1. Building and Analyzing Social Networks Case Studies of Semantic Social Network Analysis Dr. Bhavani Thuraisingham February 22, 2013

2. 23-2 9/6/2015 03:25 Outline 0 Reference: P. Mika, Semantic Web and Social Networks, Springer, 2008: Chapter 7, 8, 9, 10 0 Evaluation of Web-based Social Network Extraction 0 Semantic-based Social Network Analysis in the Sciences 0 Ontologies in Folksonomy Systems 0 How have Semantic Social Networks benefitted communities

3. 23-3 9/6/2015 03:25 Evaluation of Web-based Social Network Extraction: Chapter 7 0 Survey methods and electronic data extraction 0 Empirical Study 0 Data Collection 0 Preparing the data 0 Optimizing goodness of fit 0 Comparison across methods and networks 0 Predicting the goodness of fit 0 Evaluation through analysis

4. 23-4 9/6/2015 03:25 Differences between survey methods and electronic data extraction 0 Differences in what is measured - Challenge is to extract data from the web that reflects the real world 0 Errors introduced by the extraction methods - Homonyms 0 Errors introduced by the survey data collection - Impossible to get all of the data for analysis

5. 23-5 9/6/2015 03:25 Context of the Empirical Study 0 Collected network data of 123 researchers in Vrije University 0 Human subject approval is needed 0 Department organization 0 Different from the semantic web community which has common research interest

6. 23-6 9/6/2015 03:25 Data Collection 0 Collected personal and social information from online surveys 0 Multiple page survey 0 Questions such as - Who do you know from the list of names? - Who are similar to you? 0 The results of the survey was represented in RDF and Sesame was used to manage the database

7. 23-7 9/6/2015 03:25 Preparing the Data 0 Remove all non respondents; only 79 responded 0 Build the network: nodes and edges - Advice seeking, Advice giving, Friendships, Troubled relationships, Similarity, etc. - Nodes after non respondents removed, Nodes with edges, Edges, Edges after non respondents removed, etc. 0 Handle incomplete and inconsistent data

8. 23-8 9/6/2015 03:25 Optimizing goodness of fit 0 Need to prune the network - Minimal number of pages one must have on the web - Minimal number of relationships - PhD students have less than1000 pages while professor may have over 10000 pages - What is the appropriate parameter for filtering? - What is the similarity between the survey network and the extracted network 0 Extract relationships - Information retrieval task - Precision and Recall

9. 23-9 9/6/2015 03:25 Comparison across methods and networks 0 Use more than one method for analysis 0 Select parameters for each method separately 0 Methods selected by the author are: - Co-occurrence analysis - Average precision 0 Determine which method produces better precision and recall 0 Data mining techniques such as different association rule mining methods can also be used

10. 23-10 9/6/2015 03:25 Predicting the goodness of fit 0 Challenge is to determine the closeness a person’s real world network and his/her online network 0 Need to measure the similarity between the personal network and the survey network 0 Attributes considered include member of relations mentioned, age of the individual, number of years spent at the university, etc. 0 Some observations - More respondents are mentioned by someone the higher the precision of extraction - Survey attributes did not impact the result

11. 23-11 9/6/2015 03:25 Evaluation through analysis 0 Networks from surveys or web are used as raw data to carry out complex data analysis 0 Author has concluded that 100% match is not required for obtaining relevant results 0 Most network measures are statistical aggregates 0 Robust to missing or incorrect information

12. 23-12 9/6/2015 03:25 Semantic-based Social Network Extraction: Chapter 8 0 Context 0 Methodology - Data acquisition - Representation, storage and reasoning - Visualization and analysis 0 Results - Descriptive analysis - Structural and cognitive effects of scientific performance

13. 23-13 9/6/2015 03:25 Context 0 Community of Researchers working on semantic web 0 Community defined using the ISWC conference authors 0 Objective - Study the community, the contributions they are making, the interactions between them so that semantic web research is enhanced

14. 23-14 9/6/2015 03:25 Methodology 0 Combine existing methods of web mining ands extraction from publications and emails with semantic web-based data storage, aggregation and reasoning with social network data 0 Flank supports data collection, storage and visualization of social networks 0 Methodology consist of - Data Acquisition - Data Representation, Storage and Reasoning - Visualization and Analysis

15. 23-15 9/6/2015 03:25 Methodology 0 Data Acquisition - Four types of knowledge sources: HTML pages., FOAF profiles, public emails, and bibliographical data - Web mining component of Flink extracts social networks from the data; Calculate strength of the individuals; Associate individuals with domain concepts 0 Representation., Storage and Reasoning - Data in RDF format - Reasoning with ontologies; Ontology matching 0 Visualization and Analysis - Browse the social network through the web interface - Compute statistics

16. 23-16 9/6/2015 03:25 Results 0 Descriptive Analysis - Who are the major players in semantic web research - Central figures:, Ian Horrocks., Frank van Harmelen, Deborah Mcguiness, Jim Hendler 0 Structural and cognitive effects of scientific performance - Discussions on the structure of the network on the scientific performance - Structural and cognitive effects of scientific performance

17. 23-17 9/6/2015 03:25 Results 0 Descriptive Analysis - Who are the major players in semantic web research - Central figures:, Ian Horrocks., Frank van Harmelen, Deborah McGuiness, Jim Hendler 0 Structural and cognitive effects of scientific performance - Discussions on the structure of the network on the scientific performance - Dense interconnected networks vs. Sparse networks = Dense networks maybe mean closer ties = Sparse networks may mean diversity

18. 23-18 9/6/2015 03:25 Ontologies in Folksonomy Systems: Chapter 9 0 A folksonomy is a system of classification derived from the practice and method of collaboratively creating and managing tags to annotate and categorize content; this practice is also known as collaborative tagging, social classification, social indexing, and social tagging 0 Topics covered -Tripartite model of ontologies -Case Studies -Evaluation

19. 23-19 9/6/2015 03:25 Tripartite model of ontologies 0 Folksonomy allows users to describe a set shared objects with a set of keywords 0 Networks of folksonomies are modeled as a tripartite graph with hyper edges 0 In a social tagging system users tag objects with concepts creating as ternary association between the user, concepts and the object 0 Ultimately the tagging system is represented by a collection of ontologies

20. 23-20 9/6/2015 03:25 Case Studies 0 Otology emergence in del.icio.is -Del.icio.us is a social book marking tool -Users manage personal collections of links to web sites and describe those links -Ontologies are used to represent the bookmarks, and descriptions 0 Community based ontology extraction from web pages -Actor-concept-instance ontology -Web pages of a person and the topic of interest -Flink is used to represent and reason about the ontologies

21. 23-21 9/6/2015 03:25 Evaluation 0 How do you evaluate the results from constructing the ontologies and reasoning about the ontologies? 0 Which ontologies are better? 0 Need to consult the community to validate the results -Emailed the set of researchers and asked them to answer the questions -Not all of them responded -Apply methods discussed in Chapter 7

22. 23-22 9/6/2015 03:25 How have Semantic Social Networks benefitted communities: Chapter 10 0 Katrina PeopeFinder 0 A Second Life

23. 23-23 9/6/2015 03:25 Katrina PeopleFinder 0 Katrina, one of the worst hurricanes in US History 0 Thousands of people were displaced 0 Through the semantic social network Katrina PeopleFinder a network of the people was constructed and the associations determined 0 The results were used to connect relatives and friends

24. 23-24 9/6/2015 03:25 Second Life 0 Second Life is an online virtual world developed by Linden Lab. It was launched on June 23, 2003. A number of free client programs, or Viewers, enable Second Life users, to interact with each other through avatars (Also called Residents). Residents can explore the world (known as the grid), meet other residents, socialize, participate in individual and group activities, and create and trade virtual property and services with one another. Second Life is intended for people aged 16 and over. 0 Built into the software is a three-dimensional modeling tool based on simple geometric shapes that allows residents to build virtual objects. There is also a procedural scripting language, Linden Scripting Language, which can be used to add interactivity to objects. Sculpted prims (sculpties), mesh, textures for clothing or other objects, animations, and gestures can be created using external software and imported. The Second Life Terms of Service provide that users retain copyright for any content they create, and the server and client provide simple digital rights management functions.