Advanced Topics in Mobile Communications (AToMIC): Social Network in Mobile Big Data (Summer 2016)
Details
Workload/ECTS Credits: | 5 ECTS |
Module: | M.Inf.223: Seminar Telematik III -or- M.Inf.224: Seminar Computernetzewerke II (old Regulations) -or- 3.10: Advanced Topics in Internet Research (II)(ITIS); M.Inf.1223 (new Regulations) |
Lecturer: | Prof. Dr. Xiaoming Fu |
Teaching assistant: | Tao Zhao, MSc., Ms. Hong Huang |
Time: | 10:15-12:00 |
Place: | SR3.101 |
UniVZ | [1] |
Course Overview
People move and stay in different locations in different time. Human mobility has a lot of impact on the social group formation and dynamics, interaction, and other activities. AToMIC course in summer semester 2016 will be focused on social networks on mobile big data. It will start with introduction to related methods and theories, together with real dataset demonstration. Students are expected to be organized in groups, running some tools on selected datasets, and present some scientific work on related topics.
Requirements
Holding at least a bachelor's degree on computer science or related fields.
Passing requirements
- Demonstration (20 ~ 25 min. presentation + 10 min. Q&A for each group)
- This accounts for 20% of your grade.
- Present practical work in groups (each group member should present your own specific work).
- Final presentation (30 ~ 35 min. presentation + 10 min. Q&A for each group).
- This accounts for 40% of your grade.
- Give a final presentation in groups (each group member should present your own specific work).
- The final presentation should contain a comprehensive survey about the selected topic and final experiment results.
- Write a report on the selected topic (12-15 pages) (LaTeX Template:[2]).
- This accounts for 40% of your grade.
- Everyone in each group writes a report on your specific work in your topic (including your own comprehensive survey on the selected topic and your own practical work).
- The Demonstration and final presentation must be given in English.
- The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).
- If your group consists of more than or less than 2 students, you can adjust your total presentation duration.
Schedule
Date | Topic | Slides |
15.04.2016 | Introduction, mobile big data; literatures | |
22.04.2016 | Big data methods (machine learning, data mining, etc) | |
29.04.2016 | Big data methods (cont.); data samples | |
06.05.2016 | Social network theory | |
13.05.2016 | Interdisciplinary methods and case study | pdfpdf |
20.05.2016 | cancelled due to business trips | |
27.05.2016 | cancelled due to business trips | |
03.06.2016 | cancelled due to business trips | |
10.06.2016 | cancelled due to business trips | |
17.06.2016 | cancelled due to business trips | |
24.06.2016 | Practical session (Demonstration) | |
01.07.2016 | cancelled | |
08.07.2016 | Final presentations | |
15.07.2016 | cancelled due to business trip |
Topics
The list of topics is as follows. The topic description shows a basic task for each topic. The literature provided here is only for reference. Each group should read more related literatures about your topic to give a comprehensive survey.
Topic | Description | Dataset | Literature |
Influential user identification (assigned to Alireza Amiri and Tayyebe Emadinia) | The project is to identify influential users based on users’ features. | Twitter [3] | [4] [5] |
Community detection (assigned to Aynur Amirfallah) | The project is to cluster different communities based on topics. | Facebook [6] | [7][8] |
Point-of-Interest recommendation | The project is to make point-of-interest(POI) recommendation based on social influence and check-ins. | Gowalla [9] | [10][11] |
Link prediction and friend recommendation | The project is to make friend recommendation based on social networks and check-ins. | Brightkite [12] | [13][14] |
Analysis of individual activity and mobile pattern (assigned to Chencheng Liang) | The project is to give a detailed analysis of individual activity and mobile pattern based on everyday life tracks. | Social Evolution Dataset [15] | [16][17] |