Approximate distance queries and compact routing in sparse graphs

Local properties of geometric graphs

We study the power of four query models in the context of property testing in general graphs (i.e., with arbitrary edge densities), where our main case study is the problem of testing k-colorability. Two query types, which have been studied extensively in the past, are pair queries and neighbor queries. The former corresponds to asking whether there is an edge between any particular pair of vertices, and the latter to asking for the i’th neighbor of a particular vertex. We show that while for pair queries, testing k-colorability requires a number of queries that is a monotone decreasing function in the average degree d, the query complexity in the case of neighbor queries remains roughly the same for every density and for large values of k. We also consider a combined model that allows both types of queries, and we propose a new, stronger, query model, related to the field of Group Testing. We give one-sided error upper and lower bounds for all the models, where the bounds are nearly tight for three of the models. In some of the cases our lower bounds extend to two-sided error algorithms.The problem of testing k-colorability was previously studied in the contexts of dense and sparse graphs, and in our proofs we unify approaches from those cases, and also provide some new tools and techniques that may be of independent interest.Keywordspair queriesneighbor queriesgroup queriesk-colorability

Privacy preserving network publishing has been studied extensively in recent years. Although more works have adopted un-weighted graphs to model network relationships, weighted graph modeling can provide deeper analysis of the degree of relationships. Previous works on weighted graph privacy have concentrated on preserving the shortest path characteristic between pairs of vertices. Two common types of privacy have been proposed. One type of privacy tried to add random noise edge weights to the graph but still maintain the same shortest path. The other privacy, k-shortest path privacy, minimally perturbed edge weights so that there exists k shortest paths. However, the k-shortest path privacy only considers anonymizing same fixed number of shortest paths for all pairs of source and destination vertices. In this work, we present a new concept called [k1, k2]-shortest path privacy to allow different number of shortest paths for different pairs of vertices. A published network graph with [k1, k2]-shortest path privacy has at least k' indistinguishable shortest paths between the source and destination vertices, where k1 l k' l k2. A heuristic algorithm based on modifying only Non-Visited (NV) edges is proposed and experimental results showing the feasibility and characteristics of the proposed approach are presented.

In this paper we consider two families of competing algorithms for finding the shortest paths between all pairs of vertices (APSP) in directed weighted large graphs with different edge densities: Dijkstra and Floyd-Warshall. For comparison, we have taken Dijkstra's algorithm with dynamically varying binary heap, which solves the APSP prob-lem purely in parallel by repeatedly executing on all vertices of the graph considered as source vertices, and we have taken blocked Floyd-Warshall algorithm, which is also well-parallelizable. It is known that in terms of computational complexity, the first algorithm is preferable on sparse graphs and the second algorithm is preferable on dense graphs. At the same time, it is not clear what are the ranges of graph densities at which the first algorithm will consume less CPU time than the second algorithm. This paper describes multithreaded implementations of parallel algorithms on multicore processors that make different usage of synchronization primitives such as mutex, conditional variable, lock-ing, and atomic operation. By conducting computational experiments on an 8-core Intel(R) Core(TM) i7-10700 CPU @ 2.90GHz, we found that each algorithm has a preferred graph density. In the case of multi-threaded parallel imple-mentation, the blocked Floyd-Warshall algorithm has lower running time than Dijkstra's algorithm if the graph densi-ty is greater than 0.5. Otherwise, Dijkstra's algorithm runs faster. In the case of single-threaded implementation, the split point is 0.43.

We describe algorithms, based on Avis and Fukuda's reverse search paradigm, for listing all maximal independent sets in a sparse graph in polynomial time and delay per output. For bounded degree graphs, our algorithms take constant time per set generated; for minor-closed graph families, the time is O ( n ) per set, and for more general sparse graph families we achieve subquadratic time per set. We also describe new data structures for maintaining a dynamic vertex set S in a sparse or minor-closed graph family, and querying the number of vertices not dominated by S ; for minor-closed graph families the time per update is constant, while it is sublinear for any sparse graph family. We can also maintain a dynamic vertex set in an arbitrary m -edge graph and test the independence of the maintained set in time O (√m) per update. We use the domination data structures as part of our enumeration algorithms.

Inspired by Feige (36th STOC, 2004), we initiate a study of sublinear randomized algorithms for approximating average parameters of a graph. Specifically, we consider the average degree of a graph and the average distance between pairs of vertices in a graph. Since our focus is on sublinear algorithms, these algorithms access the input graph via queries to an adequate oracle.We consider two types of queries. The first type is standard neighborhood queries (i.e., what is the i th neighbor of vertex v ?), whereas the second type are queries regarding the quantities that we need to find the average of (i.e., what is the degree of vertex v ? and what is the distance between u and v ?, respectively).Loosely speaking, our results indicate a difference between the two problems: For approximating the average degree, the standard neighbor queries suffice and in fact are preferable to degree queries. In contrast, for approximating average distances, the standard neighbor queries are of little help whereas distance queries are crucial.KeywordsAverage DistanceAverage DegreeAverage ParameterInput GraphNeighbor QueryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The paper (Compact Routing Messages in Self-Healing Trees, TCS 2017) introduced CompactFTZ, the first self-healing compact routing algorithm that works in a distributed network with each node using only O(log n) words (i.e. O(log2 n) bits) memory and thus O(log n) sized messages. The routing uses only O(1) and O(log n) words routing table and packet labels respectively on a self-healing tree that also works using only O(1) words repairing the network in face of a strong adversary deleting nodes. This deterministic algorithm sets up its data structures in a preprocessing phase and then updates the required data structures in only O(1) parallel time per healing round during execution of the algorithm. However, CompactFTZ has no constraints in its preprocessing phase which could be done in distributed large memory or even centrally. In this paper, we correct that by developing the algorithms for preprocessing of CompactFTZ in a fully distributed manner using only O(log n) words memory in optimal time. In fact, the preprocessing for the self-healing tree (ForgivingTree) component takes only O(1) memory. We develop a local function which each node invokes to instantly compute and then relay its repair instructions (known as its Will) in only O(1) time. We formalise the low memory CONGEST model setting used in previous low memory algorithms (e.g.[24]); nodes' working memory is restricted to be much smaller (in our case, O(log n)) than the numbers of their neighbours to whom they communicate through their I/O ports. We expand the model to allow for non-contiguous ports (e.g. empty ports or neighbours unmarked or lost in dynamic settings) and adversarial order of inputs from neighbours. Besides the Wills, we set up the tree structures and traversals for the routing scheme using only O(log n) memory and O(D) parallel time, where D is the diameter. Thus, we devise the first self-healing compact routing algorithm that can be fully set up and executed in low memory.

The design of compact routing schemes in trees form the kernel of sophisticated strategies for compact routing in arbitrary graphs. This paper focuses on the space complexity for routing messages along shortest paths in trees. It was recently shown that the family of n-node trees supports routing schemes using addresses and routing tables of size O(log2 n/ log log n) bits per node, if the output port numbers of each node are chosen by an adversary. This paper shows that this result is tight, that is the sum of the sizes of the address and of the local routing table is at least ?(log2 n/ log log n) bits for some node of some tree.

We introduce a model for differentially private analysis of weighted graphs in which the graph topology (υ,e) is assumed to be public and the private information consists only of the edge weights ω : e → R+. This can express hiding congestion patterns in a known system of roads. Differential privacy requires that the output of an algorithm provides little advantage, measured by privacy parameters e and δ, for distinguishing between neighboring inputs, which are thought of as inputs that differ on the contribution of one individual. In our model, two weight functions w,w' are considered to be neighboring if they have l1 distance at most one.We study the problems of privately releasing a short path between a pair of vertices and of privately releasing approximate distances between all pairs of vertices. We are concerned with the approximation error, the difference between the length of the released path or released distance and the length of the shortest path or actual distance.For the problem of privately releasing a short path between a pair of vertices, we prove a lower bound of Ω(|υ|) on the additive approximation error for fixed privacy parameters e,δ. We provide a differentially private algorithm that matches this error bound up to a logarithmic factor and releases paths between all pairs of vertices, not just a single pair. The approximation error achieved by our algorithm can be bounded by the number of edges on the shortest path, so we achieve better accuracy than the worst-case bound for pairs of vertices that are connected by a low-weight path consisting of o(|υ|) vertices.For the problem of privately releasing all-pairs distances, we show that for trees we can release all-pairs distances with approximation error $O(log2.5|υ|) for fixed privacy parameters. For arbitrary bounded-weight graphs with edge weights in [0,M] we can brelease all distances with approximation error O(√>(|υ|M).

Given an undirected graph G with m edges, n vertices, and non-negative edge weights, and given an integer k ≥ 2, we show that a (2k − 1)-approximate distance oracle for G of size O(kn1+1/k) and with O(log k) query time can be constructed in O(min{kmn1/k, √km + kn1+c/ √k}) time for some constant c. This improves the O(k) query time of Thorup and Zwick. Furthermore, for any 0 < ∊ ≤ 1, we give an oracle of size O(kn1+1/k) that answers ((2 + ∊)k)-approximate distance queries in O(1/∊) time. At the cost of a k-factor in size, this improves the 128k approximation achieved by the constant query time oracle of Mendel and Naor and approaches the best possible tradeoff between size and stretch, implied by a widely believed girth conjecture of Erdős. We can match the O(n1+1/k) size bound of Mendel and Naor for any constant ∊ > 0 and k = O (log n/ log log n).

We discuss a new algorithmic type of problem in random graphs studying the minimum number of queries one has to ask about adjacency between pairs of vertices of a random graph in order to find a subgraph which possesses some target property with high probability. In this paper we focus on finding long paths in when for some fixed constant . This random graph is known to have typically linearly long paths. To have edges with high probability in one clearly needs to query at least pairs of vertices. Can we find a path of length economically, i.e., by querying roughly that many pairs? We argue that this is not possible and one needs to query significantly more pairs. We prove that any randomised algorithm which finds a path of length with at least constant probability in with must query at least pairs of vertices. This is tight up to the factor. © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 50, 71–85, 2017

Let G = ( V , E ) be an undirected graph on n vertices, and let δ( u , v ) denote the distance in G between two vertices u and v . Thorup and Zwick showed that for any positive integer t , the graph G can be preprocessed to build a data structure that can efficiently report t -approximate distance between any pair of vertices. That is, for any u , v ∈ V , the distance reported is at least δ( u , v ) and at most t δ( u , v ). The remarkable feature of this data structure is that, for t ≥3, it occupies subquadratic space, that is, it does not store all-pairs distances explicitly, and still it can answer any t -approximate distance query in constant time. They named the data structure “approximate distance oracle” because of this feature. Furthermore, the trade-off between the stretch t and the size of the data structure is essentially optimal.In this article, we show that we can actually construct approximate distance oracles in expected O ( n 2 ) time if the graph is unweighted. One of the new ideas used in the improved algorithm also leads to the first expected linear-time algorithm for computing an optimal size (2, 1)-spanner of an unweighted graph. A (2, 1) spanner of an undirected unweighted graph G = ( V , E ) is a subgraph ( V , Ê), Ê ⊆ E , such that for any two vertices u and v in the graph, their distance in the subgraph is at most 2δ( u , v ) + 1.

Spanners in sparse graphs

Thorup and Zwick (J. ACM 52(1):1–24, 2005 and STOC’01) in their seminal work introduced the notion of distance oracles. Given an n-vertex weighted undirected graph with m edges, they show that for any integer k≥1 it is possible to preprocess the graph in $\tilde {O}(mn^{1/k})$ time and generate a compact data structure of size O(kn 1+1/k ). For each pair of vertices, it is then possible to retrieve an estimated distance with multiplicative stretch 2k−1 in O(k) time. For k=2 this gives an oracle of O(n 1.5) size that produces in constant time estimated distances with stretch 3. Recently, Pǎtrascu and Roditty (In: Proc. of 51st FOCS, 2010) broke the theoretical status-quo in the field of distance oracles and obtained a distance oracle for sparse unweighted graphs of O(n 5/3) size that produces in constant time estimated distances with stretch 2. In this paper we show that it is possible to break the stretch 2 barrier at the price of non-constant query time in unweighted undirected graphs. We present a data structure that produces estimated distances with 1+e stretch. The size of the data structure is O(nm 1−e′) and the query time is $\tilde {O}(m^{1-\varepsilon '})$ . Using it for sparse unweighted graphs we can get a data structure of size O(n 1.87) that can supply in O(n 0.87) time estimated distances with multiplicative stretch 1.75.

We describe a method for preprocessing a weighted planar undirected graph and representing the results of the preprocessing so as to facilitate subsequent approximate distance queries. For any 0