Graph Aggregation: Application to Social Networks

Abstract : In the enterprise context, people need to exploit and mainly visualize different types of interactions between heterogeneous objects. Graph model seems to be the most appropriate way to represent those interactions. However, the extracted graphs have in general a huge size which makes it difficult to analyze and visualize. An aggregation step is needed to have more understandable graphs in order to allow users discovering underlying information and hidden relationships between entities. In this work, we propose new measures to evaluate the quality of summaries based on an existing algorithm named k-SNAP that produces a summarized graph according to user-selected node attributes and relationships.