Opportunities and challenges for network analysis of social and behavioral data

Transcription

1 Technical Co-sponsor: IEEE Hong Kong Section Robotics and Automation/Control Systems Joint Chapter Jointly presents SEMINAR SERIES ON CHAOS, CONTROL AND COMPLEX NETWORKS Opportunities and challenges for network analysis of social and behavioral data Prof. Jonathan Zhu Department of English and Communication City University of Hong Kong Date and Time: Friday, 7 Sept 2007, 4:30pm 5:30pm Venue: Room CD634, Hong Kong Polytechnic University Reception starts at 4:15pm (Language: English) Abstract A significant portion of social and behavioral data involves interactions among individuals, organizations, or localities. New information and communication technologies (e.g., the Internet, mobile phones, digital broadcast, etc.) have made the collection of social network data much easier on a much larger scale at a much lower cost than what conventional methods could offer. A major bottleneck seems to lie in the analysis and the subsequent interpretation of the resulting data. Social and behavioral scientists typically employ social network analysis to the data, sometimes supplemented or substituted by neural network analysis or graph theory. However, these techniques seem to be inadequate to handle new types of social network data that are continuous, (i.e., measured at the interval scale), dynamic (i.e., evolving over time) and multilevel (i.e., across individual, institutional, and/or regional levels). In search for new solutions, social and behavioral scientists look up complexity theory as one of the sources that may offer promising theoretical frameworks and analytical tools. About the Speaker Jonathan Zhu (Ph.D., Indiana University) is a Professor and Program Leader of MA in Communication and New Media ( at City University of Hong Kong and a Cheung Kong Chair Professor of Communication at Renmin University of China. His current research focuses on the structure, content, use, and impact of the Internet and other digital media, with some of the findings published in Communications of the ACM, Communication Research, Journal of Computer-Mediated Communication, Cyberpsychology & Behavior, and elsewhere. He has also been a cofounder of the interdisciplinary collaboration initiative Chinese e-social Science (CeSS, and a past president of Chinese Communication Association. Inquiry: G. Ron Chen (gchen@ee.cityu.edu.hk) or Michael C. K. Tse (encktse@polyu.edu.hk)

2 Opportunities and Challenges for Network Analysis of Social and Behavioral Data Jonathan Zhu, Seminar Series on Chaos, Control and Complex Networks City U of Hong Kong, Poly U of Hong Kong & IEEE Hong Kong R&A/CS Joint Chapter 7 Sept

3 Approaches of Network Analysis Social Network Social- Behavioral Data Neural Network Graph Theory 2

4 Types of Social and Behavioral Data Attribute/vector data: to describe and explain the relationship between attributes (Y and X) of an i (individual, organization, etc.) Y i = f (X i ) Y X 1 X 2 Relational/network data: to describe and explain the relationship between relations (S) of i and j (individuals, organizations, etc.) S ij = f (ij) i j i -- S ji S i. j S ij -- S j. S.i S.j -- 3

5 Examples of Social Network Data Traditional data sources: Interpersonal relations Coauthorship Citation Trade Telecommunication traffic etc. New/online data sources: P2P file sharing Instant messenger Online game playing Blog tagging Hyperlink etc. 4

6 Microsoft PowerPoint Presentation Studies on China Inter-Provincial Interactions (CIPI) Hilongjiang Jilin Beijing Gansu Ningxia Shanxi Hebei Shandong Hubei Jiang su Sichuan Chengdu CSZL Anhui Zhejiang Guangxi Guangdong Fujian Guangzhou 5

7 Data for CIPI Studies 1. Population migration across provinces, 2000 ( flow of human capitals ) 2. News coverage across provinces, ( flow of information ) 3. Hyperlinks to IP hosts across provinces, 2005 ( web link structure ) 4. Webpage references across provinces, 2005 ( web content structure ) 5. Audience migration across provincial satellite TV channels, 2007 ( flow of audience ) 6

8 Characteristics of New/Online Social Network Data Continuous measurement: the strength of the edges/links is bi-directional and valued, as opposed to unidirectional and/or binary relations; Dynamic process: the network evolves over time and thus results in motion frames of an ongoing process, as opposed to static landscape; Multi-level structure: the nodes are nested within larger nodes which may be nested within further larger nodes, as opposed to nodes operating at a unilevel 7

9 Continuous vs. Binary Measurement Binary measurement is a special and highly simplified version of the complex reality whereas continuous measurement is closer to the reality. Existing social network analysis techniques are well developed for binary data but inadequate when applied to continuous data, often resulting in: the dominance of a few giant component(s) if the data are treated as continuous measures. the arbitrary decision on a threshold if the data are recoded into binary measures. 8

10 Bow-tie and Daisy Models of the Web Graph Bow-tie Model of Weblinks (Broder et al. 2000) Daisy Model of Weblinks (Donato et al. 2005) 9

11 The World as a Single Group Data from show that the telecommunications network was composed of a single group of 81 countries. G. A. Barnett (2001). The American Behavioral Scientist, Vol. 44, No. 10, p

12 QAP Correlations from CIPI Studies News stories Hyperlinks Population 0.04 flow 0.34 Web references 0.49 * QAP: Quadratic Assignment Procedure 11

13 News Network vs. Web Network (r = 0.42) News Web 12

14 Dynamic vs. Static Structure Common approaches to longitudinal network data: Reduce the time points of the data to a single point by summary statistics (e.g., mean scores), which often hide instantaneous changes or differences and thus prevent detection of causes and effects. Maintain a limited number of time points for simple (e.g., pairwise) comparisons over time, which throws away information and may also involve similar arbitrariness as in the case of transforming continuous data to binary data 13

15 Treat Dynamic Data in Traditional Way As the world moves into the information age, the international telecommunications network has become denser, more centralized, and more highly integrated. -- G. A. Barnett (2001). The American Behavioral Scientist, Vol. 44, No. 10, p

16 QAP Correlations among Audience Flows across 31 TV Channels :00pm :00pm Measured on :00pm

17 Audience Flows in Multi- Dimensional Scaling Overall (6-10pm) 6pm 8pm 10pm 16

18 Multilevel vs. Unilevel Analysis All existing social network analysis tools deal with interactions at a single level whereas interactions in the real world often take places across levels such as: the private space: individuals households communities countries the public space: individuals organizations industries countries Unilevel analysis of multilevel data throws away information and also runs the risk of choosing a wrong level of analysis 17

19 Multilevel Structure of Web Graph Which of the above is the most appropriate level of anlaysis for web topology? Web pages (e.g., newmedia.cityu.edu.hk/index.htm) Host machines (newmedia.cityu.edu.hk) Domain names (cityu.edu.hk) Top-level domains (*.edu) Country-level domains (*.hk) The existing research on web topology mostly focuses on links among web pages whereas host or domain should be more revealing 18

20 Degree of Difficulty in Social Network Data Analysis Multilevel Unilevel Dynamic Static Dynamic Static Continuous Binary = the easiest; 8 = the most difficult 19

21 Final Remarks The issues for continuous, dynamic and multilevel networks have existed for long, but become salient only when relevant data become readily available at little or no cost. We need not merely statistical (and visualization) tools but also (or more) theoretical models and analytical frameworks to conceptualize the fundamental processes that generate such data. Interdisciplinary collaboration among computer scientists, physicists, mathematicians and social scientists is perhaps the only way to find the solutions 20