Showing papers on "Connectivity published in 2018"

End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion

Abstract: Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. In this work, we propose a novel end-to-end Structure-Aware Convolutional Network (SACN) that takes the benefit of GCN and ConvE together. SACN consists of an encoder of a weighted graph convolutional network (WGCN), and a decoder of a convolutional network called Conv-TransE. WGCN utilizes knowledge graph node structure, node attributes and edge relation types. It has learnable weights that adapt the amount of information from neighbors used in local aggregation, leading to more accurate embeddings of graph nodes. Node attributes in the graph are represented as additional nodes in the WGCN. The decoder Conv-TransE enables the state-of-the-art ConvE to be translational between entities and relations while keeps the same link prediction performance as ConvE. We demonstrate the effectiveness of the proposed SACN on standard FB15k-237 and WN18RR datasets, and it gives about 10% relative improvement over the state-of-the-art ConvE in terms of HITS@1, HITS@3 and HITS@10.

Parallel Graph Connectivity in Log Diameter Rounds

Abstract: Many modern parallel systems, such as MapReduce, Hadoop and Spark, can be modeled well by the MPC model. The MPC model captures well coarse-grained computation on large data — data is distributed to processors, each of which has a sublinear (in the input data) amount of memory and we alternate between rounds of computation and rounds of communication, where each machine can communicate an amount of data as large as the size of its memory. This model is stronger than the classical PRAM model, and it is an intriguing question to design algorithms whose running time is smaller than in the PRAM model. One fundamental graph problem is connectivity. On an undirected graph with n nodes and m edges, O(log n) round connectivity algorithms have been known for over 35 years. However, no algorithms with better complexity bounds were known. In this work, we give fully scalable, faster algorithms for the connectivity problem, by parameterizing the time complexity as a function of the diameter of the graph. Our main result is a O(log D log log_m/n n) time connectivity algorithm for diameter-d graphs, using Θ(m) total memory. If our algorithm can use more memory, it can terminate in fewer rounds, and there is no lower bound on the memory per processor. We extend our results to related graph problems such as spanning forest, finding a DFS sequence, exact/approximate minimum spanning forest, and bottleneck spanning forest. We also show that achieving similar bounds for reachability in directed graphs would imply faster boolean matrix multiplication algorithms. We introduce several new algorithmic ideas. We describe a general technique called double exponential speed problem size reduction which roughly means that if we can use total memory n to reduce a problem from size n to n/k, for k=(N/n)^Θ(1) in one phase, then we can solve the problem in O(loglog_N/n n) phases. In order to achieve this fast reduction for graph connectivity, we use a multistep algorithm. One key step is a carefully constructed truncated broadcasting scheme where each node broadcasts neighbor sets to its neighbors in a way that limits the size of the resulting neighbor sets. Another key step is random leader contraction, where we choose a smaller set of leaders than many previous works do.

Band connectivity for topological quantum chemistry: Band structures as a graph theory problem

Abstract: The conventional theory of solids is well suited to describing band structures locally near isolated points in momentum space, but struggles to capture the full, global picture necessary for understanding topological phenomena. In part of a recent paper [B. Bradlyn et al., Nature (London) 547, 298 (2017)], we have introduced the way to overcome this difficulty by formulating the problem of sewing together many disconnected local $\mathbf{k}\ifmmode\cdot\else\textperiodcentered\fi{}\mathbf{p}$ band structures across the Brillouin zone in terms of graph theory. In this paper, we give the details of our full theoretical construction. We show that crystal symmetries strongly constrain the allowed connectivities of energy bands, and we employ graph theoretic techniques such as graph connectivity to enumerate all the solutions to these constraints. The tools of graph theory allow us to identify disconnected groups of bands in these solutions, and so identify topologically distinct insulating phases.

Parallel Graph Connectivity in Log Diameter Rounds

Abstract: We study graph connectivity problem in MPC model. On an undirected graph with $n$ nodes and $m$ edges, $O(\log n)$ round connectivity algorithms have been known for over 35 years. However, no algorithms with better complexity bounds were known. In this work, we give fully scalable, faster algorithms for the connectivity problem, by parameterizing the time complexity as a function of the diameter of the graph. Our main result is a $O(\log D \log\log_{m/n} n)$ time connectivity algorithm for diameter-$D$ graphs, using $\Theta(m)$ total memory. If our algorithm can use more memory, it can terminate in fewer rounds, and there is no lower bound on the memory per processor. We extend our results to related graph problems such as spanning forest, finding a DFS sequence, exact/approximate minimum spanning forest, and bottleneck spanning forest. We also show that achieving similar bounds for reachability in directed graphs would imply faster boolean matrix multiplication algorithms. We introduce several new algorithmic ideas. We describe a general technique called double exponential speed problem size reduction which roughly means that if we can use total memory $N$ to reduce a problem from size $n$ to $n/k$, for $k=(N/n)^{\Theta(1)}$ in one phase, then we can solve the problem in $O(\log\log_{N/n} n)$ phases. In order to achieve this fast reduction for graph connectivity, we use a multistep algorithm. One key step is a carefully constructed truncated broadcasting scheme where each node broadcasts neighbor sets to its neighbors in a way that limits the size of the resulting neighbor sets. Another key step is random leader contraction, where we choose a smaller set of leaders than many previous works do.

Trajectory Tracking by Multiple Agents in Formation With Collision Avoidance and Connectivity Assurance

Abstract: This paper is concerned with the trajectory tracking by multiple agents in formation. Each agent is modeled as a double-integrator system. The proposed algorithm works on a connected graph instead of a fully connected graph that requires lesser communication and computation. The three aspects of the control scheme—connectivity assurance, collision avoidance, and formation have been ensured by the design of the novel control law consisting of four terms. A novel bounded potential function has been introduced that ensures interagent collision avoidance through the innovative design of critical parameters associated with this potential function. Another novel bounded potential function has been designed to ensure the connectivity in the group. The concept of consensus has been used to acquire and maintain the desired formation pattern with velocity agreement among agents. The proposed control scheme is further extended to the problem of trajectory tracking while achieving other control objectives. Both the schemes have been shown to be convergent using Barbalat's Lemma. Detailed theoretical analysis of the algorithms has been carried out including boundedness of the control actions. The performance of proposed algorithms has been demonstrated through extensive simulations in 2-D and 3-D environments using 6 and 60 agents, respectively.

Cellular-Enabled UAV Communication: Trajectory Optimization under Connectivity Constraint

Abstract: In this paper, we study a cellular-enabled unmanned aerial vehicle (UAV) communication system consisting of one UAV and multiple ground base stations (GBSs). The UAV has a mission of flying from an initial location to a final location, during which it needs to maintain reliable wireless connection with the cellular network by associating with one of the GBSs at each time instant. We aim to minimize the UAV mission completion time by optimizing its trajectory, subject to a quality of connectivity constraint of the GBS-UAV link specified by a minimum received signal-to-noise ratio (SNR) target, which needs to be satisfied throughout the mission. This problem is non-convex and difficult to be optimally solved. We first propose an effective approach to check its feasibility based on graph connectivity verification. Then, by examining the GBS-UAV association sequence during the UAV mission, we obtain useful insights on the optimal UAV trajectory, based on which an efficient algorithm is proposed to find an approximate solution to the trajectory optimization problem by leveraging techniques in convex optimization and graph theory. Numerical results show that our proposed trajectory design achieves near-optimal performance.

Shuffles and Circuits (On Lower Bounds for Modern Parallel Computation)

Abstract: The goal of this article is to identify fundamental limitations on how efficiently algorithms implemented on platforms such as MapReduce and Hadoop can compute the central problems in motivating application domains, such as graph connectivity problems. We introduce an abstract model of massively parallel computation, where essentially the only restrictions are that the “fan-in” of each machine is limited to s bits, where s is smaller than the input size n, and that computation proceeds in synchronized rounds, with no communication between different machines within a round. Lower bounds on the round complexity of a problem in this model apply to every computing platform that shares the most basic design principles of MapReduce-type systems. We prove that computations in our model that use few rounds can be represented as low-degree polynomials over the reals. This connection allows us to translate a lower bound on the (approximate) polynomial degree of a Boolean function to a lower bound on the round complexity of every (randomized) massively parallel computation of that function. These lower bounds apply even in the “unbounded width” version of our model, where the number of machines can be arbitrarily large. As one example of our general results, computing any nontrivial monotone graph property—such as connectivity—requires a super-constant number of rounds when every machine receives only a subpolynomial (in n) number of input bits s. Finally, we prove that, in two senses, our lower bounds are the best one could hope for. For the unbounded-width model, we prove a matching upper bound. Restricting to a polynomial number of machines, we show that asymptotically better lower bounds would separate P from NC1.

Graphs of curves on infinite-type surfaces with mapping class group actions

Abstract: We study when the mapping class group of an infinite-type surface $S$ admits an action with unbounded orbits on a connected graph whose vertices are simple closed curves on $S$. We introduce a topological invariant for infinite-type surfaces that determines in many cases whether there is such an action. This allows us to conclude that, as non-locally compact topological groups, many big mapping class groups have nontrivial coarse geometry in the sense of Rosendal.

New and Modified Eccentric Indices of Octagonal Grid Omn

Abstract: The eccentricity eu of vertex u in a connected graph G, is the distance between u and a vertex farthermost from u. The aim of the present paper is to introduce new eccentricity based index and eccentricity based polynomial, namely modified augmented eccentric connectivity index and modified augmented eccentric connectivity polynomial respectively. As an application we compute these new indices for octagonal grid O m n and we compare the results obtained with the ones obtained by other indices like Ediz eccentric connectivity index, modified eccentric connectivity index and modified eccentric connectivity polynomial ECP(G, x)

Edge Version of Metric Dimension and Doubly Resolving Sets of the Necklace Graph

Abstract: Consider an undirected and connected graph G = ( V G , E G ) , where V G and E G represent the set of vertices and the set of edges respectively. The concept of edge version of metric dimension and doubly resolving sets is based on the distances of edges in a graph. In this paper, we find the edge version of metric dimension and doubly resolving sets for the necklace graph.

Optimizing Parallel Graph Connectivity Computation via Subgraph Sampling

Abstract: Connected component identification is a fundamental problem in graph analytics, serving as a basis for subsequent computations in a wide range of applications. To determine connectivity, several parallel algorithms, whose complexity is proportional to the number of edges or graph diameter, have been proposed. However, an optimal algorithm may extract graph components by working proportionally to the number of vertices, which can be orders of magnitude lower than the number of edges. We propose Afforest: an extension of the Shiloach-Vishkin connected components algorithm that approaches optimal work efficiency by processing subgraphs in each iteration. We prove the convergence of the algorithm, analyze its work efficiency characteristics, and provide further techniques to speed up processing graphs containing a huge component. Designed with modern parallel architectures in mind, we show that the algorithm exhibits higher memory locality than existing methods. Using both synthetic and real-world graphs, we demonstrate that Afforest achieves speedups of up to 67x over the state-of-the-art on multi-core CPUs (Broadwell, POWER8) and up to 23x on GPUs (Pascal).

Computing Metric Dimension and Metric Basis of 2D Lattice of Alpha-Boron Nanotubes

Abstract: Concepts of resolving set and metric basis has enjoyed a lot of success because of multi-purpose applications both in computer and mathematical sciences. For a connected graph G(V,E) a subset W of V(G) is a resolving set for G if every two vertices of G have distinct representations with respect to W. A resolving set of minimum cardinality is called a metric basis for graph G and this minimum cardinality is known as metric dimension of G. Boron nanotubes with different lattice structures, radii and chirality’s have attracted attention due to their transport properties, electronic structure and structural stability. In the present article, we compute the metric dimension and metric basis of 2D lattices of alpha-boron nanotubes.

Lower Bounds for Subgraph Detection in the CONGEST Model

Abstract: In the subgraph-freeness problem, we are given a constant-sized graph H, and wish to de- termine whether the network graph contains H as a subgraph or not. Until now, the only lower bounds on subgraph-freeness known for the CONGEST model were for cycles of length greater than 3; here we extend and generalize the cycle lower bound, and obtain polynomial lower bounds for subgraph-freeness in the CONGEST model for two classes of subgraphs. The first class contains any graph obtained by starting from a 2-connected graph H for which we already know a lower bound, and replacing the vertices of H by arbitrary connected graphs. We show that the lower bound on H carries over to the new graph. The second class is constructed by starting from a cycle Ck of length k ≥ 4, and constructing a graph H from Ck by replacing each edge {i, (i + 1) mod k} of the cycle with a connected graph Hi, subject to some constraints on the graphs H_{0}, . . . , H_{k−1}. In this case we obtain a polynomial lower bound for the new graph H , depending on the size of the shortest cycle in H passing through the vertices of the original k-cycle.

Cooperative Control of Multiple Agents with Unknown High-frequency Gain Signs under Unbalanced and Switching Topologies

Abstract: Existing results on cooperative control of multi-agent systems with unknown control directions require that the underlying topology is either fixed with a strongly connected graph or switching between different strongly connected graphs. Furthermore, in most cases the graph is assumed to be balanced. This paper proposes a new class of nonlinear PI based algorithms to relax these requirements and allow for unbalanced and switching topologies having a jointly strongly connected basis. This is made possible for single-integrator (SI) and double-integrator (DI) agents with non-identical unknown control directions by a suitable selection of the distributed nonlinear PI functions. Moreover, as a special case, the proposed algorithms are applied to strongly connected and fixed graphs. Finally, simulation examples are given to show the validity of our theoretical results.

Computing Minimum Spanning Tree in Interval Valued Bipolar Neutrosophic Environment

Abstract: Interval valued bipolar neutrosophic sets is a new generalization of fuzzy set, bipolar fuzzy set, neutrosophic set and bipolar neutrosophic set so that it can handle uncertain information more flexibly in the process of decision making. In this paper, an algorithm for finding minimum spanning tree (MST) of an undirected neutrosophic weighted connected graph (UNWCG) in which the edge weights is represented by a an interval valued bipolar neutrosophic number is presented.

Rainbow vertex connection of digraphs

Abstract: An edge-coloured path is rainbow if its edges have distinct colours. An edge-coloured connected graph is said to be rainbow connected if any two vertices are connected by a rainbow path, and strongly rainbow connected if any two vertices are connected by a rainbow geodesic. The (strong) rainbow connection number of a connected graph is the minimum number of colours needed to make the graph (strongly) rainbow connected. These two graph parameters were introduced by Chartrand et al. (Math Bohem 133:85–98, 2008). As an extension, Krivelevich and Yuster proposed the concept of rainbow vertex-connection. The topic of rainbow connection in graphs drew much attention and various similar parameters were introduced, mostly dealing with undirected graphs. Dorbec, Schiermeyer, Sidorowicz and Sopena extended the concept of the rainbow connection to digraphs. In this paper, we consider the (strong) rainbow vertex-connection number of digraphs. Results on the (strong) rainbow vertex-connection number of biorientations of graphs, cycle digraphs, circulant digraphs and tournaments are presented.

A Framework for In-place Graph Algorithms

Abstract: Read-only memory (ROM) model is a classical model of computation to study time-space tradeoffs of algorithms A classical result on the ROM model is that any algorithm to sort n numbers using O(s) words of extra space requires Omega (n^2/s) comparisons for lg n <= s <= n/lg n and the bound has also been recently matched by an algorithm However, if we relax the model, we do have sorting algorithms (say Heapsort) that can sort using O(n lg n) comparisons using O(lg n) bits of extra space, even keeping a permutation of the given input sequence at anytime during the algorithm We address similar relaxations for graph algorithms We show that a simple natural relaxation of ROM model allows us to implement fundamental graph search methods like BFS and DFS more space efficiently than in ROM By simply allowing elements in the adjacency list of a vertex to be permuted, we show that, on an undirected or directed connected graph G having n vertices and m edges, the vertices of G can be output in a DFS or BFS order using O(lg n) bits of extra space and O(n^3 lg n) time Thus we obtain similar bounds for reachability and shortest path distance (both for undirected and directed graphs) With a little more (but still polynomial) time, we can also output vertices in the lex-DFS order As reachability in directed graphs (even in DAGs) and shortest path distance (even in undirected graphs) are NL-complete, and lex-DFS is P-complete, our results show that our model is more powerful than ROM if L != P En route, we also introduce and develop algorithms for another relaxation of ROM where the adjacency lists of the vertices are circular lists and we can modify only the heads of the lists Here we first show a linear time DFS implementation using n + O(lg n) bits of extra space Improving the extra space exponentially to only O(lg n) bits, we also obtain BFS and DFS albeit with a slightly slower running time Both the models we propose maintain the graph structure throughout the algorithm, only the order of vertices in the adjacency list changes In sharp contrast, for BFS and DFS, to the best of our knowledge, there are no algorithms in ROM that use even O(n^{1-epsilon}) bits of extra space; in fact, implementing DFS using cn bits for c<1 has been mentioned as an open problem Furthermore, DFS (BFS, respectively) algorithms using n+o(n) (o(n), respectively) bits of extra use Reingold's [JACM, 2008] or Barnes et al's reachability algorithm [SICOMP, 1998] and hence have high runtime Our results can be contrasted with the recent result of Buhrman et al [STOC, 2014] which gives an algorithm for directed st-reachability on catalytic Turing machines using O(lg n) bits with catalytic space O(n^2 lg n) and time O(n^9)

Joint Optimization of Scheduling and Routing in Multicast Wireless Ad-Hoc Network Using Soft Graph Coloring and Non-linear Cubic Games

Abstract: In this paper we present matrix game-theoretic models for joint routing, network coding, and scheduling problem. First routing and network coding are modeled by using a new approach based on compressed topology matrix that takes into account the inherent multicast gain of the network. The scheduling is optimized by a new approach called network graph soft coloring. Soft graph coloring is designed by switching between different components of a wireless network graph, which we refer to as graph fractals, with appropriate usage rates. The network components, represented by graph fractals, are a new paradigm in network graph partitioning that enables modeling of the network optimization problem by using the matrix game framework. In the proposed game which is a nonlinear cubic game, the strategy sets of the players are links, path, and network components. The outputs of this game model are mixed strategy vectors of the second and the third players at equilibrium. Strategy vector of the second player specifies optimum multi-path routing and network coding solution while mixed strategy vector of the third players indicates optimum switching rate among different network components or membership probabilities for optimal soft scheduling approach. Optimum throughput is the value of the proposed nonlinear cubic game at equilibrium. The proposed nonlinear cubic game is solved by extending fictitious playing method. Numerical and simulation results prove the superior performance of the proposed techniques compared to the conventional schemes using hard graph coloring.