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Finding Euler Tours in One Pass in the W-Streaming Model with O(n log(n)) RAM

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arxiv 1710.04091 v1 pith:XNCDZMQP submitted 2017-10-11 cs.DS

classification cs.DS
keywords edgescyclesedgeeulermodelw-streamingmergesuccessor
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We study the problem of finding an Euler tour in an undirected graph G in the W-Streaming model with O(n polylog(n)) RAM, where n resp. m is the number of nodes resp. edges of G. Our main result is the first one pass W-Streaming algorithm computing an Euler tour of G in the form of an edge successor function with only O(n log(n)) RAM which is optimal for this setting (e.g., Sun and Woodruff (2015)). The previously best-known result in this model is implicitly given by Demetrescu et al. (2010) with the parallel algorithm of Atallah and Vishkin (1984) using O(m/n) passes under the same RAM limitation. For graphs with \omega(n) edges this is non-constant. Our overall approach is to partition the edges into edge-disjoint cycles and to merge the cycles until a single Euler tour is achieved. Note that in the W-Streaming model such a merging is far from being obvious as the limited RAM allows the processing of only a constant number of cycles at once. This enforces us to merge cycles that partially are no longer present in RAM. Furthermore, the successor of an edge cannot be changed after the edge has left RAM. So, we steadily have to output edges and their designated successors, not knowing the appearance of edges and cycles yet to come. We solve this problem with a special edge swapping technique, for which two certain edges per node are sufficient to merge tours without having all of their edges in RAM. Mathematically, this is controlled by structural results on the space of certain equivalence classes corresponding to cycles and the characterization of associated successor functions. For example, we give conditions under which the swapping of edge successors leads to a merging of equivalence classes.

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  1. Improved Streaming Edge Coloring

    cs.DS 2025-04 conditional novelty 7.0 of 10

    A W-streaming edge coloring algorithm uses O(delta to the 4/3 plus epsilon) colors with near-linear space, improving the prior randomized delta to 1.5 bound and giving the first deterministic subquadratic result.

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