126 research outputs found
Simpler, faster and shorter labels for distances in graphs
We consider how to assign labels to any undirected graph with n nodes such
that, given the labels of two nodes and no other information regarding the
graph, it is possible to determine the distance between the two nodes. The
challenge in such a distance labeling scheme is primarily to minimize the
maximum label lenght and secondarily to minimize the time needed to answer
distance queries (decoding). Previous schemes have offered different trade-offs
between label lengths and query time. This paper presents a simple algorithm
with shorter labels and shorter query time than any previous solution, thereby
improving the state-of-the-art with respect to both label length and query time
in one single algorithm. Our solution addresses several open problems
concerning label length and decoding time and is the first improvement of label
length for more than three decades.
More specifically, we present a distance labeling scheme with label size (log
3)/2 + o(n) (logarithms are in base 2) and O(1) decoding time. This outperforms
all existing results with respect to both size and decoding time, including
Winkler's (Combinatorica 1983) decade-old result, which uses labels of size
(log 3)n and O(n/log n) decoding time, and Gavoille et al. (SODA'01), which
uses labels of size 11n + o(n) and O(loglog n) decoding time. In addition, our
algorithm is simpler than the previous ones. In the case of integral edge
weights of size at most W, we present almost matching upper and lower bounds
for label sizes. For r-additive approximation schemes, where distances can be
off by an additive constant r, we give both upper and lower bounds. In
particular, we present an upper bound for 1-additive approximation schemes
which, in the unweighted case, has the same size (ignoring second order terms)
as an adjacency scheme: n/2. We also give results for bipartite graphs and for
exact and 1-additive distance oracles
Graph embedding in SYNCHEM2, an expert system for organic synthesis discovery
AbstractGraph embedding (subgraph isomorphism) is an NP-complete problem of great theoretical and practical importance in the sciences, especially chemistry and computer science. This paper presents positive test results for techniques to speed embedding by modeling graphs with subroutines, precalculating edge tables, turning recursion into iteration, and using search-ordering heuristics.The expert system synchem2 searches for synthesis routes of organic molecules without the online guidance of a user, and this paper examines how embedding information helps to implement the central operations of synchem2: selection, application, and evaluation of chemical reactions. The paper also outlines the architecture of synchem2, analyzes the computational time complexity of embedding and related problems in graph isomorphism and canonical chemical naming, and suggests topics and techniques for further research
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