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Deciding firstorder properties for sparse graphs
"... We present a lineartime algorithm for deciding firstorder logic (FOL) properties in classes of graphs with bounded expansion. Many natural classes of graphs have bounded expansion: graphs of bounded treewidth, all proper minorclosed classes of graphs, graphs of bounded degree, graphs with no sub ..."
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Cited by 29 (1 self)
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We present a lineartime algorithm for deciding firstorder logic (FOL) properties in classes of graphs with bounded expansion. Many natural classes of graphs have bounded expansion: graphs of bounded treewidth, all proper minorclosed classes of graphs, graphs of bounded degree, graphs with no subgraph isomorphic to a subdivision of a fixed graph, and graphs that can be drawn in a fixed surface in such a way that each edge crosses at most a constant number of other edges. We also develop an almost lineartime algorithm for deciding FOL properties in classes of graphs with locally bounded expansion; those include classes of graphs with locally bounded treewidth or locally excluding a minor. More generally, we design a dynamic data structure for graphs belonging to a fixed class of graphs of bounded expansion. After a lineartime initialization the data structure allows us to test an FOL property in constant time, and the data structure can be updated in constant time after addition/deletion of an edge, provided the list of possible edges to be added is known in advance and their addition results in a graph in the class. In addition, we design a dynamic data structure for testing existential properties or the existence of short paths between prescribed vertices in such classes of graphs. All our results also hold for relational structures and are based on the seminal result of Nesetril and Ossona de Mendez on the existence of low treedepth colorings.
Localized and compact datastructure for comparability graphs
, 2009
"... We show that every comparability graph of any twodimensional poset over n elements (a.k.a. permutation graph) can be preprocessed in O(n) time, if two linear extensions of the poset are given, to produce an O(n) space datastructure supporting distance queries in constant time. The datastructure i ..."
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Cited by 14 (5 self)
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We show that every comparability graph of any twodimensional poset over n elements (a.k.a. permutation graph) can be preprocessed in O(n) time, if two linear extensions of the poset are given, to produce an O(n) space datastructure supporting distance queries in constant time. The datastructure is localized and given as a distance labeling, that is each vertex receives a label of O(log n) bits so that distance queries between any two vertices are answered by inspecting their labels only. This result improves the previous scheme due to Katz, Katz and Peleg [M. Katz, N.A. Katz, D. Peleg, Distance labeling schemes for wellseparated graph classes, Discrete Applied Mathematics 145 (2005) 384–402] by a log n factor. As a byproduct, our datastructure supports allpair shortestpath queries in O(d) time for distanced pairs, and so identifies in constant time the first edge along a shortest path between any source and destination. More fundamentally, we show that this optimal space and time datastructure cannot be extended for higher dimension posets. More precisely, we prove that for comparability graphs of threedimensional posets, every distance labeling scheme requires Ω(n 1/3) bit labels.
Adjacency queries in dynamic sparse graphs
 Inf. Process. Lett
, 2007
"... We deal with the problem of maintaining a dynamic graph so that queries of the form “is there an edge between u and v? ” are processed fast. We consider graphs of bounded arboricity, i.e., graphs with no dense subgraphs, like for example planar graphs. Brodal and Fagerberg [WADS’99] described a very ..."
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Cited by 3 (0 self)
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We deal with the problem of maintaining a dynamic graph so that queries of the form “is there an edge between u and v? ” are processed fast. We consider graphs of bounded arboricity, i.e., graphs with no dense subgraphs, like for example planar graphs. Brodal and Fagerberg [WADS’99] described a very simple linearsize data structure which processes queries in constant worstcase time and performs insertions and deletions in O(1) and O(log n) amortized time, respectively. We show a complementary result that their data structure can be used to get O(log n) worstcase time for query, O(1) amortized time for insertions and O(1) worstcase time for deletions. Moreover, our analysis shows that by combining the data structure of Brodal and Fagerberg with efficient dictionaries one gets O(log log logn) worstcase time bound for queries and deletions and O(log log logn) amortized time for insertions, with size of the data structure still linear. This last result holds even for graphs of arboricity bounded by O(logk n), for some constant k.