Nearly Linear Time Minimum Spanning TreeMaintenance for Transient Node Failures

Given a 2-node connected, real weighted, and undirected graph $G=(V,E)$, with $n$ nodes and $m$ edges, and given a minimum spanning tree (MST) $T=(V,E_T)$ of $G$, we study the problem of finding, for every node $v \in V$, a set of replacement edges which can be used for constructing an MST of $G-v$ (i.e., the graph $G$ deprived of $v$ and all its incident edges). We show that this problem can be solved on a pointer machine in ${\cal O}(m \cdot \alpha(m,n))$ time and ${\cal O}(m)$ space, where $\alpha$ is the functional inverse of Ackermann's function. Our solution improves over the previously best known ${\cal O}(\min\{m \cdot \alpha(n,n), m + n \log n\})$ time bound, and allows us to close the gap existing with the fastest solution for the edge-removal version of the problem (i.e., that of finding, for every edge $e \in E_T$, a replacement edge which can be used for constructing an MST of $G-e=(V,E \backslash \{e\})$). Our algorithm finds immediate application in maintaining MST-based communication networks undergoing temporary node failures. Moreover, in a distributed environment in which nodes are managed by selfish agents, it can be used to design an efficient, truthful mechanism for building an MS

04091 Abstracts Collection -- Data Structures

From 22.02. to 27.02.2004, Dagstuhl Seminar "Data Structures" was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar are put together in this paper. The first section describes the seminar topics and goals in general

Connectivity Oracles for Graphs Subject to Vertex Failures

We introduce new data structures for answering connectivity queries in graphs subject to batched vertex failures. A deterministic structure processes a batch of $d\leq d_{\star}$ failed vertices in $\tilde{O}(d^3)$ time and thereafter answers connectivity queries in $O(d)$ time. It occupies space $O(d_{\star} m\log n)$. We develop a randomized Monte Carlo version of our data structure with update time $\tilde{O}(d^2)$, query time $O(d)$, and space $\tilde{O}(m)$ for any failure bound $d\le n$. This is the first connectivity oracle for general graphs that can efficiently deal with an unbounded number of vertex failures. We also develop a more efficient Monte Carlo edge-failure connectivity oracle. Using space $O(n\log^2 n)$, $d$ edge failures are processed in $O(d\log d\log\log n)$ time and thereafter, connectivity queries are answered in $O(\log\log n)$ time, which are correct w.h.p. Our data structures are based on a new decomposition theorem for an undirected graph $G=(V,E)$, which is of independent interest. It states that for any terminal set $U\subseteq V$ we can remove a set $B$ of $|U|/(s-2)$ vertices such that the remaining graph contains a Steiner forest for $U-B$ with maximum degree $s$

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum

Proceedings of the 8th Cologne-Twente Workshop on Graphs and Combinatorial Optimization

International audienceThe Cologne-Twente Workshop (CTW) on Graphs and Combinatorial Optimization started off as a series of workshops organized bi-annually by either Köln University or Twente University. As its importance grew over time, it re-centered its geographical focus by including northern Italy (CTW04 in Menaggio, on the lake Como and CTW08 in Gargnano, on the Garda lake). This year, CTW (in its eighth edition) will be staged in France for the first time: more precisely in the heart of Paris, at the Conservatoire National d’Arts et Métiers (CNAM), between 2nd and 4th June 2009, by a mixed organizing committee with members from LIX, Ecole Polytechnique and CEDRIC, CNAM

Dynamic distributed programming and applications to swap edge problem

Link failure is a common reason for disruption in communication networks. If communication between processes of a weighted distributed network is maintained by a spanning tree T, and if one edge e of T fails, communication can be restored by finding a new spanning tree, T’. If the network is 2-edge connected, T’ can always be constructed by replacing e by a single edge, e’, of the network. We refer to e’ as a swap edge of e. The best swap edge problem is to find the best choice of e’, that is, that e which causes the new spanning tree T’ to have the least cost, where cost is measured in a way that is determined by the application. Two examples of such measures are total weight of T‘ and diameter of T’. The all best swap edges problem is the problem of determining, in advance of any failure, the best swap edge for every edge in T. The justification for this problem is that we wish to be ready, when a failure occurs, to quickly activate a replacement for the failed edge. In this thesis, we give algorithms for the all best swap edges problem for six different cost measures. We first present an algorithm which can be adapted to all six measures, and which takes O (d2) time, where d is the diameter of T. This algorithm is essentially a form of distributed dynamic programming, since we compute the answers to sub problems at each node of T. We then present a novel paradigm for speeding up distributed computations under certain conditions. We apply this paradigm to find O(d)-time distributed algorithms for the all best swap edge problem for all but one of our cost measures. Formal algorithms and their correctness proofs will be given

Algorithms for nonuniform networks

In this thesis, observations on structural properties of natural networks are taken as a starting point for developing efficient algorithms for natural instances of different graph problems. The key areas discussed are sampling, clustering, routing, and pattern mining for large, nonuniform graphs. The results include observations on structural effects together with algorithms that aim to reveal structural properties or exploit their presence in solving an interesting graph problem. Traditionally networks were modeled with uniform random graphs, assuming that each vertex was equally important and each edge equally likely to be present. Within the last decade, the approach has drastically changed due to the numerous observations on structural complexity in natural networks, many of which proved the uniform model to be inadequate for some contexts. This quickly lead to various models and measures that aim to characterize topological properties of different kinds of real-world networks also beyond the uniform networks. The goal of this thesis is to utilize such observations in algorithm design, in addition to empowering the process of network analysis. Knowing that a graph exhibits certain characteristics allows for more efficient storage, processing, analysis, and feature extraction. Our emphasis is on local methods that avoid resorting to information of the graph structure that is not relevant to the answer sought. For example, when seeking for the cluster of a single vertex, we compute it without using any global knowledge of the graph, iteratively examining the vicinity of the seed vertex. Similarly we propose methods for sampling and spanning-tree construction according to certain criteria on the outcome without requiring knowledge of the graph as a whole. Our motivation for concentrating on local methods is two-fold: one driving factor is the ever-increasing size of real-world problems, but an equally important fact is the nonuniformity present in many natural graph instances; properties that hold for the entire graph are often lost when only a small subgraph is examined.reviewe