Statistical Relational Learning and Social Network
Simon Fraser University
Instructor: Oliver Schulte
List of Readings
- Week 1
and 2. Background: Relational Databases, Feature Vector Classification.
Getoor on why relational learning cannot just
"flatten" the relational database into a single table: From Dissertation, Stanford U 2002.
Another introductory discussion of this point appears
in "ILP for Knowledge Discovery in Databases", S. Wrobel, Sections
4.1-4.2, in "Relational Data Mining", Springer2 2001. I can't seem to
find an electronic version of this piece.
Overheads on Relational Models and
- Overheads on decision tree learning.
CI Space, Decision Tree Applet or
Exploratorium. These are applets that allow you to run decision tree
learner and explore datasets. I suggest you play around with at least one
data set to get a feel for the learning algorithms.
Classification: Propositionalization and ILP.
- The FOIL ILP system. Overview from Inductive Logic
Programming, Techniques and
Applications, Lavrac and Dzeroski,
1994. The whole book is available at http://www-ai.ijs.si/SasoDzeroski/ILPBook/.
Software: FOIL .
- Data Mining in Social Networks,
Neville and Jensen. Symposium on Dynamic Social Network Modeling and
Analysis. National Academy of
Sciences. November 7-9, 2002. Washington, DC: National Academy Press.
Data Mining, An Introduction, Dzeroski, SigKDD 2003.
Bayes Nets. Software: CIspace.
E. Charniak, 1991. "Bayesian
Networks without Tears", AI magazine.
Read through Kevin Murphy's Intro http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
. Don't worry if you don't understand all the details.
Probabilistic Relational Models, Getoor et al., 2001.
Network Analysis: Introductions and Overview.
JUNG-check out the demos, like
the ranking demo. For other software, see http://en.wikipedia.org/wiki/Social_network_analysis_software
. Pajek is a commonly used tool.
on the Web: What
is Social Network Analysis? and Basic
Concepts in Social Network Analysis .
Survey: Different Aspects of Social
Network Analysis, Mohsen Jamali and Hassan Abolhassani, IEEE/WIC/ACM
conference on Web Intelligence 2006. Official Link is http://portal.acm.org/citation.cfm?id=1249050&dl=&coll=.
Introduction to Basic Concepts: Introduction
to the Formal Analysis of Social Networks Using Mathematica, Luis
Izquierdo and Robert Hanneman, Wolfram Library Archive.
to Complex Networks: M. E. J. Newman, The structure and function of
complex networks, SIAM Reviews, 45(2): 167-256, 2003 Newman.pdf
. Let's read Sections I,II and III and Section VII, pages 30-35
(total 26 pages).
Research in Network Analysis and Data Mining in Networks
- D. Kempe, J.
Kleinberg, E. Tardos, Maximizing the spread of influence through a social
- J. Kleinberg. The
small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM
Symposium on Theory of Computing, 2000. Also appears as Cornell Computer
Science Technical Report 99-1776 (October 1999). I highly recommend that
you watch at least part of his invited talk on SNA at http://videolectures.net/kdd07_kleinberg_cisnd/
Mining – a survey. Getoor and Diehl, SIGKDD 2005.
Flake, S. Lawrence, and C.L. Giles. Efficient
identification of web communities. Proc. of the 6th International
Conference on Knowledge Discovery and Data Mining (KDD), 2000.
As a further reference (optional for this course), a more rigorous
follow-up paper by Flake is available on-line:
Clustering and Minimum Cut Trees, Internet Mathematics Vol. 1, No. 4:
385-40. Gary William Flake, Robert E. Tarjan, and Kostas
SR Framework: Markov
Logic Networks: A Unifying Framework for SR learning. Software: ALCHEMY.