IEEE Intelligent Systems, Special Issue on Social Learning, 2010

1. IEEE Intelligent Systems, Special Issue on Social Learning, 2010

2. Goal: analyzing how the networks evolve over time • Models of social networks • networks follow power-law degree distribution, • have a small diameter • exhibit small-world structure and community structure • Social networks is a function of time • The Obama network • Various Models have been studied • many tools have been built (example)

3. Dynamic Models • The preferential attachment model: • assumes that new network nodes have a higher probability of forming links with high-degree nodes • creating a “rich-get-richer” effect Network diameter shrinks over time From: Leskovec et al. KDD’05

4. Questions raised in this paper • Studying network formation strategies • Microscopic level • Graph Evolution Rules • Association rules • From current network configurations • Predict future edges and nodes • Rules • Old-old • Old-new • New-new

5. Co-authorship Network • http://www.arnetminer.org/viewperson.do?id=486660&name=Qiang%20Yang • http://academic.research.microsoft.com/VisualExplorer#435931

6. Customer buys both Customer buys diaper Customer buys beer Basic Concepts: Frequent Patterns and Association Rules (from J. Han) • Itemset X={x1, …, xk} • Find all the rules XYwith min confidence and support • support, s, probability Pr(X,Y) • confidence, c,conditional probability Pr(Y|X). • Let min_support = 50%, min_conf = 50%: • A  C (50%, 66.7%) • C  A (50%, 100%)

7. Association Rules on Graphs • First, we need to record time stamps on graphs • Nodes • Edges • A large-degree node (label 3), which at time t is connected to four medium-degree nodes (label 2), at time t +1 will be connected to a fifth node. • The collaboration-rich researcher gets richer.

8. G-span based GER Algorithm

9. Example GER Rules

10. Too many rules? Ranking

11. Datasets

12. Precision-Recall Curve

13. Dataset Statistics