semvec/pypi-packages huggingface dataset:
psi-score 1.0.0
Metric of user influence in Online Social Networks
License: MIT
Requires Python: >=3.9,<3.11
Keywords: graph algorithms,network science,markov chain,social networks,pagerank

# Psi-score

$\psi$-score: Metric of user influence in Online Social Networks

## Requirements
* Python >=3.9,<3.11

## Installation

```bash
$ pip install psi-score
```

## Usage

```python
>>> 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:
```python
>>> 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):
```python
>>> 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
super-map 1.3.3
Like dict, but easier
License: MIT
Requires Python: >=3.6

# What is this?

A python package that provides an alternative to `dict` that is much easier for lazy people to use.

# How do I use this?

`pip install super_map`


```python
from super_map import Map, LazyDict

item = Map()

# no error
item["value"]["subvalue"] = True

# prints true
print(item.value.subvalue == item["value"]["subvalue"]) # prints true

# dont need .items()
for each_key, each_value in item:
    print(each_key)

for each_key in item[Map.Keys]:
    print(each_key)

for each_key in item[Map.Values]:
    print(each_key)

value = item.a.b.c.d.e
if not value:
    # this prints out
    print("item.a.b.c.d.e doesn't exist")

item_as_dict = item[Map.Dict]

item.f.g.h += 1
print('will print out 1:', item.f.g.h)
```
Funciones-Generales 0.0.1
Librería que guarda algunas funciones esenciales que son faciles,pero pesadas.
License: MIT

Librería que recoge numerosas funciones de uso cotidiano para su implentación en programación.
Son funciones sencillas que no necesitan una gran habilidad para su lectura, sin embargo se recogen aquí.

Esta versión tiene poca información y la funciones son un poco probres, la siguiente versión incluira comentarios para comprenderlo mejor y mayor número de funciones conforme vaya aprendiendo.