1. use git hub, push figures in it, 4 figures a paper. play with toy sample
2. do toy sample, use Tex
for edge2vec thing:
1. debiashing hubs
link random walk to nodes with less degree
2. when links goes to the hub, it tend to random walk to the other links close to it.
Tuesday, January 24, 2017
Monday, January 23, 2017
python 2.7 and 3.5
http://conda.pydata.org/docs/py2or3.html
$ conda create -n py35 python=3.5 anaconda
#
# To activate this environment, use:
# $ source activate py35 (source activate py3)
#
# To deactivate this environment, use:
# $ source deactivate
#
Monday, October 3, 2016
Linux command
sync local files to remote server:
rsync -avz ./health_care gao27@burrow.soic.indiana.edu://nfs/nfs4/home/gao27/health_care
sync remote server files to local:
rsync -avz gao27@burrow.soic.indiana.edu://nfs/nfs4/home/gao27/health_care ./
/usr/libexec/java_home -verbose can return all javas in your computer
把一个文件的前十行 存到另外一个文件中
ps aux 指令详解
https://blog.csdn.net/hanner_cheung/article/details/6081440
df -h 查看分区剩余容量
d -i 查看index的剩余容量
du -h -d 1 查看当前文件夹大小
vim 的教程:
https://harttle.land/2015/11/07/vim-cursor.html
使用&和wait改造
在每个进程中使用&符号进行让脚本在后台运行,无需等待当前进程结束。
为了确保每个进程都执行完成,最后务必使用wait关键字,用来确保每一个子进程都执行完成。
[root@artisan test]# cat call_parallel.sh
#!/bin/bash
#当前目录下执行如下脚本 相对路径
./1.sh &
./2.sh &
wait
echo "继续执行剩下的逻辑..."
[root@artisan test]#
文件重命名:https://www.cnblogs.com/longdouhzt/archive/2012/04/30/2477282.html
/usr/libexec/java_home -verbose can return all javas in your computer
把一个文件的前十行 存到另外一个文件中
sed -n -e '1,10p' test.csv > sample.csv
ps aux 指令详解
https://blog.csdn.net/hanner_cheung/article/details/6081440
df -h 查看分区剩余容量
d -i 查看index的剩余容量
du -h -d 1 查看当前文件夹大小
vim 的教程:
https://harttle.land/2015/11/07/vim-cursor.html
使用&和wait改造
在每个进程中使用&符号进行让脚本在后台运行,无需等待当前进程结束。
为了确保每个进程都执行完成,最后务必使用wait关键字,用来确保每一个子进程都执行完成。
[root@artisan test]# cat call_parallel.sh
#!/bin/bash
#当前目录下执行如下脚本 相对路径
./1.sh &
./2.sh &
wait
echo "继续执行剩下的逻辑..."
[root@artisan test]#
文件重命名:https://www.cnblogs.com/longdouhzt/archive/2012/04/30/2477282.html
Monday, May 16, 2016
genetic algorithm
https://segmentfault.com/a/1190000004155021
http://blog.sina.com.cn/s/blog_4cde15140100gi2u.html
http://blog.csdn.net/v_JULY_v/article/details/6132775
http://arxiv.org/pdf/cond-mat/0501368.pdf
https://www.researchgate.net/profile/Clara_Pizzuti/publication/220739952_Community_detection_in_social_networks_with_genetic_algorithms/links/542ea6a50cf277d58e8ed068.pdf
https://arxiv.org/pdf/cond-mat/0604419.pdf
https://www.researchgate.net/profile/Clara_Pizzuti/publication/221417662_A_Multi-objective_Genetic_Algorithm_for_Community_Detection_in_Networks/links/542ea6a10cf29bbc126f39d9.pdf
http://see.xidian.edu.cn/iiip/mggong/down/PHYSA2012Gong.pdf
http://arxiv.org/pdf/cond-mat/0501368.pdf
https://pdfs.semanticscholar.org/c342/c914dff35d6630c5963a54e3a07adf84f44f.pdf
..........
http://arxiv.org/pdf/cond-mat/0308217.pdf
network modularity
During initial population creation, each node is assigned a random community identifier. However we need a mechanism to give a bias for initial placement of nodes into communities. If two nodes are to be in the same community, they should have connectivity with each other; in the simplest case they might be neighbors. From this assumption, after assigning random community IDs to nodes, we randomly select some nodes and assign their community IDs to all of their neighbors. This bias in the initial population creation improves the convergence of the algorithm and eliminates unnecessary iterations.
http://blog.sina.com.cn/s/blog_4cde15140100gi2u.html
http://blog.csdn.net/v_JULY_v/article/details/6132775
http://arxiv.org/pdf/cond-mat/0501368.pdf
https://www.researchgate.net/profile/Clara_Pizzuti/publication/220739952_Community_detection_in_social_networks_with_genetic_algorithms/links/542ea6a50cf277d58e8ed068.pdf
https://arxiv.org/pdf/cond-mat/0604419.pdf
https://www.researchgate.net/profile/Clara_Pizzuti/publication/221417662_A_Multi-objective_Genetic_Algorithm_for_Community_Detection_in_Networks/links/542ea6a10cf29bbc126f39d9.pdf
http://see.xidian.edu.cn/iiip/mggong/down/PHYSA2012Gong.pdf
http://arxiv.org/pdf/cond-mat/0501368.pdf
https://pdfs.semanticscholar.org/c342/c914dff35d6630c5963a54e3a07adf84f44f.pdf
..........
http://arxiv.org/pdf/cond-mat/0308217.pdf
network modularity
During initial population creation, each node is assigned a random community identifier. However we need a mechanism to give a bias for initial placement of nodes into communities. If two nodes are to be in the same community, they should have connectivity with each other; in the simplest case they might be neighbors. From this assumption, after assigning random community IDs to nodes, we randomly select some nodes and assign their community IDs to all of their neighbors. This bias in the initial population creation improves the convergence of the algorithm and eliminates unnecessary iterations.
Classic machine learning algorithm
PCA
LDA
LSA
matrix factorization
EM
SVM
Random forest
其他:
KMP算法
Hash表算法
LDA
LSA
matrix factorization
EM
SVM
Random forest
其他:
KMP算法
Hash表算法
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