Graph Isomorphism, Color Refinement, and Compactness
Graph Isomorphism, Color Refinement, and Compactness
复制标题
图同构、颜色细化和紧致性
DOI:
10.1007/s00037-016-0147-6
复制
发表时间:
2017
影响因子:
1.4
通讯作者:
O. Verbitsky
中科院分区:
文献类型:
--
作者:
V. Arvind;J. Köbler;G. Rattan;O. Verbitsky
Color refinementis a classical technique used to show that two given graphsGandHare non-isomorphic; it is very efficient, although it does not succeed on all graphs. We call a graphGamenableto color refinement if the color refinement procedure succeeds in distinguishingGfrom any non-isomorphic graphH. Babai et al. (SIAM J Comput 9(3):628–635, 1980) have shown that random graphs are amenable with high probability. We determine the exact range of applicability of color refinement by showing that amenable graphs are recognizable in time, wherenandmdenote the number of vertices and the number of edges in the input graph.We use our characterization of amenable graphs to analyze the approach to Graph Isomorphism based on the notion ofcompact graphs. A graph is called compact if the polytope of its fractional automorphisms is integral. Tinhofer (Discrete Appl Math 30(2–3):253–264, 1991) noted that isomorphism testing for compact graphs can be done quite efficiently by linear programming. However, the problem of characterizing compact graphs and recognizing them in polynomial time remains an open question. Our results in this direction are summarized below:○We show that all amenable graphs are compact. In other words, the applicability range for Tinhofer’s linear programming approach to isomorphism testing is at least as large as for the combinatorial approach based on color refinement.○Exploring the relationship between color refinement and compactness further, we study related combinatorial and algebraic graph properties introduced by Tinhofer and Godsil. We show that the corresponding classes of graphs form a hierarchy, and we prove that recognizing each of these graph classes isP-hard. In particular, this gives a first complexity lower bound for recognizing compact graphs.
登录
查看更多内容
影响因子:
0.8
作者:
R. Tyshkevich
通讯作者:
R. Tyshkevich
影响因子:
1.1
作者:
H. Schreck;G. Tinhofer
通讯作者:
G. Tinhofer
DOI:
10.1145/1008354.1008356
发表时间:
1977
期刊:
SIGACT News
影响因子:
--
作者:
L. Goldschlager
通讯作者:
L. Goldschlager
DOI:
10.1007/s10114-004-0485-1
发表时间:
2005
期刊:
Acta Mathematica Sinica
影响因子:
--
作者:
Ping Wang;Jiongsheng Li
通讯作者:
Jiongsheng Li
DOI:
--
发表时间:
--
期刊:
影响因子:
--
作者:
A. Borri;T. Calamoneri;R. Petreschi;S. Das;R. Uehara;A. Borri;T. Calamoneri;R. Petreschi
通讯作者:
R. Petreschi