• 9-临界图边数的下界，孙庆波，张俊萍，本文主要运用Dischange方法，深入讨论了临界图边数的下界问题，提高了最大度为9的临界图边数的下界，本文虽然还没有证明出Vizing猜想�
• 该程序能够生成任何节点大小、状态大小和度数大小的离散贝叶斯网络。 当您需要大规模贝叶斯网络进行算法验证时，它会很有帮助。
• Achievement motivation and self-reported grade point average ACHIEVEMENT MOTIVATION AND SELF-REPORTED GRADE POINT AVERAGE PATRICK B.... The University of Wisconsin, Madison ...degree of
• Degree number of edges connected to a ... Average degree = 2*edges/nodes Path how many edges between two specific nodes. Clustering coefficent How many percentage of the triple nodes connected ...
Degree

number of edges connected to a node. Average degree = 2*edges/nodes

Path

how many edges between two specific nodes.

Clustering coefficent

How many percentage of the triple nodes connected together.

In a team, we can use the above to represent a team.

How to form a network?

(1) Random connection

P: the probability of 2 nodes connected

N:NO of the nodes.

if P> 1/(N-1) then the whole network may conncet.

(2) Small world

(3)Preferential attachment

Nodes may connect to a node with the probability proportional to the NO of the nodes connecting to this node. Just handful of nodes have high degree.
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• Computer Science MS Degree MS Degree or Depth 45.00 Hours Required http://scpd.stanford.edu/online-engineering-courses.jsp The Master of Science degre...


Computer Science MS Degree
MS Degree or Depth

45.00 Hours Required

http://scpd.stanford.edu/online-engineering-courses.jsp

The Master of Science degree in Computer Science indicates two things to prospective employers. First, it guarantees that you have a broad grounding in computer science as a discipline. Second, it certifies that you have studied a particular area in detail and thus have additional depth in a particular specialty. Both components are important to the Master's program, and it is not possible to secure a Stanford MSCS degree that does not meet both requirements. The central requirement for the MSCS degree is completion of at least 45 units that represent an approved academic plan. The concrete representation of that academic plan is your program sheet, which lists the courses you intend to use to satisfy the 45-unit requirement.
Breadth Requirement
Students are asked to demonstrate breadth by taking courses in three general areas:
Mathematical and Theoretical Foundations
Computer Systems
AI and Applications
Typically, each area is organized as a small set of required courses and a larger set from which you can choose particular courses that fit best with your overall program. To satisfy the breadth requirement, you must demonstrate that you have taken each of the required courses, along with an appropriate subset of the higher-level breadth courses that meet the requirements for each area. It is important to understand that only coursework can be used to satisfy the breadth requirement. Additionally, you may not count more than 21 units from the set of courses that comprise the program prerequisites (courses numbered between CS100 and CS109) and the courses listed under the breadth requirement category. If you need to take more courses in these categories, your program will have to include more than 45 total units. Sometime early in your first quarter - preferably in the first week or two - you should schedule a meeting with your academic advisor and go over your breadth requirements. Contact the department of Computer Science for detailed information on the Breadth Requirement by sending email to: admissions@cs.stanford.edu
Seminars
The MSCS program requires you to complete at least one 500-level CS seminar (or EE380 or EE385A) so that you have some exposure to the research activity of the department. Although they may take more, students may only count a maximum of three units of seminars (or other 1-2 unit courses) toward the MSCS degree.
Depth Requirement
In addition to the breadth requirement, the Stanford MSCS program requires that all students take at least 21 units in a specific area of specialization. Most students complete one of the ten department approved specializations, but may also petition the MSCS committee to approve a specialization of their own design. The ten approved specializations are:
Artificial Intelligence
Biocomputation
Computer and Network Security
Database Systems
Human-Computer Interaction
Numerical Analysis/Scientific Computation
Real-World Computing
Software Theory
Systems
Theoretical Computer Science
In most cases, a specialization consists of a set of required courses, a larger set of courses out of which you must select some subset, and a larger set from which you select additional courses to fill out the 21-unit requirement. More detailed information about breadth requirements and specialization areas can be found at: CS degree planning tool. Questions about admission to the graduate program in Computer Science should be directed to: admissions@cs.stanford.edu
Electives
Elective courses are really up to the student to select, even though the entire program must be approved by their advisor. In general, courses in computer science numbered at the 100-level or above (with the exception of CS196, 197, and 198) are suitable as electives. Courses in related departments, such as Electrical Engineering, Mathematics, and Statistics, numbered at the 100-level or above and technical in nature are also likely to be approved. On the other hand, courses that are completely unrelated to computer science would not normally be appropriate as electives.
Additional Requirements
Minimum GPA requirement: In order to receive an MSCS degree, the student's GPA in the courses they submit on their program sheet must be at least 3.0, which corresponds to a B in Stanford's grading scale. Note that students need not get a B in every course. All the requirement states is that the overall GPA, which is simply the average of the numeric grade weighted by the number of units in each course, must be at least a 3.0. Note, however, that the GPA is computed only for the courses students submit on their program sheet. If a student does poorly in several courses, it may be wise for them to eliminate those courses from their program sheet and substitute other courses in which they have done better. Substitutions may require the student to take more than 45 units, but it is important to know that a single disastrous grade will not necessarily doom their entire program.
Letter-grade requirement: This requirement is mostly self-explanatory but nonetheless deserves emphasis. At least 36 of the required 45 units, including all of the depth units submitted for specialization, must be taken for a letter grade. Note that seminar courses, which must be taken on an S/NC basis, are not letter-graded courses. The remaining 9 units may be taken on a credit/no credit basis if the student so chooses.

Tuition
For course tuition and fees, please click Tuition & Fees.
Time Commitment
Most part-time students take an average of 3 to 5 years to complete the 45-unit requirement.  You must complete a Master's degree within 5 years of starting the program.
Admissions
Detailed information about the graduate degree program and admission process can be found on the Computer Science Department web site: Computer Science Graduate Program. For assistance while in the application process, please contact the Computer Science Student Services Office using the following email address: admissions@cs.stanford.edu
Expected Background
The MSCS program assumes that all entering students have acquired the foundations of computer science at the level of an undergrad minor. At Stanford, these foundations are represented by the following courses, which are considered as the standard prerequisites for the program:
CS103 (Logic, Automata and Complexity)
CS109 or STAT116 or CME106 or MS&E220(Probability)
CS161 (Algorithmic Analysis)
CS107 (Computer Organization and Systems)
CS110 (Principles of Computer Systems)
If you have taken these courses - either at Stanford or elsewhere - you have the necessary background to begin studying at the MSCS level.
Program Proposal
Students must file their initial program sheet before the end of your first registered quarter as a MSCS student. Filing the program sheet, however, does not lock you into taking exactly the set of courses you originally propose. If you need to change your plan of study, you must simply renegotiate the contract, which means filing a new program sheet that represents your updated course of study. You must get your advisor's signature on the revised plan but need not get new signatures for individual courses that were approved on a previously filed program. The important thing to remember is that, before you will be cleared for graduation, you must have a program sheet on file that matches the courses that you in fact completed. If you decide to change your course of study, you should get a new program sheet signed as soon as possible to ensure that the changes are in fact approved.

转载于:https://www.cnblogs.com/kungfupanda/p/6649487.html
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• 人工网络生成程序，可在CSDN上免费下载 或者科学网这边也可以下载 ...• k: average degree;• maxk: maximum degree;• mu: mixing parameter (the higher the mixing parameter of a network is, the more diffic...

人工网络生成程序，可在CSDN上免费下载
或者科学网这边也可以下载
参数
• n: number of vertices;• k: average degree;• maxk: maximum degree;• mu: mixing parameter (the higher the mixing parameter of a network is, the more difficult it is to reveal the communitystructure);• minc: minimum for the community sizes;• maxc: maximum for the community sizes.
mu值越大，社区结构越难发现
下面是可执行程序的操作流程

非重叠社区的参数主要由

转载于:https://www.cnblogs.com/bethansy/p/6891852.html
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• ER网络的创建、degree distribution与可视化 复杂网络是一门交叉学科，理学院也在研究、计算机院也在研究。 它能应用在许多领域，例如传染病研究、社交网络分析、金融、交通网、电力网等等 今天的实验内容是，使用...
ER网络的创建、degree distribution与可视化
复杂网络是一门交叉学科，理学院也在研究、计算机院也在研究。
它能应用在许多领域，例如传染病研究、社交网络分析、金融、交通网、电力网等等
今天的实验内容是，使用C语言创建一个ER网络，并统计degree distribution（我也不知道中文名是什么），最后使用图形进行展示
下一步的实验目标是给出平均degree和nodes数量，也能创建ER网络（现在的degree是随机的）
参考的资料如下：

复杂网络之ER随机网络的构建、度分布计算、可视化实现（python实现）
复杂网络介绍（一）
图论算法（二）：networkx 新建Graph（节点+连边）

主要是通过理解链接1中的python代码，然后将其优化后使用C语言表达，最后将输出的数据使用python进行可视化
代码如下
/**
* Created by yingmanwumen on Oct 30 10:04:55
* To create a random ER network and save it to file
* then to calculate the degree distribution
*/

#include <stdio.h>
#include <stdlib.h>
#include <stdbool.h>
#include <time.h>

// create a random num between 0 and 1
#define RANDOM (((rand() % 10001)) / 10000.0l)

typedef int **Mat_t;
typedef double *Vec_t;
typedef struct
{
int n;
double p;
Mat_t rlatMat;  // relationship matrix
Vec_t degDist;  // degree distribution
int * deg;		// the degree of every node
int links;		// nums of links in ER net
double avgDeg;	// average degree
}ERnet_t, *ERnet_pt;

void ERnetCre(ERnet_pt net, int n, double p);
void ERnetSaveToFile(ERnet_pt net);
void ERnetFree(ERnet_pt net);
int rlatMatInit(Mat_t rlatMat, int n, double p);
double degDistStat(Vec_t degDist, int *deg, Mat_t rlatMat, int n);

int main()
{
ERnet_t net;
srand(time(NULL));

int n;		// size of nodes
double p;	// probability to connect between two nodes
printf("nodes num: ");
scanf("%d", &n);
printf("probability: ");
scanf("%lf", &p);
ERnetCre(&net, n, p);  // create a ER network
ERnetSaveToFile(&net);  // save to file
ERnetFree(&net);  // free

return 0;
}

/*
create a new ERnet
*/
void ERnetCre(ERnet_pt net, int n, double p)
{
net->n = n;
net->p = p;
net->deg     = (int *)malloc(sizeof(int) * n);
net->degDist = (Vec_t)malloc(sizeof(double) * n);
net->rlatMat = (Mat_t)malloc(sizeof(int *) * n);
for (int i = 0; i < n; i ++)
{
net->rlatMat[i] = (int *)malloc(sizeof(int) * n);
}

net->links  = rlatMatInit(net->rlatMat, n, p);
net->avgDeg = degDistStat(net->degDist, net->deg, net->rlatMat, n);
}

/*
save data
*/
void ERnetSaveToFile(ERnet_pt net)
{
FILE *degDist_fp, *rlatMat_fp;
degDist_fp = fopen("degDist.txt", "w+");
rlatMat_fp = fopen("rlatMat.txt", "w+");
if (!degDist_fp || !rlatMat_fp)
exit(-1);

for (int i = 0; i < net->n; i ++)
{
fprintf(degDist_fp, "%d %lf\n", i, net->degDist[i]);
}
for (int i = 0; i < net->n; i ++)
{
for (int j = 0; j < net->n; j ++)
{
fprintf(rlatMat_fp, "%d ", net->rlatMat[i][j]);
}
fprintf(rlatMat_fp, "\n");
}
fclose(degDist_fp);
fclose(rlatMat_fp);
}

void ERnetFree(ERnet_pt net)
{
free(net->degDist);
free(net->deg);
for(int i = 0; i < net->n; i ++)
{
free(net->rlatMat[i]);
}
free(net->rlatMat);
net->degDist = net->deg = net->rlatMat = NULL;
net->n = net->p = net->links = net->avgDeg = 0;
}

/*
init the relationship matrix
*/
int rlatMatInit(Mat_t rlatMat, int n, double p)
{
int links = 0;
for (int i = 0; i < n; i ++)
{
for (int j = 0; j < n; j ++)
{
bool flag = RANDOM <= p;  // probability to connect is p
links += flag;
rlatMat[i][j] = rlatMat[j][i] = flag;
}
}
return links;
}

/*
the statistical degree distribution
*/
double degDistStat(Vec_t degDist, int *deg, Mat_t rlatMat, int n)
{
double avg = 0.0;
double fraction = 1.0l / n;
for (int i = 0; i < n; i ++)
{
for (int j = 0; j < n; j ++)
{
deg[i] += rlatMat[i][j];
}
}
for (int i = 0; i < n; i ++)
{
avg += deg[i];
degDist[deg[i]] += fraction;
}
return avg / n;
}

python可视化代码如下
import networkx as nx
import matplotlib.pyplot as plt

def showGraph():
G = nx.Graph()

print('start to run')
f = open('rlatMat.txt', 'r')
buf = f.read()
f.close()
buf = buf.split('\n')
for i in range(0, len(buf)):
vec = list(buf[i].split(' '))
for j in range(0, len(vec)):
if vec[j] and int(vec[j]) == 1:
G.add_edge(i, j)
nx.draw(G)
plt.show()

showGraph()

说实话我想搞个git仓库了，都大二了还在傻乎乎地复制粘贴代码
贴一下运行结果
$./ERnet nodes num: 30 probability: 0.25$ python draw.py
start run


$cat rlatMat.txt 0 0 1 1 0 1 0 1 0 0 0 0 0 1 1 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 0 1 0 0 1 0 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 1 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0  $ cat degDist.txt
0 0.000000
1 0.000000
2 0.000000
3 0.000000
4 0.000000
5 0.166667
6 0.166667
7 0.033333
8 0.166667
9 0.166667
10 0.100000
11 0.100000
12 0.033333
13 0.066667
14 0.000000
15 0.000000
16 0.000000
17 0.000000
18 0.000000
19 0.000000
20 0.000000
21 0.000000
22 0.000000
23 0.000000
24 0.000000
25 0.000000
26 0.000000
27 0.000000
28 0.000000
29 0.000000



展开全文
• The bidirectional selection between two classes widely emerges in various social lives, such as commercial trading and mate... Furthermore, it is found that the high average degree of networks cont
• networks，一般都会解释成人际关系网络。人际关系网络，就是人与人之间的关联情况的描述。...Average Degree = 2({Edges}\div{Node})  Path Length【路径长度】，从一个节点到另一个节点的路径长度 Connected...
• ER网络的创建之G（N，L）法 ER网络的创建一般分为两种方法...大体思路：根据average degree 计算出 L=avgDeg∗N/2L = avgDeg * N / 2L=avgDeg∗N/2，然后随机分配L条边 其中最重要的代码如下： void rlatMatInit(Mat_t
• 度（degree）/ 自由度，也叫valency（直译化合价），是指Graph(图)中一个节点，有多少条边连接其上。 图的度是其所有节点的度中最大值。对于“度”的理解，结合自由度，和化合价这个英文原词来理解，本意应该是...
• average degree of 16. p_in, p_out: the probability of intra-community and inter-community links. r = p_out/p_in = 0.01, 0.02,..., 1.0 gen-networks / gen_WS.m（Matlab） 根据Watts-Strogatz模型生成网络。...
• Our empirical evaluation shows that all of the tensor decomposition models perform well when the average degree of an entity in a graph is high, with constraint-based models doing better on graphs ...
• 图的基本概念 如何表示图 点集合用N表示，边集合用E表示，图用G...平均度数（average degree） 图中每个节点的连边数均值称为平均度数， 无向图的平均度数是 有向图的平均度数是 二部图（Bipartite Graphs） 把图G的
• Top 5 Programming Languages to Learn in 2020 to Get a Job Without a College Degree 自己边看视频边做的导图，做的很简陋哈 1.python✨ There are many different frameworks and libraries in Python that ...
• https://www.pcmag.com/commentary/343924/dont-dismiss-georgia-techs-6-600-online-masters-degree ...Don't Dismiss Georgia Tech's \$6,600 Online Master's Degree I'm not about to let my ideological reser...
• The N (2 ) cows are interested in figuring out which cow has the smallest average degree of separation from all the other cows. excluding herself of course. The cows have made M (1 ) movies and it is ...
• Oracle 11g中的IO Calibrate（IO校准）--Automatic Degree of Parallelism(DOP) Oracle 11g中的IO Calibrate（IO校准）...
• Being average is not a poor issue. The thin-and-light Gateway EC14D07u travels nearly anywhere; its processor and a 320GB tough generate are enough for enterprise and school work; and its LCDpacks in ...
• 05年浙江大学计算机硕士毕业去向(05 years of computer master's degree in Zhejiang University)05年浙江大学计算机硕士毕业去向(05 years of computer master's degree in Zhejiang University)05 Zhejiang ...
• ## R语言绘制箱线图

千次阅读 2019-08-01 16:05:27
1.数据处理,把数据处理成箱图...-data.frame(Average_Degree=c(13,4,2,12.15,6.667,10.67,2.286,1,1,5,11.2,11.5,11.3,11.5,11.5),Method=rep("DYNMOGA",times=15)) a2<-data.frame(Average_Degree=c(11.57,8.2,...
• average neighbor degree 5.1 degree distribution 度分布 5.1.1 度分布 表示为 p（k） 为节点的度（连边数）为k的频率分布 5.1.2 呈幂律分布的度分布-无标度网络 无尺度网络的分布满足幂律分布（powder-l...
• "name": "Structure","children": [{"description_en": "Indicates the overall dissemination cost of the ... It is the average dependency degree among software units based on the statistical values ...
• 一．绪论 1.何谓程序设计？ 程序 = 算法 + 数据结构 2.数据结构的定义 是相互之间存在一种或多种特定关系的数据元素的集合 ...3.数据、数据元素、数据对象的概念 ... 数据（data）：对客观事物的符号表示，含义很广...
• Achievement motivation and self-reported grade point average ACHIEVEMENT MOTIVATION AND SELF-REPORTED GRADE POINT AVERAGE PATRICK B.... The University of Wisconsin, Madison ...degree of
• degree distribution 关系多不多 通过度集和顶点集innerjoin算出来 平均路径长度 average path length 网络距离 通过用pregel接口定制算法 网络密度 dense

...