a python library for graph flow
Project description
XFlow Homepage | Documentation | Paper Collection | Colab Notebooks Tutorials
XFlow is a library built upon Python to easily write and train method for a wide range of applications related to network flow problems. XFlow is organized task-wise, which provide datasets benchmarks, baselines and auxiliary implementation.
Installation
pip install xflow-net
Example
from xflow.dataset import cora, random, ba
from xflow.diffusion import ic, si
from xflow.seed import random, degree, eigen
from xflow.method.im import celf, sigma
# graphs to test
gs = [cora, random, ba]
# diffusion models to test
df = [ic, si]
# seed configurations to test
se = [random, degree, eigen]
# methods to test
me = [celf, sigma, imrank]
# configurations of experiments
rt = run(graph=gs, diffusion=df, seed=se, method=me, eval='im', epoch=10, output=['animation', 'csv', 'fig'])
[Result]
Create your own models
Benchmark Task
Influence Maximization
- simulation: greedy, CELF, and CELF++,
- proxy: pi, sigma, degree, and eigen-centrality
- sketch: RIS, SKIM, IMM
Blocking Maximization
- greedy
- pi
- sigma
- eigen-centrality
- degree
Source Localization
- NETSLEUTH (Legacy and Fast versions)
- Jordan Centrality
- LISN.
Experimental Configurations
- Graphs: Compatiable with graph objects/class by Networkx and Pytorch Geometric
- Diffusion Models: Support NDLib
Contact
Feel free to email us if you wish your work to be listed in this repo. If you notice anything unexpected, please open an issue and let us know. If you have any questions or are missing a specific feature, feel free to discuss them with us. We are motivated to constantly make XFlow even better.
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