https://github.com/iamcorey/imp

AI Project 03
https://github.com/iamcorey/imp

Last synced: 7 days ago
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AI Project 03

• Host: GitHub
• URL: https://github.com/iamcorey/imp
• Owner: iAmCorey
• License: mit
• Created: 2018-12-01T11:46:24.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2018-12-11T02:24:41.000Z (almost 6 years ago)
• Last Synced: 2023-11-07T17:58:53.940Z (about 1 year ago)
• Language: Python
• Size: 161 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# IMP

# Overview

Influence Maximization Problem (IMP) is the problem of finding a small subset of nodes (referred to as seed set) in a social network that could maximize the spread of influence.

The influence spread is the expected number of nodes that are influenced by the nodes in the seed set in a cascade manner.

# File Description

executable estimator - ISE.py

executable solver - IMP.py

# Usage

## Task1: influence spread computation

`python ISE.py –i  -s  -m  -t `

 is the absolute path of the social network file

 is the absolute path of the seed set file

 can only be IC or LT

 is a positive number which indicates how many seconds(in Wall clock time, range: [60s, 1200s]) your algorithm can spend on this instance. 

e.g. `python ISE.py -i network.txt -s seeds.txt -m LT -t 120`

Output:

-    The value of the estimated influence spread

## Taks2: influence maximization

`python IMP.py –i  -k  -m  -t `

 is the absolute path of the social network file

 is a positive integer

 can only be IC or LT

 is a positive number which indicates how many seconds (in Wall clock time, range: [60s, 1200s]) your algorithm can spend on this instance. 

e.g. `python IMP.py -i network.txt -k 10 -m LT -t 120`

Output: 

-   The seed set found by your algorithm.

-   The format of the seed set output should be as follows: each line contains a node index. An example is also included in the package.

# Input:

-   A graph *G=(V,E)*

-   A predefined seed set cardinality *k*

-   A predefined stochastic diffsion model - *IC/LT*

# Output: 

A *size-k* seed set S' with the maximal 𝜎(𝑆) for any *size-k* seed set S ⊆ 𝑉  

# Stochastic Diffusion Models

Diffusion process: At round 0, S中的所有node变成active，其余是inactive，每一轮，每个actived node都会active它的neighbors，直到所有nodes都activated，process end.s

## Independent Cascade(IC)

一个node u active它的neighbor v的几率与weight w(u,v)成比例。

w(u,v) = v 的 in-degree 的倒数

## Linear Threshold(LT)

一开始，每一个node都有一个random threshold 𝜃（在[0,1]之间）。在round t(t>0)，一个inactive node v，如果它的所有activated neighbors u 与v的w(u,v)加起来>= 𝜃，那v就会activated。

w(u,v) = v 的 in-degree 的倒数