Juan J. Cameron

Bio: Juan J. Cameron is an academic researcher from University of Manitoba. The author has contributed to research in topics: Social computing & Data structure. The author has an hindex of 8, co-authored 10 publications receiving 241 citations.

Interactive discovery of influential friends from social networks

Abstract: Social networks, which are made of social entities (eg, individual users) linked by some specific types of interdependencies such as friendship, have become popular to facilitate collaboration and knowledge sharing among users Such interactions or interdependencies can be dependent on or influenced by user characteristics such as connectivity, centrality, weight, importance, and activity in the networks As such, some users in the social networks can be considered as highly influential to others In this article, we propose a computational model that integrates data mining with social computing to help users discover influential friends from a specific portion of the social networks that they are interested in Moreover, our social network analysis and mining model also allows users to interactively change their mining parameters (eg, scopes of their interested portions of the social networks)

Interactive Mining of Strong Friends from Social Networks and Its Applications in E-Commerce

Abstract: Social networks are generally made of individuals who are linked by some types of interdependencies such as friendship. Most individuals in social networks have many linkages in terms of friends, connections, and/or followers. Among these linkages, some of them are stronger than others. For instance, some friends may be acquaintances of an individual, whereas others may be friends who care about him or her (e.g., who frequently post on his or her wall). In this study, we integrate data mining with social computing to form a social network mining algorithm, which helps the individual distinguish these strong friends from a large number of friends in a specific portion of the social networks in which he or she is interested. Moreover, our mining algorithm allows the individual to interactively change his or her mining parameters. Furthermore, we discuss applications of our social mining algorithm to organizational computing and e-commerce

Finding Strong Groups of Friends among Friends in Social Networks

Abstract: Over the past few years, the rapid growth and the exponential use of social digital media has led to an increase in popularity of social networks and the emergence of social computing. In general, social networks are structures made of social entities (e.g., individuals) that are linked by some specific types of interdependency such as friendship. Most users of social media (e.g., Face book, Google+, Linked In, My Space, Twitter) have many linkages in terms of friends, connections, and/or followers. Among all these linkages, some of them are more important than another. For instance, some friends of a user may be casual ones who acquaintances met him at some points in time, whereas some others may be friends that care about him in such a way that they frequently post on his wall, view his updated profile, send him messages, invite him for events, and/or follow his tweets. In this paper, we apply data mining techniques to social networks to help users of the social digital media to distinguish these important friends from a large number of friends in their social networks.

DIFSoN: Discovering influential friends from social networks

Abstract: Social networks, which are made of social entities (e.g., individual users) linked by some specific types of interdependencies such as friendship, have become popular to facilitate collaboration and knowledge sharing among users. Such interactions or interdependencies can be dependent on or influenced by user characteristics such as connectivity, centrality, weight, importance, and activity in the networks. As such, some users in the social networks can be considered as highly influential to others. In this paper, we propose a computational model that integrates data mining with social computing to help users to discover influential friends from the social networks.

Models and methods in social network analysis

Abstract: Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust’s Social Network Analysis: Methods and Applications, it is a collection of original articles by leading methodologists reviewing recent advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodeling, the analysis of diffusion through networks, the analysis of affiliation or “two-mode” networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphic techniques for exploring network data, and software for the analysis of social networks.

Message from the general chairs and program committee chairs

Abstract: This book constitutes the refereed proceedings of 4 international workshops held in conjunction with the 14th Asia-Pacific Web Conference, APWeb 2012, in Kunming, China, in April 2012 (see LNCS 7235). The 29 revised full papers presented were carefully reviewed and selected for presentation at the following 4 workshops: the 1st workshop on sensor networks and data engineering (SenDe 2012), the 1st international workshop on intelligent data processing (IDP 2012), the 1st international workshop on information extraction and knowledge base building (IEKB 2012), and the 3rd international workshop on mobile business collaboration (MBC 2012).