximum size of each subgraph (default: unlimited)
`-a ` : Target FWER (default: 0.05)
`-j ` : Number of permutations in `-w` mode (default: 1000)
`-r ` : Seed for permutations in `-w` mode (default: 0)
`-i ` : Input file of graph database
`-c ` : Input file of class labels
`-o ` : Output file of significant subgraphs
Example
-------
There is an example graph database "Chemical_340" and the corresponding class file "Chemical_340_class". To run the algorithm on this database, type
```
./sgmine -i Chemical_340 -c Chemical_340_class -o output
```
You can redirect the resulting statistics like
```
./sgmine -i Chemical_340 -c Chemical_340_class -o output > stat
```
If you use Westfall-Young permutation, type
```
./sgmine -w -i Chemical_340 -c Chemical_340_class -o output
```
Contact
-------
* Author: Mahito Sugiyama
* Affiliation: ISIR, Osaka University, Japan
* Mail: mahito@ar.sanken.osaka-u.ac.jp