1. RELATIONSHIP MINING IN SOCIAL NETWORKS
CS 8803 AIA
RELATIONSHIP MINING IN SOCIAL NETWORKS
GROUP 16
Abhishek Saxena
Ankit Kharadi
Chirag Rajan

2. Outline Introduction Goal (s) of our Project Social Networks
Principles
Phases of our project
Components of our project
Applications

3. Introduction Social Networks are growing at an exponential rate
These networks contain a wealth of information which can be used

4. Goal(s) of our Project
To uncover hidden associations as functions of visible relationships among social network entities
Visualize the relations

5. Statistics about Top Social Networks
Table 1 Top 10 Social Networking Sites For April 2006 (US, Home and Work)
Site
Apr-05
Apr-06
YOY
Growth
MySpace
8210
38359
367%
Blogger
10301
18508
80%
Classmates Online
11672
12865
10%
YouTube NA
12505
MSN Groups
12352
10570
-14%
AOL Hometown
11236
9590
-15%
Yahoo! Groups
8262
9165
11%
MSN Spaces
1857
7165
286%
Six Apart TypePad
5065
6711
32%
Xanga
5202
6631
27%

6. Top Social Networking Sites
1) MySpace
2) Facebook
3) Takepart
4) Nextcat
5) LinkedIn
6) Friendster
7) Flickr
8) Massify
9) Idealist.org
10) Goodreads.com

7. Principles Mining results should be governed by user preferences
Multiple Relationships play a role in the existence of a hidden relationship

8. Phases of our project Phase 1: Gathering data
Phase 2: Transforming data into usable format
Phase 3: Mining named entities using CRF based extractor
Phase 4: Applying Data Mining Techniques based on expectation maximization
Phase 5: Visualization of Results

9. What we used.... We chose DBLP as the social network to mine
Through this project we hope to shed light on advantages and issues pertinent to mining hidden relationships

10. Components of our project
We have used the following tools/languages to extract the data and process it to generate relational information....
Crawlers
Parsers
Stanford NER Library
MATLAB
JUNG

11. Crawlers
We have used crawlers (mostly WebSphinx) to crawl researchers web pages and fetch data about papers published and ...
In which conferences
In what years

12. Stanford NER Library
The Stanford NER Library is an open source library used to perform analysis of given data
The NER can detect 3 kinds of entities namely...
Person
Location
Organization
It employs a conditional random field based extractor

13. MATLAB We used MATLAB to perform matrix analysis on the given graphs
MATLAB offers several tools to compute statistical measures over massive data sets

14. JUNG
JUNG is a Java Library that helps draw graphs and visualize complex relationships among different entities
We're using JUNG to visualize the relationships between different authors

15. Output 0.072 VLDB 0.181 ICDE 0.602 KDD 0.145 SIGMOD Coefficient
Query : Researcher X,Researcher Y,Researcher Z
0.072
VLDB
0.181
ICDE
0.602
KDD
0.145
SIGMOD
Coefficient
Relation

16. Applications
Friend Suggestion
Targeted Marketing
Network Prediction

17. Thank You
Questions/Comments?