10,095 research outputs found

    An Efficient Algorithm for Enumerating Chordless Cycles and Chordless Paths

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    A chordless cycle (induced cycle) CC of a graph is a cycle without any chord, meaning that there is no edge outside the cycle connecting two vertices of the cycle. A chordless path is defined similarly. In this paper, we consider the problems of enumerating chordless cycles/paths of a given graph G=(V,E),G=(V,E), and propose algorithms taking O(E)O(|E|) time for each chordless cycle/path. In the existing studies, the problems had not been deeply studied in the theoretical computer science area, and no output polynomial time algorithm has been proposed. Our experiments showed that the computation time of our algorithms is constant per chordless cycle/path for non-dense random graphs and real-world graphs. They also show that the number of chordless cycles is much smaller than the number of cycles. We applied the algorithm to prediction of NMR (Nuclear Magnetic Resonance) spectra, and increased the accuracy of the prediction

    Interpreting and using CPDAGs with background knowledge

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    We develop terminology and methods for working with maximally oriented partially directed acyclic graphs (maximal PDAGs). Maximal PDAGs arise from imposing restrictions on a Markov equivalence class of directed acyclic graphs, or equivalently on its graphical representation as a completed partially directed acyclic graph (CPDAG), for example when adding background knowledge about certain edge orientations. Although maximal PDAGs often arise in practice, causal methods have been mostly developed for CPDAGs. In this paper, we extend such methodology to maximal PDAGs. In particular, we develop methodology to read off possible ancestral relationships, we introduce a graphical criterion for covariate adjustment to estimate total causal effects, and we adapt the IDA and joint-IDA frameworks to estimate multi-sets of possible causal effects. We also present a simulation study that illustrates the gain in identifiability of total causal effects as the background knowledge increases. All methods are implemented in the R package pcalg.Comment: 17 pages, 6 figures, UAI 201

    Linear Time Subgraph Counting, Graph Degeneracy, and the Chasm at Size Six

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    We consider the problem of counting all k-vertex subgraphs in an input graph, for any constant k. This problem (denoted SUB-CNT_k) has been studied extensively in both theory and practice. In a classic result, Chiba and Nishizeki (SICOMP 85) gave linear time algorithms for clique and 4-cycle counting for bounded degeneracy graphs. This is a rich class of sparse graphs that contains, for example, all minor-free families and preferential attachment graphs. The techniques from this result have inspired a number of recent practical algorithms for SUB-CNT_k. Towards a better understanding of the limits of these techniques, we ask: for what values of k can SUB_CNT_k be solved in linear time? We discover a chasm at k=6. Specifically, we prove that for k < 6, SUB_CNT_k can be solved in linear time. Assuming a standard conjecture in fine-grained complexity, we prove that for all k ? 6, SUB-CNT_k cannot be solved even in near-linear time

    Extended h-Index Parameterized Data Structures for Computing Dynamic Subgraph Statistics

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    We present techniques for maintaining subgraph frequencies in a dynamic graph, using data structures that are parameterized in terms of h, the h-index of the graph. Our methods extend previous results of Eppstein and Spiro for maintaining statistics for undirected subgraphs of size three to directed subgraphs and to subgraphs of size four. For the directed case, we provide a data structure to maintain counts for all 3-vertex induced subgraphs in O(h) amortized time per update. For the undirected case, we maintain the counts of size-four subgraphs in O(h^2) amortized time per update. These extensions enable a number of new applications in Bioinformatics and Social Networking research

    Faster and Simpler Distributed Algorithms for Testing and Correcting Graph Properties in the CONGEST-Model

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    In this paper we present distributed testing algorithms of graph properties in the CONGEST-model [Censor-Hillel et al. 2016]. We present one-sided error testing algorithms in the general graph model. We first describe a general procedure for converting ϵ\epsilon-testers with a number of rounds f(D)f(D), where DD denotes the diameter of the graph, to O((logn)/ϵ)+f((logn)/ϵ)O((\log n)/\epsilon)+f((\log n)/\epsilon) rounds, where nn is the number of processors of the network. We then apply this procedure to obtain an optimal tester, in terms of nn, for testing bipartiteness, whose round complexity is O(ϵ1logn)O(\epsilon^{-1}\log n), which improves over the poly(ϵ1logn)poly(\epsilon^{-1} \log n)-round algorithm by Censor-Hillel et al. (DISC 2016). Moreover, for cycle-freeness, we obtain a \emph{corrector} of the graph that locally corrects the graph so that the corrected graph is acyclic. Note that, unlike a tester, a corrector needs to mend the graph in many places in the case that the graph is far from having the property. In the second part of the paper we design algorithms for testing whether the network is HH-free for any connected HH of size up to four with round complexity of O(ϵ1)O(\epsilon^{-1}). This improves over the O(ϵ2)O(\epsilon^{-2})-round algorithms for testing triangle freeness by Censor-Hillel et al. (DISC 2016) and for testing excluded graphs of size 44 by Fraigniaud et al. (DISC 2016). In the last part we generalize the global tester by Iwama and Yoshida (ITCS 2014) of testing kk-path freeness to testing the exclusion of any tree of order kk. We then show how to simulate this algorithm in the CONGEST-model in O(kk2+1ϵk)O(k^{k^2+1}\cdot\epsilon^{-k}) rounds

    Sparse Kneser graphs are Hamiltonian

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    For integers k1k\geq 1 and n2k+1n\geq 2k+1, the Kneser graph K(n,k)K(n,k) is the graph whose vertices are the kk-element subsets of {1,,n}\{1,\ldots,n\} and whose edges connect pairs of subsets that are disjoint. The Kneser graphs of the form K(2k+1,k)K(2k+1,k) are also known as the odd graphs. We settle an old problem due to Meredith, Lloyd, and Biggs from the 1970s, proving that for every k3k\geq 3, the odd graph K(2k+1,k)K(2k+1,k) has a Hamilton cycle. This and a known conditional result due to Johnson imply that all Kneser graphs of the form K(2k+2a,k)K(2k+2^a,k) with k3k\geq 3 and a0a\geq 0 have a Hamilton cycle. We also prove that K(2k+1,k)K(2k+1,k) has at least 22k62^{2^{k-6}} distinct Hamilton cycles for k6k\geq 6. Our proofs are based on a reduction of the Hamiltonicity problem in the odd graph to the problem of finding a spanning tree in a suitably defined hypergraph on Dyck words
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