Archive

This week, the University of Antwerp has been hosting the ECML-PKDD conference. It is a good opportunity to hear the newest thinking in machine learning and knowledge discovery, and talk directly to researchers. The organizers have worked very hard to make the conference a success. One of their many good ideas is to have every paper be presented both as a talk and as a poster, so if you have questions that were not answered in the talk, the author can explain the work again using the poster as an aid.

On Tuesday I had the opportunity to chair the Matrix Factorization session, arguably the highest-quality research session at the conference, since out of the four papers presented one received the Best Paper in Machine Learning award, and another one the Best Student Paper in Knowledge Discovery award.

To those of us who didn’t take Linear Algebra 101, Matrix Factorization may sound imposing, but really it is a beautiful, unifying idea behind many techniques such as community discovery, document classification (e.g. into spam and non-spam emails), and collaborative filtering, which is what Amazon or Netflix does when they recommend an item for you based on your previous purchases compared to those of other customers.

In the session, Ajit Singh gave a talk on how the matrix factorization idea encompasses several methods that might not look like matrix algebra on the surface. Alexandros Karatzoglou explained several improvements on Maximum Margin Matrix Factorization, one of the hottest collaborative filtering methods around. Pauli Miettinen discussed factorizing binary matrices, which is quite a different problem from usual linear algebra methods, and Bin Cao et al.’s paper was about a new adaptive way to compute a similarity metric for collaborative filtering.

Dessert served in style at the conference banquet on Wednesday

Dr. Juha Vesanto from Xtract held a guest lecture at Helsinki University of Technology about the data analytics process and how to turn data into solutions to business problems. Other guest lectures were Jyrki Alakuijala from Google.

One interesting problem in social networks is to identify the various roles of people. Some people forward information between communities, others are the central players in communities and some are peripheral persons with only a small social network.

At Xtract, we have studied the roles in social networks for a long time. Our Alpha User concept is directly related to finding the central players in networks. During the years, also other network roles have been analyzed by us.

In the academics, there is also same interesting research being done on the topic of finding network roles. One interesting paper is Classes of complex networks defined by role-to-role connectivity profiles (pdf) by Roger Guimerà et al. They separate the roles in networks into six groups.

Another interesting paper with quite similar aims is Node Roles and Community Structure in Networks (pdf) by Jerry Scripps et al. They have given descriptive names to the different types of people found in social networks. Big fish are the persons who have many friends but belong to only one community. Ambassadors have many friends and belong to multiple communities. Bridges connect communities and loners have only a few friends and participate in just one community.

The scientific work done on finding network roles provides both inspiration and directly applicable methods that can be used to help clients understand their social neighborhoods.