Metric of user influence in Online Social Networks
Project description
Psi-score
$\psi$-score: Metric of user influence in Online Social Networks
Requirements
- Python >=3.9,<3.11
Installation
$ pip install psi-score
Usage
>>> from psi_score import PsiScore
>>> adjacency = {0: [1, 3], 1: [0, 2], 2: [0, 1, 3], 3: [0]}
>>> lambdas = [0.23, 0.50, 0.86, 0.19]
>>> mus = [0.42, 0.17, 0.10, 0.37]
>>> psiscore = PsiScore()
>>> scores = psiscore.fit_transform(adjacency, lambdas, mus)
>>> scores
array([0.21158803, 0.35253745, 0.28798439, 0.14789014])
>>> np.round(scores, 2)
array([0.21, 0.35, 0.29, 0.15])
You can use another algorithm and change some parameters:
>>> psiscore = PsiScore(solver='power_nf', n_iter=500, tol=1e-3)
>>> scores = psiscore.fit_transform(adjacency, lambdas, mus, ps=[1], qs=[0, 3])
The ps
and qs
parameters allows to have some chosen p_i
and q_i
vectors (only with the push
and power_nf
methods):
>>> psiscore.P
{1: array([0.5333334 , 0.1681094 , 0.46801851, 0.34442264])}
>>> psiscore.Q
{0: array([0.46164044, 0.0514935 , 0.02798624, 0.30484491]),
3: array([0.13087053, 0.01616898, 0.01850541, 0.42554885])}
License
psi-score
was created by Nouamane Arhachoui. It is licensed under the terms of the MIT license.
References
-
Giovanidis, A., Baynat, B., Magnien, C., & Vendeville, A. (2021). Ranking Online Social Users by Their Influence. IEEE/ACM Transactions on Networking, 29(5), 2198-2214. https://doi.org/10.1109/tnet.2021.3085201
-
Arhachoui, N., Bautista, E., Danisch, M., & Giovanidis, A. (2022). A Fast Algorithm for Ranking Users by their Influence in Online Social Platforms. 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 526-533. https://doi.org/10.1109/ASONAM55673.2022.10068673
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