Advanced Topics in Mobile Communications (AToMIC): Social Network in Mobile Big Data (Summer 2016): Difference between revisions
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|[http://delivery.acm.org/10.1145/2530000/2525357/p364-wang.pdf?ip=134.76.38.65&id=2525357&acc=ACTIVE%20SERVICE&key=2BA2C432AB83DA15%2E8C14E74AF280C121%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=608291892&CFTOKEN=67082599&__acm__=1461925898_213e912ffab3caa09300913b074bd7b0][http://delivery.acm.org/10.1145/2490000/2484030/p363-yuan.pdf?ip=134.76.38.65&id=2484030&acc=ACTIVE%20SERVICE&key=2BA2C432AB83DA15%2E8C14E74AF280C121%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=608291892&CFTOKEN=67082599&__acm__=1461925876_930b6ea9af138edd6b005c438ccafaae] | |[http://delivery.acm.org/10.1145/2530000/2525357/p364-wang.pdf?ip=134.76.38.65&id=2525357&acc=ACTIVE%20SERVICE&key=2BA2C432AB83DA15%2E8C14E74AF280C121%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=608291892&CFTOKEN=67082599&__acm__=1461925898_213e912ffab3caa09300913b074bd7b0][http://delivery.acm.org/10.1145/2490000/2484030/p363-yuan.pdf?ip=134.76.38.65&id=2484030&acc=ACTIVE%20SERVICE&key=2BA2C432AB83DA15%2E8C14E74AF280C121%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=608291892&CFTOKEN=67082599&__acm__=1461925876_930b6ea9af138edd6b005c438ccafaae] | ||
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|Link prediction and friend recommendation | |Link prediction and friend recommendation (assigned) | ||
|The project is to make friend recommendation based on social networks and check-ins. | |The project is to make friend recommendation based on social networks and check-ins. | ||
|Brightkite [http://snap.stanford.edu/data/loc-brightkite.html] | |Brightkite [http://snap.stanford.edu/data/loc-brightkite.html] |
Revision as of 20:01, 2 May 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.
Presentation Schedule
TBD
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 | |
20.05.2016 | cancelled due to business trips | |
27.05.2016 | cancelled due to business trips | |
03.06.2016 | Mining opinion leaders and structure hole spanners | |
10.06.2016 | cancelled due to business trips | |
17.06.2016 | cancelled due to business trips | |
25.06.2016 | Practical session | |
01.07.2016 | Final presentations (1) | |
08.07.2016 | Final presentations (2) | |
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) | The project is to identify influential users based on users’ features. | Twitter [2] | [3] [4] |
Community detection (assigned) | The project is to cluster different communities based on topics. | Facebook [5] | [6][7] |
Point-of-Interest recommendation | The project is to make point-of-interest(POI) recommendation based on social influence and check-ins. | Gowalla [8] | [9][10] |
Link prediction and friend recommendation (assigned) | The project is to make friend recommendation based on social networks and check-ins. | Brightkite [11] | [12][13] |
Analysis of individual activity and mobile pattern | The project is to give a detailed analysis of individual activity and mobile pattern based on everyday life tracks. | Social Evolution Dataset [14] | [15][16] |