CSE 316: SOCIAL NETWORK ANALYSIS INTRODUCTION. Fall 2017 Marion Neumann

Transcription

1 CSE 316: SOCIAL NETWORK ANALYSIS Fall 2017 Marion Neumann INTRODUCTION Contents in these slides may be subject to copyright. Some materials are adopted from: /kleinber/ networks-book, cs2 24w/,

2 COMPLEX SYSTEMS IT S ALL ABOUT CONNECTIONS! 2

3 COMPLEX SYSTEMS & CONNECTEDNESS social interactions communication transportation information epidemics cellular interactions globalized economy financial markets? influence flow spread functioning crash search chaos self-organization How can we represent these complex systems? 3

4 AS NETWORKS M. Neumann, R. Garnett, K. Kersting, Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels, Asian Conference on Machine Learning,

5 EXAMPLES Karate club friendships Wayne Zachary. An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33(4): ,

6 EXAMPLES communication at Hewlett Packard Lada A. Adamic and Eytan Adar. How to search a social network. Social Networks, 27(3): ,

7 EXAMPLES Links among web pages Lada Adamic and Natalie Glance. The political blogosphere and the 2004 U.S. election: Divided they blog. In Proceedings of the 3rd International Workshop on Link Discovery, pages 36 43,

8 EXAMPLES Loans among financial institutions Morton L. Bechand Enghin Atalay. The topology of the federal funds market. Technical Report 354, Federal Reserve Bank of New York, Nov

9 UNDERSTANDING COMPLEX SYSTEMS To be able to understand these complex systems we need to understand the networks behind them! How to summarize the information represented by the whole network? How to describe a single entity s role in the network? How to quantify and understand the structural features of a network? How to understand behavior influence and (information) spread? 9

10 EXAMPLES Karate club friendships à understanding conflicts Wayne Zachary. An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33(4): ,

11 EXAMPLES communication at Hewlett Packard à optimizing knowledge management use to spread new ideas / set trends Lada A. Adamic and Eytan Adar. How to search a social network. Social Networks, 27(3): ,

12 EXAMPLES Links among web pages à organizing/grouping information Liberals Conservatives Lada Adamic and Natalie Glance. The political blogosphere and the 2004 U.S. election: Divided they blog. In Proceedings of the 3rd International Workshop on Link Discovery, pages 36 43,

13 EXAMPLES Loans among financial institutions à understanding financial dependencies / crises Morton L. Bechand Enghin Atalay. The topology of the federal funds market. Technical Report 354, Federal Reserve Bank of New York, Nov

14 NETWORKS WHY SHOULD WE CARE? Networks only look complicated, but they are awesome!! Universal language for describing complex data networks from science, nature, and technology are more similar than one would expect Studying and understanding networks has a huge impact social networking, social media à better understand human interactions and their consequences drug design, economics, health and medical applications à make money, improve standard of living Massive amounts of data à computational challenge (Social) network analysis is a task for us (computer scientists)! 14

15 (SOCIAL) NETWORKS LET S ATTEMPT A DEFINITION! 15

16 WHAT IS A SOCIAL NETWORK? entities à e.g., people, groups of people relationships between entities e.g., friends can be of different types can have weights relationships are not random cluster assumption à locality kind of relationship depends on what question we want to answer Other social networks networks collaboration networks web graphs food webs! social network = graph with cluster assumption 16

17 NETWORKS Social networks created from questionnaires, interviews, direct observation collaboration networks online social network platforms online communication (twitter, , etc.), who-talks-to-whom networks Technological networks the internet (actual servers) telephone network power grids transportation networks delivery & distribution networks Information Networks the world wide web (linked webpages, blogs, Wikipedia articles) citation networks Biological networks biochemical networks, neural networks ecological networks, food webs 17

18 (SOCIAL) NETWORK ANALYSIS WHAT CAN WE DO WITH NETWORK ANALYSIS? 18

19 FRAUD DETECTION Which transaction is likely to be fraudulent? Jure Leskovec, Stanford CS224W: Social and Information Network Analysis 19

20 COMMUNITY DETECTION Targeted advertising on social network platforms friends like similar things M. Neumann, Learning with Graphs using Kernels from Propagated Information Universitäts-und Landesbibliothek Bonn 20

21 NODE CLASSIFICATION Targeted advertising on social network platforms infer missing personal information M. Neumann, Learning with Graphs using Kernels from Propagated Information Universitäts-und Landesbibliothek Bonn 21

22 LINK PREDICTION Friendship recommendations on social network platforms? M. Neumann, Learning with Graphs using Kernels from Propagated Information Universitäts-und Landesbibliothek Bonn 22

23 FINDING OPINION LEADERS Promote an idea while minimizing advertising costs M. Neumann, Learning with Graphs using Kernels from Propagated Information Universitäts-und Landesbibliothek Bonn 23

24 HOW DOES INFORMATION SPREAD? Information Cascades Can cascades be predicted? Cheng et al., WWW

25 HOW DOES INFORMATION SPREAD? Information Cascades Jure Leskovec, Stanford CS224W: Social and Information Network Analysis 25

26 IDENTIFYING ENDANGERED SPECIES Food webs: Who eats whom? 26

27 MODELING EPIDEMICS Abstract in PLoS ONE: A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network 27

28 DRUG DESIGN How similar are two molecules? M. Neumann, Learning with Graphs using Kernels from Propagated Information, Universitäts-und Landesbibliothek Bonn Find promising chemical compounds in a timeand cost-effective manner. 28

29 (SOCIAL) NETWORK ANALYSIS HOW DO WE DO NETWORK ANALYSIS? 29

30 REASONING WITH NETWORK DATA So, what are we looking for? patterns and statistical properties of networks network models and their parameters similarities between entities or entire networks How do we find these? empirical: study networks to find underlying phenomena mathematical models: understand behaviors and distinguish surprising from expected phenomena algorithms: analyze graphs and quantify the network structure and properties 30

31 SUMMARY Social network analysis can be applied to networks in general Goal: we aim to model and understand structure: who* is connected to whom*? behavior: who* is influenced by whom*? * what How: we will develop analytical tools and algorithms for networked data apply them to study network phenomena 31

32 READING [NCM] Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg, Cambridge University Press, 2010 NCM Chapter 1 Chapter