Research Meeting Seungseok Kang Center for E-Business Technology Seoul National University Seoul, Korea

Copyright  2008 by CEBT Introduction  Device-Centric Social Network Model on Mobile Environment Social Network Model – Sociometric data modeling – Graph representation – Centrality and centralization – Correspondence analysis Issues on Mobile Environment – Various types of devices – High mobility of users – Power consuming and caching – Device clustering Service Types – Collaborative filtering with user’s association regarding the issues of mobile environment

Copyright  2008 by CEBT Social Network Model  Sociometric data modeling Components, cores, and cliques Networks and relations Positions, roles, and clusters Blockmodeling (1992~) – Knowing the structure of a network – Facilitating the understanding of network phenomena  Graph representation Sociogram (1932~) – A classic approach to display sociometric data – directed graph with nodes, edges, and adjacency matrix Random Graph Model – For multiple network of relations with dependencies

Copyright  2008 by CEBT Social Network Model (cont’d)  Centrality and centralization Local and global centrality of social network – Degree-based measure – Closeness and betweenness Group centrality (1999~) – Normalized group degree Adjacency-based measurement – Two-mode Centrality Regarding different kind of data  Actors and events / binary relation (membership or participation) – Core-periphery measures Calculating coreness by using core-periphery structure  Correspondence analysis Researches on affiliation network – The decomposition of a matrix into its basic structure

Copyright  2008 by CEBT Issues on Mobile Environment  Initial characteristics of social network on mobile environment Small-world phenomenon – The average length of the shortest path between devices may be different  Various types of devices graph representation scheme Understanding the characteristics of devices which is able to be distinguished from entities of traditional social network model  High mobility of users Density calculation – Dynamic change of device’s density Decentralized problem – Devices may be distributed more sparsely than people – May need to adapt extended centralization scheme  Power consuming and caching Similar to caching issues in sensor network Ganesh Santhanakrishnan et al., “towards Universal Mobile Caching”, MobiDE’05  Device clustering  Context-awareness Expanding existing representation scheme with context

Copyright  2008 by CEBT Service Types  What will we do within device-centric social network? Context-aware services – Context caching – Context abstraction – Dynamic device clustering with context information Collaborative Filtering – Regarding the association or correspondence between devices

Copyright  2008 by CEBT Papers  WWW Conference referred track – social networks & Web 2.0 Analysis of Social Networks & Online Interaction – Parag Singla et al., “Yes, There is a Correlation – From Social Networks to Personal Behavior on the Web”, WWW 2008 – Vicenc Gomez et al., “Statistical Analysis of the Social Network and Discussion Threads in Slashdot”, WWW 2008 – Lada A. Adamic et al., “Knowledge Sharing and Yahoo Answers: Everyone Knows Something”, WWW 2008 Discovery and Evolution of Communities – Xin Li et al, “Tag-based Social Interest Discovery”, WWW 2007 – Jure Leskovec et al., “Statistical Properties of Community Structure in Large Social and Information Networks”, WWW 2008  Others Jon Kleinberg, “The Convergence of Social and Technological Networks”, ACM Communications, 2008 Souvik Debnath, “Festure Weighting in Content Based Recommendation System using Social Network Analysis”, WWW 2008 poster paper Frank Edward Walter et al, “A model of a trust-based recommendation system on a social network”, Auton Agent Muti-Agent System, Springer, 2008

Copyright  2008 by CEBT Issues  Huge gap between the researches and practical services Group Centrality, Blockmodeling, Sociogram, … vs Cyworld.com, MySpace.com, Facebook.com, … In most cases, online social network service have not depended on theoretical algorithms or social network model – They build and use their own social network according to their service types and business process – Their social network model may be able to be analyzed with traditional researches, but there are still such a big gap between theoretical results and practical services – What should we focus on? Analyzing the current online social network based on traditional approaches and extending the algorithms within the existing online social network service model such as Facebook.com Suggesting a new theory (or framework, algorithms, whatever) by analyzing the problems or limitation of previous theoretical research issues ……