Shortest Paths and Distances with Differential Privacy

We introduce a model for differentially private analysis of weighted graphs in which the graph topology $(V,E)$ is assumed to be public and the private information consists only of the edge weights $w:E\to\mathbb{R}^+$. 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 $\epsilon$ and $\delta$, 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 $\ell_1$ 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 privately releasing a short path between a pair of vertices, we prove a lower bound of $\Omega(|V|)$ on the additive approximation error for fixed $\epsilon,\delta$. We provide a differentially private algorithm that matches this error bound up to a logarithmic factor and releases paths between all pairs of vertices. The approximation error of 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 vertex pairs that are connected by a low-weight path with $o(|V|)$ vertices. For privately releasing all-pairs distances, we show that for trees we can release all distances with approximation error $O(\log^{2.5}|V|)$ for fixed privacy parameters. For arbitrary bounded-weight graphs with edge weights in $[0,M]$ we can release all distances with approximation error $\tilde{O}(\sqrt{|V|M})$

GG-reconstruction of graphs

Let $G$ be a group of permutations acting on an $n$-vertex set $V$, and $X$ and $Y$ be two simple graphs on $V$. We say that $X$ and $Y$ are $G$-isomorphic if $Y$ belongs to the orbit of $X$ under the action of $G$. One can naturally generalize the reconstruction problems so that when $G$ is $S_n$, the symmetric group, we have the usual reconstruction problems. In this paper, we study $G$-edge reconstructibility of graphs. We prove some old and new results on edge reconstruction and reconstruction from end vertex deleted subgraphs.Comment: 8 page

Lipschitz Functions on Expanders are Typically Flat

This work studies the typical behavior of random integer-valued Lipschitz functions on expander graphs with sufficiently good expansion. We consider two families of functions: M-Lipschitz functions (functions that change by at most M along edges) and integer-homomorphisms (functions that change by exactly 1 along edges). We prove that such functions typically exhibit very small fluctuations. For instance, we show that a uniformly chosen M-Lipschitz function takes only M+1 values on most of the graph, with a double exponential decay for the probability to take other values.Comment: 26 page

Combinatorial Optimization Problems with Interaction Costs: Complexity and Solvable Cases

We introduce and study the combinatorial optimization problem with interaction costs (COPIC). COPIC is the problem of finding two combinatorial structures, one from each of two given families, such that the sum of their independent linear costs and the interaction costs between elements of the two selected structures is minimized. COPIC generalizes the quadratic assignment problem and many other well studied combinatorial optimization problems, and hence covers many real world applications. We show how various topics from different areas in the literature can be formulated as special cases of COPIC. The main contributions of this paper are results on the computational complexity and approximability of COPIC for different families of combinatorial structures (e.g. spanning trees, paths, matroids), and special structures of the interaction costs. More specifically, we analyze the complexity if the interaction cost matrix is parameterized by its rank and if it is a diagonal matrix. Also, we determine the structure of the intersection cost matrix, such that COPIC is equivalent to independently solving linear optimization problems for the two given families of combinatorial structures

Combinatorics of Tripartite Boundary Connections for Trees and Dimers

A grove is a spanning forest of a planar graph in which every component tree contains at least one of a special subset of vertices on the outer face called nodes. For the natural probability measure on groves, we compute various connection probabilities for the nodes in a random grove. In particular, for "tripartite" pairings of the nodes, the probability can be computed as a Pfaffian in the entries of the Dirichlet-to-Neumann matrix (discrete Hilbert transform) of the graph. These formulas generalize the determinant formulas given by Curtis, Ingerman, and Morrow, and by Fomin, for parallel pairings. These Pfaffian formulas are used to give exact expressions for reconstruction: reconstructing the conductances of a planar graph from boundary measurements. We prove similar theorems for the double-dimer model on bipartite planar graphs.Comment: 29 pages, 7 figure

Tree 3-spanners of diameter at most 5

Tree spanners approximate distances within graphs; a subtree of a graph is a tree $t$-spanner of the graph if and only if for every pair of vertices their distance in the subtree is at most $t$ times their distance in the graph. When a graph contains a subtree of diameter at most $t$, then trivially admits a tree $t$-spanner. Now, determining whether a graph admits a tree $t$-spanner of diameter at most $t+1$ is an NP complete problem, when $t\geq 4$, and it is tractable, when $t\leq 3$. Although it is not known whether it is tractable to decide graphs that admit a tree 3-spanner of any diameter, an efficient algorithm to determine graphs that admit a tree 3-spanner of diameter at most 5 is presented. Moreover, it is proved that if a graph of diameter at most 3 admits a tee 3-spanner, then it admits a tree 3-spanner of diameter at most 5. Hence, this algorithm decides tree 3-spanner admissibility of diameter at most 3 graphs

Families of 22-weights of some particular graphs

Let ${\cal G}=(G,w)$ be a positive-weighted graph, that is a graph $G$ endowed with a function $w$ from the edge set of $G$ to the set of positive real numbers; for any distinct vertices $i,j$, we define $D_{i,j}({\cal G})$ to be the weight of the path in $G$ joining $i$ and $j$ with minimum weight. In this paper we fix a particular class of graphs and we give a criterion to establish whether, given a family of positive real numbers $\{D_I\}_{I \in { \{1,...., n\} \choose 2}}$, there exists a positive-weighted graph ${\cal G} =(G,w)$ in the class we have fixed, with vertex set equal to $\{1,....,n\}$ and such that $D_I ({\cal G}) =D_I$ for any $I \in { \{1,...., n\} \choose 2}$. In particular, the classes of graphs we consider are the following: snakes, caterpillars, polygons, bipartite graphs, complete graphs, planar graphs.Comment: 14 page

Quantum algorithms for graph problems with cut queries

Let $G$ be an $n$-vertex graph with $m$ edges. When asked a subset $S$ of vertices, a cut query on $G$ returns the number of edges of $G$ that have exactly one endpoint in $S$. We show that there is a bounded-error quantum algorithm that determines all connected components of $G$ after making $O(\log(n)^6)$ many cut queries. In contrast, it follows from results in communication complexity that any randomized algorithm even just to decide whether the graph is connected or not must make at least $\Omega(n/\log(n))$ many cut queries. We further show that with $O(\log(n)^8)$ many cut queries a quantum algorithm can with high probability output a spanning forest for $G$. En route to proving these results, we design quantum algorithms for learning a graph using cut queries. We show that a quantum algorithm can learn a graph with maximum degree $d$ after $O(d \log(n)^2)$ many cut queries, and can learn a general graph with $O(\sqrt{m} \log(n)^{3/2})$ many cut queries. These two upper bounds are tight up to the poly-logarithmic factors, and compare to $\Omega(dn)$ and $\Omega(m/\log(n))$ lower bounds on the number of cut queries needed by a randomized algorithm for the same problems, respectively. The key ingredients in our results are the Bernstein-Vazirani algorithm, approximate counting with "OR queries", and learning sparse vectors from inner products as in compressed sensing.Comment: Corrected an error in Lemma 1. This led to an extra log factor in the complexity of the connectivity and spanning forest algorithm

Topological Graphic Passwords And Their Matchings Towards Cryptography

Graphical passwords (GPWs) are convenient for mobile equipments with touch screen. Topological graphic passwords (Topsnut-gpws) can be saved in computer by classical matrices and run quickly than the existing GPWs. We research Topsnut-gpws by the matching of view, since they have many advantages. We discuss: configuration matching partition, coloring/labelling matching partition, set matching partition, matching chain, etc. And, we introduce new graph labellings for enriching Topsnut-matchings and show that these labellings can be realized for trees or spanning trees of networks. In theoretical works we explore Graph Labelling Analysis, and show that every graph admits our extremal labellings and set-type labellings in graph theory. Many of the graph labellings mentioned are related with problems of set matching partitions to number theory, and yield new objects and new problems to graph theory

Uniform hypergraphs and dominating sets of graphs

A (simple) hypergraph is a family H of pairwise incomparable sets of a finite set. We say that a hypergraph H is a domination hypergraph if there is at least a graph G such that the collection of minimal dominating sets of G is equal to H. Given a hypergraph, we are interested in determining if it is a domination hypergraph and, if this is not the case, we want to find domination hypergraphs in some sense close to it, the domination completions. Here we will focus on the family of hypergraphs containing all the subsets with the same cardinality, the uniform hypergraphs of maximum size. Specifically, we characterize those hypergraphs H in this family that are domination hypergraphs and, in any other case, we prove that the hypergraph H is uniquely determined by some of its domination completions and that H can be recovered from them by using a suitable hypergraph operation.Comment: 24 pages, 3 figure