Tutorials on social networks mining

Social Network Mining.
Jennifer Neville, Purdue University, Foster Provost, New York University, Stern School of Business.
AAAI 2008 Tutorial, Chicago, IL, USA
webpage slides additional resources

Modeling Social and Information Networks: Opportunities for ML.
Jure Leskovec.
ICML 2009.
webpage slides video

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A simple implementation of DTW(Dynamic Time Warping) in C#/python

DTW(Dynamic Time Warping) is a very useful tools for time series analysis. This is a very simple (but not very efficient) c# implementation of DTW, the source code is available at https://gist.github.com/1966342 . Use the program as below: double[] x = {9,3,1,5,1,2,0,1,0,2,2,8,1,7,0,6,4,4,5}; double[] y = {1,0,5,5,0,1,0,1,0,3,3,2,8,1,0,6,4,4,5}; SimpleDTW dtw = new SimpleDTW(x,y); dtw.calculateDTW(); The python implementation is available at https://gist.github.com/3265694 . from python-dtw import Dtw import math dtw = Dtw([1, 2, 3, 4, 6], [1, 2, 3, 5], distance_func=lambda x, y: math.fabs(x - y)) print dtw.calculate() #calculate the distance print dtw.get_path() #calculate the mapping path

Install mysql-python with mariadb

mysql-python requires libmysqlclient-dev in ubuntu, but the installation of mariadb will have the lib with unmet dependenccies, so the error of "mysql_config not found" may occurred if you install mysql-python via pip. The case is that mariadb has a compatible package, if you have the ppa setup as in http://downloads.mariadb.org/ . Just "sudo apt-get install libmariadbclient-dev".

PrefixSpan source code in python

The prefixspan is a key algorithm for mining sequential patterns. I have implemented the algorithm in Python. The algorithm is based on the following paper: Jian Pei, Jiawei Han, Senior Member, Behzad Mortazavi-asl, Jianyong Wang, Helen Pinto, Qiming Chen, Umeshwar Dayal. Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Transactions on Knowledge and Data Engineering, 2004. or their conference paper You may download the source code at the following addresses: Link1

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