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SNAP复杂网络处理平台
简介：这是斯坦福提供的，用于处理复杂网络的平台库，核心是使用C++编写的，效率相对很高，提供了C++和Python的接口，源代码可以在snap网站下载，也可以在github上下载。
C++
编译，安装与测试
需要安装两个相关的软件，用于绘图，Gnuplot和Graphviz这两个的安装都很简单，在ubuntu上只需要一行安装命令即可，具体的可百度。
在ubuntu环境下，在snap根目录下执行make all，执行完成之后，进入到example文件夹下，可以使用已经提供的接口，例如，使用下列命令生成一个小世界网络。
cd examples/graphgen
./graphgen -g:w -n:1000 -k:4 -p:0.1 -o:smallworld.txt
生成指定度分布的幂律图
查看文件，发现只需要提供nodes和exponent即可生成，速度极快，可能采用的是文章《Efficient and Simple Generation of Random Simple Connected Graphs with Prescribed Degree Sequence》类似的方法，使用命令为：
./graphgen -g:p -n:100000 -p:1.5 -o:t1.5.txt
-n表示nodes数
-p表示exponent（指数）
-o表示输出文件
查看源码，在examples中的graphgen.cpp中，可以使用已经提供的生成图方式。生成幂律图对应以下函数。
G = TSnap::GenRndPowerLaw(N, P, true);
经测试，生成的图符合分布！
附graphgen.cpp文件源码
#include "stdafx.h"int main(int argc, char* argv[]) {Env = TEnv(argc, argv, TNotify::StdNotify);Env.PrepArgs(TStr::Fmt("Graph generators. build: %s, %s. Time: %s", __TIME__, __DATE__, TExeTm::GetCurTm()));TExeTm ExeTm;Tryconst TStr OutFNm = Env.GetIfArgPrefixStr("-o:", "output.txt", "Output graph filename");const TStr Plot = Env.GetIfArgPrefixStr("-g:", "e", "Which generator to use:""\n\tf: Complete graph. Required parameters: n (number of nodes)""\n\ts: Star graph. Required parameters: n (number of nodes)""\n\t2: 2D Grid. Required parameters: n (number of rows), m (number of columns)""\n\te: Erdos-Renyi (G_nm). Required parameters: n (number of nodes), m (number of edges)""\n\tk: Random k-regular graph. Required parameters: n (number of nodes), k (degree of every node)""\n\tb: Albert-Barabasi Preferential Attachment. Required parameters: n (number of nodes), k (edges created by each new node)""\n\tp: Random Power-Law graph. Required parameters: n (number of nodes), p (power-law degree exponent)""\n\tc: Copying model by Kleinberg et al. Required parameters: n (number of nodes), p (copying probability Beta)""\n\tw: Small-world model. Required parameters: n (number of nodes), k (each node is connected to k nearest neighbors in ring topology), p (rewiring probability)\n");const int N = Env.GetIfArgPrefixInt("-n:", 1000, "Number of nodes");const int M = Env.GetIfArgPrefixInt("-m:", 5000, "Number of edges");const double P = Env.GetIfArgPrefixFlt("-p:", 0.1, "Probability/Degree-exponent");const int K = Env.GetIfArgPrefixInt("-k:", 3, "Degree");if (Env.IsEndOfRun()) { return 0; }TExeTm ExeTm;TInt::Rnd.PutSeed(0); // initialize random seedprintf("Generating...\n");PUNGraph G;TStr DescStr;if (Plot == "f") {G = TSnap::GenFull<PUNGraph>(N);DescStr = TStr::Fmt("Undirected complete graph.");} elseif (Plot == "s") {G = TSnap::GenStar<PUNGraph>(N, false);DescStr = TStr::Fmt("Undirected star graph (1 center node connected to all other nodes).");} elseif (Plot == "2") {G = TSnap::GenGrid<PUNGraph>(N, M, false);DescStr = TStr::Fmt("Undirected 2D grid of %d rows and %d columns.", N, M);} elseif (Plot == "e") {G = TSnap::GenRndGnm<PUNGraph>(N, M, false);DescStr = TStr::Fmt("Undirected Erdos-Renyi random graph.");} elseif (Plot == "k") {G = TSnap::GenRndDegK(N, K);DescStr = TStr::Fmt("Undirected k-regular random graph (every node has degree K).");} elseif (Plot == "b") {G = TSnap::GenPrefAttach(N, K);DescStr = TStr::Fmt("Undirected Albert-Barabasi Preferential Attachment graph (each new node creades k preferentially attached edges).");} elseif (Plot == "p") {G = TSnap::GenRndPowerLaw(N, P, true);DescStr = TStr::Fmt("Random Graph with Power-Law degree distribution with exponent P.");} elseif (Plot == "c") {G = TSnap::ConvertGraph<PUNGraph>(TSnap::GenCopyModel(N, P));DescStr = TStr::Fmt("Copying model by Kleinberg et al. Node u comes, selects a random v, and with prob P it links to v, with 1-P links u links to neighbor of v. Power-law degree slope is 1/(1-P).");} elseif (Plot == "w") {G = TSnap::GenSmallWorld(N, K, P);DescStr = TStr::Fmt("Watts-Strogatz Small-world model. Every node links to K other nodes.");}printf("done.\n");TSnap::SaveEdgeList(G, OutFNm, DescStr);Catchprintf("\nrun time: %s (%s)\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());return 0;
}
Python
与C++仅接口的调用形式不同，核心实现使用的是C++
重点总结
SNAP这个平台一直以来没有发现，这应该是非常优秀的一个平台，速度极快，有时间可以好好研究一下。
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