1. Presenter: Waqas Nawaz
Multi-User Personalized Community Detection using Collaborative Similarity Measure
The Case of Enron* Database
Waqas Nawaz, Yongkoo Han, Kifayat-Ullah Khan, Young-Koo Lee
* Publically available dataset
Presenter: Waqas Nawaz
Data and Knowledge Engineering Lab,
Department of Computer Engineering, Kyung Hee University,
Yongin-si, Gyeonggi-do , Republic of Korea

2. Agenda Motivation Problem Statement Solutions by Category
Contributions

3. Appendix
Motivation (1/2)
is an asynchronous and most prevalent means of communication among others (vocal, visual)
Users: Worldwide service providers namely Hotmail, Yahoo and Gmail has 330, 302 and 193 million users respectively in [1]
Communication: Yahoo network deliver around 38 thousand s per second Globally[2]
It contains significant information:
sender, receivers, contents, time etc.
to depict one’s communication behavior or pattern

4. Motivation (2/2) Importance of Communities
Community detection [4] has received a great deal of attention with the knowledge of entire network [5] [6]
Communities towards better understanding
Community structures can help us understand the network more deeply and reveal interesting properties shared by the members
People belonging to the same community are more likely to have common communication behaviors
Applications
The identified communities can be used for classifying s, discovery of prominent users, and highlighting abnormal activities inside the network [7] …[18]

5. Personalization Personal Interaction Network (Net)
from Host’s (user ‘A’) Repository A B @ C F G 7 2 4 5
Host A B @ C E D 4 2 3 5
Host
User ‘A’ as Sender
User ‘A’ as Receiver

6. Problem Statement Formulation (1/2)
provides a sophisticated bridge among individuals or groups to interact and share useful information
Is there any possibility to identify the groups having similar communication pattern?
Can we partition the individuals or sources separately with irregular activity?
Grouping the strongly connected individuals with analogous interaction patterns (behaviors) is admissible?
Under the constraint of privacy and unavailability of entire network information

7. Problem Statement Formulation (2/2)
By Using Multi-User Personalized Information (Restricted Information)
Find the K group of users with similar communication behavior
How to Efficiently and Effectively anticipate the global network community structure using multi- user personalized information?

8. Solutions: Categorical Overview
History
Classical Approaches
Global information is utilized for community detection
Unavailability of the data or time complexity for very large data
Localized Approaches
Partial data (usually in decentralized environment) of a particular user is used to identify communities
Ineffective, only link information is exploited
Personalized Approaches
All the data of a particular user is used for community detection
Limit the view of the network up to two hops and difficult to approximate the entire network communities
Proposed Approach
Multi-User Personalization
Multi-User Personalized network constructed from multiple accounts
Structure and semantics of the personalized network,
Ability to explore the global community information using restricted information

9. Contributions Application: Email Perspective
User Point of View
Automatic group prediction or management
Strainer (e.g. Assist spam filtration )
Service Provider Point of View
Efficiently anticipate the global network structure using personalized information
Community Dynamics Analysis (Qualitative and Visual) in terms of network properties
Network properties: Network Density, Avg. No. of Neighbors, Clustering Coefficient, Network Centralization
How the community structure evolves as we include other user's information?