506 research outputs found
Reliable Hubs for Partially-Dynamic All-Pairs Shortest Paths in Directed Graphs
We give new partially-dynamic algorithms for the all-pairs shortest paths problem in weighted directed graphs. Most importantly, we give a new deterministic incremental algorithm for the problem that handles updates in O~(mn^(4/3) log{W}/epsilon) total time (where the edge weights are from [1,W]) and explicitly maintains a (1+epsilon)-approximate distance matrix. For a fixed epsilon>0, this is the first deterministic partially dynamic algorithm for all-pairs shortest paths in directed graphs, whose update time is o(n^2) regardless of the number of edges. Furthermore, we also show how to improve the state-of-the-art partially dynamic randomized algorithms for all-pairs shortest paths [Baswana et al. STOC\u2702, Bernstein STOC\u2713] from Monte Carlo randomized to Las Vegas randomized without increasing the running time bounds (with respect to the O~(*) notation).
Our results are obtained by giving new algorithms for the problem of dynamically maintaining hubs, that is a set of O~(n/d) vertices which hit a shortest path between each pair of vertices, provided it has hop-length Omega(d). We give new subquadratic deterministic and Las Vegas algorithms for maintenance of hubs under either edge insertions or deletions
Fully Dynamic Algorithms for Minimum Weight Cycle and Related Problems
We consider the directed minimum weight cycle problem in the fully dynamic
setting. To the best of our knowledge, so far no fully dynamic algorithms have
been designed specifically for the minimum weight cycle problem in general
digraphs. One can achieve amortized update time by simply
invoking the fully dynamic APSP algorithm of Demetrescu and Italiano [J.
ACM'04]. This bound, however, yields no improvement over the trivial
recompute-from-scratch algorithm for sparse graphs.
Our first contribution is a very simple deterministic
-approximate algorithm supporting vertex updates (i.e., changing
all edges incident to a specified vertex) in conditionally near-optimal
amortized time for digraphs with real edge
weights in . Using known techniques, the algorithm can be implemented on
planar graphs and also gives some new sublinear fully dynamic algorithms
maintaining approximate cuts and flows in planar digraphs.
Additionally, we show a Monte Carlo randomized exact fully dynamic minimum
weight cycle algorithm with worst-case update that works
for real edge weights. To this end, we generalize the exact fully dynamic APSP
data structure of Abraham et al. [SODA'17] to solve the ``multiple-pairs
shortest paths problem'', where one is interested in computing distances for
some (instead of all ) fixed source-target pairs after each update. We
show that in such a scenario, worst-case update time
is possible.Comment: Full version of an ICALP 2021 pape
Computing paths and cycles in biological interaction graphs
<p>Abstract</p> <p>Background</p> <p>Interaction graphs (signed directed graphs) provide an important qualitative modeling approach for Systems Biology. They enable the analysis of causal relationships in cellular networks and can even be useful for predicting qualitative aspects of systems dynamics. Fundamental issues in the analysis of interaction graphs are the enumeration of paths and cycles (feedback loops) and the calculation of shortest positive/negative paths. These computational problems have been discussed only to a minor extent in the context of Systems Biology and in particular the shortest signed paths problem requires algorithmic developments.</p> <p>Results</p> <p>We first review algorithms for the enumeration of paths and cycles and show that these algorithms are superior to a recently proposed enumeration approach based on elementary-modes computation. The main part of this work deals with the computation of shortest positive/negative paths, an NP-complete problem for which only very few algorithms are described in the literature. We propose extensions and several new algorithm variants for computing either exact results or approximations. Benchmarks with various concrete biological networks show that exact results can sometimes be obtained in networks with several hundred nodes. A class of even larger graphs can still be treated exactly by a new algorithm combining exhaustive and simple search strategies. For graphs, where the computation of exact solutions becomes time-consuming or infeasible, we devised an approximative algorithm with polynomial complexity. Strikingly, in realistic networks (where a comparison with exact results was possible) this algorithm delivered results that are very close or equal to the exact values. This phenomenon can probably be attributed to the particular topology of cellular signaling and regulatory networks which contain a relatively low number of negative feedback loops.</p> <p>Conclusion</p> <p>The calculation of shortest positive/negative paths and cycles in interaction graphs is an important method for network analysis in Systems Biology. This contribution draws the attention of the community to this important computational problem and provides a number of new algorithms, partially specifically tailored for biological interaction graphs. All algorithms have been implemented in the <it>CellNetAnalyzer </it>framework which can be downloaded for academic use at <url>http://www.mpi-magdeburg.mpg.de/projects/cna/cna.html</url>.</p
All-Pairs Min-Cut in Sparse Networks
Algorithms are presented for the all-pairs min-cut problem in bounded treewidth, planar, and sparse networks. The approach used is to preprocess the input n-vertex network so that afterward, the value of a min-cut between any two vertices can be efficiently computed. A tradeoff is shown between the preprocessing time and the time taken to compute min-cuts subsequently. In particular, after an Onlog Ž n. preprocessing of a bounded tree-width network, it is possible to find the value of a min-cut between any two vertices in constant time. This implies that for Ž 2 such networks the all-pairs min-cut problem can be solved in time On.. This algorithm is used in conjunction with a graph decomposition technique of Frederickson to obtain algorithms for sparse and planar networks. The running times depend upon a topological property, �, of the input network. The parameter � varies between 1 and �Ž. n; the algorithms perform well when � � on. Ž. The value Ž 2 of a min-cut can be found in time On� � log �. and all-pairs min-cut can be Ž 2 4 solved in time On � � log �. for sparse networks. The corresponding runnin
Sensitivity and Dynamic Distance Oracles via Generic Matrices and Frobenius Form
Algebraic techniques have had an important impact on graph algorithms so far.
Porting them, e.g., the matrix inverse, into the dynamic regime improved
best-known bounds for various dynamic graph problems. In this paper, we develop
new algorithms for another cornerstone algebraic primitive, the Frobenius
normal form (FNF). We apply our developments to dynamic and fault-tolerant
exact distance oracle problems on directed graphs.
For generic matrices over a finite field accompanied by an FNF, we show
(1) an efficient data structure for querying submatrices of the first
powers of , and (2) a near-optimal algorithm updating the FNF explicitly
under rank-1 updates.
By representing an unweighted digraph using a generic matrix over a
sufficiently large field (obtained by random sampling) and leveraging the
developed FNF toolbox, we obtain: (a) a conditionally optimal distance
sensitivity oracle (DSO) in the case of single-edge or single-vertex failures,
providing a partial answer to the open question of Gu and Ren [ICALP'21], (b) a
multiple-failures DSO improving upon the state of the art (vd. Brand and
Saranurak [FOCS'19]) wrt. both preprocessing and query time, (c) improved
dynamic distance oracles in the case of single-edge updates, and (d) a dynamic
distance oracle supporting vertex updates, i.e., changing all edges incident to
a single vertex, in worst-case time and distance queries in
time.Comment: To appear at FOCS 202
Fully dynamic all-pairs shortest paths with worst-case update-time revisited
We revisit the classic problem of dynamically maintaining shortest paths
between all pairs of nodes of a directed weighted graph. The allowed updates
are insertions and deletions of nodes and their incident edges. We give
worst-case guarantees on the time needed to process a single update (in
contrast to related results, the update time is not amortized over a sequence
of updates).
Our main result is a simple randomized algorithm that for any parameter
has a worst-case update time of and answers
distance queries correctly with probability , against an adaptive
online adversary if the graph contains no negative cycle. The best
deterministic algorithm is by Thorup [STOC 2005] with a worst-case update time
of and assumes non-negative weights. This is the first
improvement for this problem for more than a decade. Conceptually, our
algorithm shows that randomization along with a more direct approach can
provide better bounds.Comment: To be presented at the Symposium on Discrete Algorithms (SODA) 201
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