1. Social Networking and On-Line Communities: Classification and Research Trends Maria Ioannidou, Eugenia Raptotasiou, Ioannis Anagnostopoulos

2. Evolution of the on-line social networks research field in WWW conf. series until 2005 the papers are few (1 to 3 per year) increase of scientific interest from 2006, when online social networks are recognized as an autonomous track numbers keep rising: 28 in WWW2010 In WWW2011 out of 90 regular papers 12 contained the term “social” in their title 18 out of 89 posters were classified in “Social Systems and Graph Analysis” Frequency of papers on social networks in WWW conferences in the period 2000-2010. 2

3. Web Science Subject Categorization* E.Web Society E.2 Social Engagement and Social Science E.2.1 Social networks E.2.7 Virtual communities, groups and identity E.6 Politics and Governance E.6.2 Policy and Regulation E.6.2.2 Privacy E.6.2.3 Trust E.6.2.4 Security 3 * http://webscience.org/2010/wssc.html “The Web Science Subject Categorization (WSSC) system aims to facilitate communication and collaboration among scholars of the Web from various perspectives i.e. computational, mathematical, social, economic and legal”

4. Our categorisation Social Engagement and Social Science ◦ Social networks  Web as a Complex System  Systems, Social structures and processes  Technologies used ◦ Virtual communities, groups and identity (Personalisation / Adaptation)  Social interaction and behaviour  Information propagation  Social interest discovery, personalisation  Community structure, evolution Politics and Governance Policy and Regulation (Security, Privacy, Trust)  User anonymization  Groups and mixed user profiles  Collective privacy management  User Reputation 4

5. How we worked… an example In degree Out degree

6. Social Networks See the Web as a Complex System: ◦ Graphs Systems, Social structures and processes ◦ Network applications Technologies used ◦ Services, Games 6

7. Social Networks

8. Virtual communities, groups and identity Social interaction and behaviour -relation between a person’s social interactions and personal behavior -Positive / Negative links Information propagation -how quickly does information propagate -how widely does information propagate in the social network -what is the role of word-of-mouth exchanges between friends in the overall propagation of information in the network. Social interest discovery, personalisation Community structure, evolution -discovering and processing some of the community characteristics in order to predict their future evolution 8

9. Virtual communities, groups and identity Profile / Group based Behaviour based Collaboration based Implicit Explicit Hybrid

10. Security, Privacy, Trust User anonymization has to do with identifying security and privacy in on-line social networks and how this ensures, that the users are protected by malicious targeted attacks. Groups and mixed user profiles attacks that exploit the user groups with mixed profiles, in an effort to predict the users’ sensitive private attributes. Collective privacy management data that do not necessarily belong to the users that publish them. Privacy wizard Solutions and models that help users to describe their preferences and define their privacy settings automatically. User Reputation 10

11. 11 Security, Privacy, Trust

12. 12 Relevancy between E.2.1/E.2.7 and E.6.2

13. Graph Statistics 222 papers from which 104 and 31 concern categories E.2.1/E.2.7 and E.6.2 10% on the total papers are isolated nodes on average, each node is connected to two other nodes generally, most nodes have low in/out degree 13 there are links with papers from almost all other tracks! high efficiency in information exchanging (network’s efficiency value was 0.5)

14. Generic Conclusions Number of research efforts in social networks is still increasing Existing problems become more intense and new arise as on- line users’ numbers increase The topic of security and privacy seems to gain ground for now Almost all other web topics are affected as well as other science fields Semantic web research / semi-automatic information organisation can benefit from the study of social networks Verify relations and weights between already established categorisations / semantic networks, or even exploit new ones (by graph clustering) 14

15. Future work explore in detail network’s structure and evolution by including data from other sources / network graphs examine the graph association of a specific topic with other topics (below an example from WWW conf. series) 15 Connections of E.6.2 with other topics: black  E.6.2 grey  other

16. Thank you …!!! Questions ???

17. Specific Conclusions Social networking services fail to provide users the necessary protection and users tend to compromise their privacy neglecting necessary precautions Community structure and activities within the communities have become more and more complicated but if studied can provide valuable information about human behavior and interaction Similarity measurement among user profiles could be exploited in marketing methods and undergoing semantic web research 17