Finder

from influ import finder

SeedFinder

This is basic object for finding key nodes in your network. Works for both directed and undirected graphs. Graphs needs to have special structure and because of that only graphs loaded with reader module are recommended.

Initialization parameters

  • graph [required] – graph that will be analysed
  • number [optional, default: 5] - value of number or percentage of seeds to choose
  • unit [optional, default: ‘number’] - either percent or number;
  • random_seed [optional, default: None] – value used as seed for random function to ensure repetitive results;

SeedFinder . configure

Parameters:

  • number [optional, default: None] – value of number or percentage of seeds to choose; have to be configured together with unit parameter
  • unit [optional, default: None] – either percent or number; have to be configured together wit number parameter
  • random_seed [optional, default: None] – value used as seed for random function to ensure repetitive results. It’s used at the beginning of every model evaluation. If random_seed is equal to None (default) then no random seed will be used

SeedFinder . by_indegree

Return list of n first vertices indices sorted by their indegree. Takes no parameters.

SeedFinder . by_outdegree

Return list of n first vertices indices sorted by their outdegree. Takes no parameters.

SeedFinder . by_degree

Return list of n first vertices indices sorted by their degree. Takes no parameters.

SeedFinder . by_betweenness

Return list of n first vertices indices sorted by their betweenness. Takes no parameters.

SeedFinder . by_clustering_coefficient

Return list of n first vertices indices sorted by their clustering coefficient (transitivity). IMPORTANT: in directed graph only mutual edges will be considered Takes no parameters.

SeedFinder . greedy

Search for vertices indices that are the best seeds using greedy approach.

Parameters:

  • model [optional, default: Model.LinearThreshold] - model of social influence. Currently only Linear Treshold (LT) and Independent Cascade (IC) are available
  • threshold [optional, default: None] - defines value of threshold in influence model. In Linear Threshold model it defines threshold of sum of influence that have to applied to node to activate it. In Independent Cascade model it’s probability that activated node activates another node.
  • depth [optional, default: None] - how many iterations will be in spreading simulations :return: list of ids of nodes considered as the best seeds

SeedFinder . brute_force

Search for vertices indices that are the best seeds using brute force approach.

Parameters:

  • model [optional, default: Model.LinearThreshold] - model of social influence. Currently only Linear Treshold (LT) and Independent Cascade (IC) are available
  • threshold [optional, default: None] - defines value of threshold in influence model. In Linear Threshold model it defines threshold of sum of influence that have to applied to node to activate it. In Independent Cascade model it’s probability that activated node activates another node.
  • depth [optional, default: None] - how many iterations will be in spreading simulations :return: list of ids of nodes considered as the best seeds

SeedFinder . CELFpp

Search for vertices indices that are the best seeds using CELF++ approach.

Parameters:

  • model [optional, default: Model.LinearThreshold] - model of social influence. Currently only Linear Treshold (LT) and Independent Cascade (IC) are available
  • threshold [optional, default: None] - defines value of threshold in influence model. In Linear Threshold model it defines threshold of sum of influence that have to applied to node to activate it. In Independent Cascade model it’s probability that activated node activates another node.
  • depth [optional, default: None] - how many iterations will be in spreading simulations :return: list of ids of nodes considered as the best seeds

SeedFinder . plot_influence

Run influence simulation for given set of seed and plot result graph.

Parameters:

  • seeds [required] - list of seed ids for influence spreading simulation
  • model [optional, default: Model.LinearThreshold] - model of social influence. Currently only Linear Treshold (LT) and Independent Cascade (IC) are available
  • threshold [optional, default: None] - defines value of threshold in influence model. In Linear Threshold model it defines threshold of sum of influence that have to applied to node to activate it. In Independent Cascade model it’s probability that activated node activates another node.
  • depth [optional, default: None] - how many iterations will be in spreading simulations :return: list of ids of nodes considered as the best seeds

Model

Social influence model enum

Model . LinearThreshold

Enum value. Represents Linear Threshold Model

Model . IndependentCascade

Enum value. Represents Independent Cascade Model