Effective Computation of a Feedback Arc Set Using PageRank

Vasileios Geladaris, Panagiotis Lionakis, Ioannis G. Tollis
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引用次数: 0

Abstract

. Computing a minimum Feedback Arc Set (FAS) is important for visualizing directed graphs in hierarchical style. It is the first step of both known frameworks for hierarchical graph drawing of directed graphs and it is NP-hard. We present a new heuristic algorithm for computing a minimum FAS in directed graphs. The new technique produces solutions that, for graph drawing datasets, are better than the ones produced by the best previously known heuristics, often reducing the FAS size by more than 50%. The heuristic is based on computing the PageRank score of the nodes of the directed line graph of the input directed graph. Although the time required by our heuristic is heavily influenced by the size of the produced line graph, our experimental results show that it runs very fast even for very large graphs used in graph drawing. We compare results produced by our heuristic to known exact results for specific graphs used in a previous study and discuss the interesting trade-off. Finally, our experimental results on large web-graphs show that our technique found smaller FAS than it was known before for some web-graphs from a data set used in a recent study.
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基于PageRank的反馈弧集有效计算
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来源期刊
Journal of Graph Algorithms and Applications
Journal of Graph Algorithms and Applications Mathematics-Geometry and Topology
CiteScore
1.20
自引率
0.00%
发文量
28
审稿时长
50 weeks
期刊介绍: The Journal of Graph Algorithms and Applications (JGAA) is a peer-reviewed scientific journal devoted to the publication of high-quality research papers on the analysis, design, implementation, and applications of graph algorithms. JGAA is supported by distinguished advisory and editorial boards, has high scientific standards and is distributed in electronic form. JGAA is a gold open access journal that charges no author fees. Topics of interest for JGAA include but are not limited to: Design and analysis of graph algorithms: exact and approximation graph algorithms; centralized and distributed graph algorithms; static and dynamic graph algorithms; internal- and external-memory graph algorithms; sequential and parallel graph algorithms; deterministic and randomized graph algorithms. Experiences with graph and network algorithms: animations; experimentations; implementations. Applications of graph and network algorithms: biomedical informatics; computational biology; computational geometry; computer graphics; computer-aided design; computer and interconnection networks; constraint systems; databases; economic networks; graph drawing; graph embedding and layout; knowledge representation; multimedia; social networks; software engineering; telecommunication networks; user interfaces and visualization; VLSI circuits.
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