|Grube, Tim; Volk, Florian; Mühlhäuser, Max; Bhairav, Suhas; Sachidananda, Vinay; Elovici, Yuval
|Human exploration of large graph structures becomes increasingly difficult with growing graph sizes. A visual representation of such large graphs, for example, social networks and citational networks, has to find a trade-off between showing details in a magnified view and the verall graph structure. Displaying these both aspects at the same time results in an overloaded visualization that is inaccessible for human users. In this paper, we present a new approach to address this issue by combining and extending graph-theoretic properties with community detection algorithms. Our approach is semi-automated and non-destructive. The aim is to retain core properties of the graph while--at the same time--hiding less important side information from the human user. We analyze the results yielded by applying our approach to large real-world network data sets, revealing a massive reduction of displayed nodes and links.
|10th International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services, p.24-31