COMMPER 2011 - Workshop on Community Data Mining and People Recommenders

Data mining and knowledge discovery in social networks has advanced significantly over the past several years, due to the availability of a large variety of online and online social network systems. The focus of COMMPER is on two main streams of social networks: community mining and system recommenders.

The first focus of this workshop is on mining communities in social networks and in particular in scientific collaboration networks. Consider, for example, a dataset of scientific publications along with information about each publication and the complete citation network. Many data-analysis questions arise: what are the underlying communities, who are the most influential authors, what are the set-skills of individual authors, what are the observed collaboration patterns, how does interest on popular topics propagates, who does the network evolve in terms of collaborations, topics, citations, and so on. In this workshop we indent to bring domain experts, such as bibliometricians, closer to researchers from the fields of data mining and social networks. The expected outcome is to strengthen the collaboration of these communities aiming at high impact-research contributions and discussions. We aspire that the workshop will lead to the development of new insights and data mining methodologies that could be employed for the analysis of communities, models of human collaboration, topic discovery, evolution of social networks, and more.

People recommenders, the second main topic of this workshop, deal with the problem of finding meaningful relationships among people or organisations. In online social networks, relationships can be friends on Facebook, professional contacts on LinkedIn, dates on an online dating site, jobs or workers on employment websites, or people to follow on Twitter. The nature of these domains makes people-to-people recommender systems to be significantly different from traditional item-to-people recommenders. One basic difference in the people recommender domain is the benefit or requirement of reciprocal relationships. Another difference between these domains is that people recommenders are likely to have rich user profiles available. The goal of this workshop is to build a community around people recommenders and instigate discussion about this emerging area of research for recommender systems. With this workshop, we want to reach out to research done in both academia and industry.

Topics

We encourage that papers submitted to COMMPER focus on, but are not limited to the following topics:
analysis of scientific communities;
collaboration networks;
bibliometrics and data mining;
analysis of co-authorship networks;
analysis of citation networks;
communities in social networks;
dynamic networks;
formation of teams;
learning skills of individuals;
topic and community evolution and dynamics;
comparative studies of community networks;
people recommendation in social networks;
community recommendations in social networks;
mentor/mentee recommendations in tutoring systems;
expert search and expertise recommendation;
employee/employer recommendations;
online dating recommendations;
people search in the enterprise;
team recommendations;
reviewer assignment;
location-aware people recommendation.