Duen Horng Chau
June 30 7:30PM
We present SHIFTR, a system that assists users in making sense of large scale graph data. Making sense of information represented as large graphs is a fundamental challenge in many data-intensive domains. We suggest the potential of strong synergies between the data mining, cognitive psychology, and HCI communities in matching powerful graph mining tools with insights into how people learn and interact with information, and here we present SHIFTR as one such application. SHIFTR adapts the Belief Propagation algorithm to target important sensemaking tasks such as flexibly reorganizing graph entities into multiple groups based on both positive and negative examples. SHIFTR scales linearly with the graph size through its fast algorithm, novel mList data structure, and externalization of graph meta data.
We demonstrate SHIFTR’s usage and benefits through real-world sensemaking scenarios using the DBLP dataset that has almost 2 million author-publication relationships.
A demo video of SHIFTR can be downloaded at http://www.cs.cmu.edu/~dchau/shiftr/shiftr.mov.