1. Data Mining: Principles and Algorithms Introduction to Network Analysis
Jiawei Han
Department of Computer Science
University of Illinois at Urbana-Champaign
©2012 Jiawei Han. All rights reserved.
Acknowledgements: Based on the slides by Sangkyum Kim and Chen Chen

2. Introduction to Network Analysis
Measure and Metrics of Networks
Mining Information Network 2 2

3. Measure & Metrics Degree Centrality Eigen Vector Centrality
Not all neighbors are equal
Katz Centrality
PageRank
两个假设：
1. 在web图中，如果一个页面节点的入度越大，则表示这个页面越重要
2. 指向页面A的入链的权重不同，质量越高的页面会给A贡献越大的权重
HITS
分为Authority页面与Hub页面；Authority页面指高质量的网页，而Hub为指向authority的那些网页
1. 一个好的Authority页面会被很多Hub页面引用
2. 一个好的Hub页面会指向很多好的Authority页面

4. Measure & Metrics (2): Eigen Vector Centrality

5. Measure & Metrics (3): Katz Centrality

6. Introduction to Network Analysis
Measure and Metrics of Networks
Mining Information Network 6 6

7. What Are Information Networks?
Information network: A network where each node represents an entity (e.g., actor in a social network) and each link (e.g., tie) a relationship between entities
Each node/link may have attributes, labels, and weights
Link may carry rich semantic information
Homogeneous vs. heterogeneous networks
Homogeneous networks
Single object type and single link type
Single model social networks (e.g., friends)
WWW: a collection of linked Web pages
Heterogeneous, multi-typed networks
Multiple object and link types
Medical network: patients, doctors, disease, contacts, treatments
Bibliographic network: publications, authors, venues 7

8. Clustering and Ranking: Two Critical FunctionsH F J I B D
Clustering
Ranking
Not distinguishing objects in each cluster? 1 2 3 4 5 A C E H 1 2 3 4 5 B D I F J 1 2 3 4 5 6 7 8 9 10 A C E B D G I H F J G
Yelp: department store vs. restaurant A C
A better solution:
Integrating clustering with ranking
Comparing apples and oranges? I E G B D J H F

9. RankClus: Integrating Clustering with Ranking
Simple solution: Project the bi-typed network into homogeneous conference network?
Information-loss projection!

10. A New Methodology: RankClus
Ranking as the feature of the cluster
Ranking is conditional on a specific cluster
E.g., VLDB’s rank in Theory vs. its rank in the DB area
The distributions of ranking scores over objects are different in each cluster
Clustering and ranking are mutually enhanced
Better clustering: rank distributions for clusters are more distinguishing from each other
Better ranking: better metric for objects is learned from the ranking
Not every object should be treated equally in clustering!
Y. Sun, et al., “RankClus: Integrating Clustering with Ranking for Heterogeneous Information Network Analysis”, EDBT'09.

11. Simple Ranking vs. Authority Ranking
Proportional to # of publications of an author / a conference
Considers only immediate neighborhood in the network
Authority Ranking:
More sophisticated “rank rules” are needed
Propagate the ranking scores in the network over different types
What about an author publishing 100 papers in very weak conferences?

12. Rules for Authority Ranking
Rule 1: Highly ranked authors publish many papers in highly ranked conferences
Rule 2: Highly ranked conferences attract many papers from many highly ranked authors
Rule 3: The rank of an author is enhanced if he or she co-authors with many highly ranked authors

13. RankClus: Algorithm Framework
Sub-Network
Ranking
Clustering
Initialization
Randomly partition
Repeat
Ranking
Ranking objects in each sub-network induced from each cluster
Generating new measure space
Estimate mixture model coefficients for each target object
Adjusting cluster
Until stable

14. RankClus: Clustering & Ranking CS Conferences
Top-10 conferences in 5 clusters using RankClus in DBLP (when k = 15)
RankClus outperforms spectral clustering [Shi and Malik, 2000] algorithms on projected homogeneous networks