【读书笔记】Community Detection and Mining in Social Media（一）

　　该读书笔记是对一个课程的粗略总结，课程主页为 http://dmml.asu.edu/cdm/

Social Media and Social Computing

　　一些基本概念和背景我就不做记录了。仅仅抽取出一些重要的。 

• Properties of Large-Scale Networks

1. Networks in social media are typically huge, involving millions of actors and connections
2. Large-scale networks in real world demonstrate similar patterns

————————————————Scale free distribution————————————————

　　Degree distribution in large-scale networks often follows a power law.

　　

　　Power law distribution becomes a straight line if plot in a log-log scale

　　

————————————————Small world effect————————————————

　　“Six Degrees of Separation”

　　A famous experiment conducted by Travers and Milgram（1969）

　　　　Subjects were asked to send a chain letter to his acquaintance in order to reach a target person

　　　　The average path length is around 5.5

　　Verified on a planetary-scale IM network of 180 million users (Leskovec and Horvitz 2008)

　　　　The average path length is around 6.6

　　Diameter

　　　　Measures used to calibrate the small world effect

　　　　　　Diameter: the longest shortest path in a network

　　　　　　Average shortest path length

————————————————Strong Community Structure————————————————

　　　　Community: People in a group interact with each other more frequently than those outside the group

　　　　Measured by clustering coefficient:

　　　　　　density of connections among one’s friends

　　

• Challenges

Scalability

　　　　Social networks are often in a scale of millions of nodes and connections

　　　　Traditional Network Analysis often deals with at most hundreds of subjects

　　Heterogeneity

　　　　Various types of entities and interactions are involved

　　Evolution

　　　　Timeliness is emphasized in social media

　　Collective Intelligence

　　　　How to utilize wisdom of crowds in forms of tags, wikis, reviews

　　Evaluation

　　　　Lack of ground truth, and complete information due to privacy

• SocialComputingTasks

————————————————Network Modeling————————————————

　　　　Large Networks demonstrate statistical patterns

　　　　Model the network dynamics

　　　　Used for simulation to understand network properties

——————————Centrality Analysis and Influence Modeling——————————

　　　　Centrality Analysis:　　

　　　　　　Identify the most important actors or edges

　　　　　　Various criteria

　　　　Influence modeling:

　　　　　　How is the information diffused?

　　　　　　How does one influence each other?

———————————————Community Detection———————————————

　　　　Recommendation based communities
　　　　Network compression
　　　　Visualization of a huge network

————————————Classification and Recommendation———————————

　　　　Tag suggestion
　　　　Friend or group recommendation
　　　　Targetting

——————————————Privacy, Spam and Security——————————————