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XFlow is a library built upon Python to easily write and train method for a wide range of applications related to graph flow problems. XFlow is organized task-wise, which provide datasets benchmarks, baselines and auxiliary implementation.

pip install xflow-net

import xflow

from xflow.dataset.nx import BA, connSW
from xflow.dataset.pyg import Cora
from xflow.diffusion import SI, IC, LT
from xflow.util import run

# graphs to test
fn = lambda: connSW(n=1000, beta=0.1)
fn.__name__ = 'connSW'
gs = [Cora, fn, BA]

# Diffusion models to test
df = [SI, IC, LT]

# Configurations of IM experiments
from xflow.method.im import pi as im_pi, degree as im_degree, sigma as im_sigma, celfpp as im_celfpp, greedy as im_greedy
me = [im_pi]
rt = run (
    graph = gs, diffusion = df,
    method = me, eval = 'im', epoch = 10, 
    budget = 10, 
    output = [ 'animation', 'csv', 'fig'])

See more examples in folder examples

• simulation: greedy, CELF, and CELF++,
• proxy: pi, sigma, degree, and eigen-centrality
• sketch: RIS

• greedy
• pi
• sigma
• eigen-centrality
• degree

• NETSLEUTH (Legacy and Fast versions)
• Jordan Centrality
• LISN

• Graphs: Compatiable with graph objects/class by Networkx and Pytorch Geometric
• Diffusion Models: Support NDLib

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.