INFORMATION ON CS/SE 6301 (Spring'2018)
Introduction to Social Computing
4:00-6:45pm Thur, FO1.202

Office Hours: Friday, 3:45-5:00 pm
in Room ECSS 3-611.

Teaching Assistants: Jing Yuan

Office Hours: 9:00-11:00am Friday
Room: ECSS 2.104A
email: [email protected]

Textbook

No textbook
Please read ppt and cited references

Lectures

Syllabus .

Unit 1 Introduction

1.1 What is the Social Network?
1.2 Heuristic and Approximation

Unit 2 Influence

2.1 Influence Maximization
2.2 Independent Cascade and Linear Threshold
2.3 BKS-Conjecture
2.4 General Threshold and Cascade
2.5 KKT-Conjecture
2.6 Radomness of Influence: Complexity
2.7 Radomness of Influence: Algorithm

Unit 3 Maximization in Social Computing

3.1 Submodular Optimization in Social Computing
3.2 Linear Maximization
3.3 Monotone Submodular Maximization
3.4 Nonmonotone Submodular Maximization
3.5 Monotone Nonsubmodular maximization
3.6 Muiti-factor Influence Maximization

Unit 4 Minimization in Social Computing

4.1 Submodular Set Cover
4.2 Submodular Objective

Unit 5 Rumor

4.1 Community-based Rumor Blocking
4.2 NonCooperative Rumor Blocking
4.3 Randomized Rumor Blocking
4.4 Source Detection: Centrality
4.5 Source Detection: Multiple Observation
4.6 Monitor Placement
4.7 Group Testing Approach

Unit 6 Community Detection

5.1 Community Detection
5.2 Modularity Maximization
5.3 Influence-based Detection

Unit 6 Power Law Graphs

6.1 Madularity Maximizatiom
6.2 Positive-Influnce Target-Dominating
6.2 (detail proof)

Unit 6 Positive Influence

5.1 Seeds for Positive-Influence
5.2 Target Influence
5.3 Partial Influence
5.4 Weighted Seeds

Unit 7 Marketing (This unit will be organized later.)
7.1. Competive Influence
7.2. Community Expansion

Chapter 11 Other Topics

11.1 Influence and Profit
11.2 Seed Selection for Certain Profit 10.1 Max Influence Path
10.2 Active Friending
10.3 Metric Approximation


Homeworks, Examinations and Grade

There are 2-3 homework and 2 take-home exam.