Home // CENTRIC 2017, The Tenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services // View article

Complexity Reduction in Graphs: A User Centric Approach to Graph Exploration

Authors:
Tim Grube
Florian Volk
Max Mühlhäuser
Suhas Bhairav
Vinay Sachidananda
Yuval Elovici

Keywords: Complexity reduction; graph visualisation; big data exploration; graph metrics; community detection

Abstract:
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.

Pages: 24 to 31

Copyright: Copyright (c) IARIA, 2017

Publication date: October 8, 2017

Published in: conference

ISSN: 2308-3492

ISBN: 978-1-61208-592-0

Location: Athens, Greece

Dates: from October 8, 2017 to October 12, 2017