“Search, Show Context, Expand on Demand”: Supporting Large Graph Exploration with Degree-of-Interest

摘要: A common goal in graph visualization research is the design of novel techniques for displaying an overview entire graph. However, there are many situations where such not relevant or practical users, as analyzing global structure may be related to main task users that have semi-specific information needs. Furthermore, accessing large databases through online connection running on less powerful (mobile) hardware simply do resources needed compute these overviews. In this paper, we advocate interaction model allows remotely browse immediate context around a specific node interest. We show how Furnas' original degree interest function can adapted from trees graphs and use metric extract useful contextual subgraphs, control complexity generated direct interesting datapoints context. demonstrate effectiveness our approach with exploration dense database containing over 3 million legal citations.