normol的博客

官方提供的函数：function h = WattsStrogatz(N,K,beta)% H = WattsStrogatz(N,K,beta) returns a Watts-Strogatz model graph with N% nodes, N*K edges, mean node degree 2*K, and rewiring probability beta.%%

Coursera上的视频做笔记学习前言： 其实，semantic network与text mining是紧密相关的。 Semantic network 1、so this means the data mining problem is basically taking a lot of data as input and giving actionale knowle

主要解决的问题： explore how we can represent a domain specific knowledge using the different concepts.这篇文章： we will see how we can represent the concepts and how we can represent the knowledge using thes

左边的图共有两个cycles，上面的period=5，下面的period=6，因此最大公因数=1，是aperiodic graph 而右边的图，三个cycle的period=3，另一个period=6，因此最大公因数是3，就不是aperiodic graphRanking on directed graph troubles: 1:Absorbing nodes 2:Sourc

Which vertices are important? （是Youtube上的课程，这儿做笔记学习）Graph-theoretic measures: （每个node旁的数字，即这个node的eccentricity，因此diameter就是max（eccentricity），radius就是min（eccentricity），central point是拥有最小的

图同构（isomorphic）的定义：（不理解直接跳过即可） if and only if there exists a bijection alpha from vertex set of G to the vertex set of H,such that alpha of u,alpha of v is an edge of H if and only if uv is an edge

这是非官方的容易理解的同样的网络模型模拟下面的官方的模拟（但自己没有去看，只是粘过来而已）：function A = BAgraph_dir(N,mo,m)% Generates a scale-free directed adjacency matrix using Barabasi and Albert algorithm% Input: N - number of nodes

原博客地址 这儿是学习并做笔记N = 1000; m0 = 3;m = 3; %初始结点3，度3 节点数Nadjacent_matrix = sparse(m0,m0);%初始化邻接矩阵for i = 1:m0 for j = 1:m0 if j~=i%去掉自身形成的环 adjacent_matrix(i,j) = 1;%建立初始临界矩

需要一个单独的m文件：%即写上三个微分方程function y=SIRModel(t,x,lambda,mu)y=[-lambda*x(1)*x(2),lambda*x(1)*x(2)-mu*x(2),mu*x(2)]';再进行作图>> ts=0:1:100;>> lambda=0.00001;>> mu=1/14;>> x0=[45400,2100,2500];>>

IDF stands for inverse document frequency TF:it’ll convert the raw count of a word in the document into some weight that reflects our belief about how important this word in the document. |