目錄

１、環(huán)境配置

２、LT傳播模型算法實(shí)現(xiàn)

３、LT傳播模型算法測(cè)試

４、測(cè)試文件Wiki-Vote.txt數(shù)據(jù)

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社交網(wǎng)絡(luò)影響力最大化——線性閾值模型（LT模型）算法實(shí)現(xiàn)（Python實(shí)現(xiàn)）

１、環(huán)境配置

環(huán)境配置：Win7 Pycharm Anaconda2

該算法每個(gè)節(jié)點(diǎn)的閾值設(shè)為 0.5

２、LT傳播模型算法實(shí)現(xiàn)

linear_threshold.py (LT傳播模型算法)

# -*- coding: utf-8 -*- """ Implement linear threshold models 社交網(wǎng)絡(luò)影響力最大化 傳播模型——線性閾值（LT）模型算法實(shí)現(xiàn) """ import copy import itertools import random import math import networkx as nx__all__ = ['linear_threshold']#------------------------------------------------------------------------- # Some Famous Diffusion Models #-------------------------------------------------------------------------def linear_threshold(G, seeds, steps=0): #LT線性閾值算法"""Parameters----------G : networkx graph #所有節(jié)點(diǎn)構(gòu)成的圖The number of nodes.seeds: list of nodes #子節(jié)點(diǎn)集The seed nodes of the graphsteps: int #激活節(jié)點(diǎn)的層數(shù)（深度），當(dāng)steps<=0時(shí)，返回子節(jié)點(diǎn)集能激活的所有節(jié)點(diǎn)The number of steps to diffuseWhen steps <= 0, the model diffuses until no more nodescan be activatedReturn------layer_i_nodes : list of list of activated nodeslayer_i_nodes[0]: the seeds #子節(jié)點(diǎn)集layer_i_nodes[k]: the nodes activated at the kth diffusion step #該子節(jié)點(diǎn)集激活的節(jié)點(diǎn)集Notes-----1. Each node is supposed to have an attribute "threshold". If not, thedefault value is given (0.5). #每個(gè)節(jié)點(diǎn)有一個(gè)閾值，這里默認(rèn)閾值為：0.52. Each edge is supposed to have an attribute "influence". If not, thedefault value is given (1/in_degree) #每個(gè)邊有一個(gè)權(quán)重值，這里默認(rèn)為：1/入度References----------[1] GranovetterMark. Threshold models of collective behavior.The American journal of sociology, 1978."""if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:raise Exception( \"linear_threshold() is not defined for graphs with multiedges.")# make sure the seeds are in the graphfor s in seeds:if s not in G.nodes():raise Exception("seed", s, "is not in graph")# change to directed graphif not G.is_directed():DG = G.to_directed()else:DG = copy.deepcopy(G) # copy.deepcopy 深拷貝 拷貝對(duì)象及其子對(duì)象# init thresholdsfor n in DG.nodes():if 'threshold' not in DG.node[n]:DG.node[n]['threshold'] = 0.5elif DG.node[n]['threshold'] > 1:raise Exception("node threshold:", DG.node[n]['threshold'], \"cannot be larger than 1")# init influencesin_deg = DG.in_degree() #獲取所有節(jié)點(diǎn)的入度f(wàn)or e in DG.edges():if 'influence' not in DG[e[0]][e[1]]:DG[e[0]][e[1]]['influence'] = 1.0 / in_deg[e[1]] #計(jì)算邊的權(quán)重elif DG[e[0]][e[1]]['influence'] > 1:raise Exception("edge influence:", DG[e[0]][e[1]]['influence'], \"cannot be larger than 1")# perform diffusionA = copy.deepcopy(seeds)if steps <= 0:# perform diffusion until no more nodes can be activatedreturn _diffuse_all(DG, A)# perform diffusion for at most "steps" rounds onlyreturn _diffuse_k_rounds(DG, A, steps)def _diffuse_all(G, A):layer_i_nodes = [ ]layer_i_nodes.append([i for i in A])while True:len_old = len(A)A, activated_nodes_of_this_round = _diffuse_one_round(G, A)layer_i_nodes.append(activated_nodes_of_this_round)if len(A) == len_old:breakreturn layer_i_nodesdef _diffuse_k_rounds(G, A, steps):layer_i_nodes = [ ]layer_i_nodes.append([i for i in A])while steps > 0 and len(A) < len(G):len_old = len(A)A, activated_nodes_of_this_round = _diffuse_one_round(G, A)layer_i_nodes.append(activated_nodes_of_this_round)if len(A) == len_old:breaksteps -= 1return layer_i_nodesdef _diffuse_one_round(G, A):activated_nodes_of_this_round = set()for s in A:nbs = G.successors(s)for nb in nbs:if nb in A:continueactive_nb = list(set(G.predecessors(nb)).intersection(set(A)))if _influence_sum(G, active_nb, nb) >= G.node[nb]['threshold']:activated_nodes_of_this_round.add(nb)A.extend(list(activated_nodes_of_this_round))return A, list(activated_nodes_of_this_round)def _influence_sum(G, froms, to):influence_sum = 0.0for f in froms:influence_sum += G[f][to]['influence']return influence_sum

３、LT傳播模型算法測(cè)試

test_linear_threshold.py（LT模型算法測(cè)試）

#!/usr/bin/env python # coding=UTF-8 #支持中文字符需要添加 coding=UTF-8 from nose.tools import * from networkx import * from linear_threshold import * import time """Test Diffusion Models ---------------------------- """ if __name__=='__main__':start=time.clock()datasets=[]f=open("Wiki-Vote.txt","r") #讀取文件數(shù)據(jù)（邊的數(shù)據(jù)）data=f.read()rows=data.split('\n')for row in rows:split_row=row.split('\t')name=(int(split_row[0]),int(split_row[1]))datasets.append(name) #將邊的數(shù)據(jù)以元組的形式存放到列表中G=networkx.DiGraph() #建立一個(gè)空的有向圖GG.add_edges_from(datasets) #向有向圖G中添加邊的數(shù)據(jù)列表layers=linear_threshold(G,[6],2) #調(diào)用LT線性閾值算法，返回子節(jié)點(diǎn)集和該子節(jié)點(diǎn)集的最大激活節(jié)點(diǎn)集del layers[-1]length=0for i in range(len(layers)):length =length+len(layers[i])lengths=length-len(layers[0]) #獲得子節(jié)點(diǎn)的激活節(jié)點(diǎn)的個(gè)數(shù)（長(zhǎng)度）end=time.clock()#測(cè)試數(shù)據(jù)輸出結(jié)果print(layers) #[[25], [33, 3, 6, 8, 55, 80, 50, 19, 54, 23, 75, 28, 29, 30, 35]]print(lengths) #15print('Running time: %s Seconds'%(end-start)) #輸出代碼運(yùn)行時(shí)間

社交網(wǎng)絡(luò)影響力最大化（Python實(shí)現(xiàn)）及Wiki-Vote數(shù)據(jù)集資源下載：

社交網(wǎng)絡(luò)影響力最大化（Python實(shí)現(xiàn)）及Wiki-Vote數(shù)據(jù)集-機(jī)器學(xué)習(xí)文檔類資源-CSDN下載

本人博文社交網(wǎng)絡(luò)影響力最大化項(xiàng)目實(shí)戰(zhàn)基礎(chǔ)學(xué)習(xí)

1、社交網(wǎng)絡(luò)影響力最大化（獨(dú)立級(jí)聯(lián)（IC）模型和線性閾值（LT）模型）介紹

2、社交網(wǎng)絡(luò)影響力最大化—線性閾值模型（LT模型）算法實(shí)現(xiàn)（Python實(shí)現(xiàn))

3、社交網(wǎng)絡(luò)影響力最大化—貪心算法實(shí)現(xiàn)（Python實(shí)現(xiàn)）

4、社交網(wǎng)絡(luò)影響力最大化項(xiàng)目實(shí)戰(zhàn)源代碼和Wiki-Vote數(shù)據(jù)集下載

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