Connectivity graphs: a method for proving deadlock freedom based on separation logic

The fastest deterministic algorithms for connected components take logarithmic time and perform superlinear work on a Parallel Random Access Machine (PRAM). These algorithms maintain a spanning forest by merging and compressing trees, which requires pointer-chasing operations that increase memory access latency and are limited to shared-memory systems. Many of these PRAM algorithms are also very complicated to implement. Another popular method is “leader-contraction” where the challenge is to select a constant fraction of leaders that are adjacent to a constant fraction of non-leaders with high probability, but this can require adding more edges than were in the original graph. Instead we investigate label propagation because it is deterministic, easy to implement, and does not rely on pointer-chasing. Label propagation exchanges representative labels within a component using simple graph traversal, but it is inherently difficult to complete in a sublinear number of steps. We are able to overcome the problems with label propagation for graph connectivity. We introduce a surprisingly simple framework for deterministic, undirected graph connectivity using label propagation that is easily adaptable to many computational models. It achieves logarithmic convergence independently of the number of processors and without increasing the edge count. We employ a novel method of propagating directed edges in alternating direction while performing minimum reduction on vertex labels. We present new algorithms in PRAM, Stream, and MapReduce. Given a simple, undirected graph [Formula: see text] with [Formula: see text] vertices, [Formula: see text] edges, our approach takes O(m) work each step, but we can only prove logarithmic convergence on a path graph. It was conjectured by Liu and Tarjan (2019) to take [Formula: see text] steps or possibly [Formula: see text] steps. Our experiments on a range of difficult graphs also suggest logarithmic convergence. We leave the proof of convergence as an open problem.

The Clar number of a (hydro)carbon molecule, introduced by Clar (The aromatic sextet, 1972), is the maximum number of mutually disjoint resonant hexagons in the molecule. Calculating the Clar number can be formulated as an optimization problem on 2-connected plane graphs. Namely, it is the maximum number of mutually disjoint even faces a perfect matching can simultaneously alternate on. It was proved by Abeledo and Atkinson (Linear Algebra Appl 420(2):441–448, 2007) that the Clar number can be computed in polynomial time if the plane graph has even faces only. We prove that calculating the Clar number in general 2-connected plane graphs is \(\mathsf {NP}\)-hard. We also prove \(\mathsf {NP}\)-hardness of the maximum independent set problem for 2-connected plane graphs with odd faces only, which may be of independent interest. Finally, we give an exact algorithm that determines the Clar number of a given 2-connected plane graph. The algorithm has a polynomial running time if the length of the shortest odd join in the planar dual graph is fixed, which gives an efficient algorithm for some fullerene classes, such as carbon nanotubes.

The incorrect use of pointers, such as null pointer dereference and memory leak, is one of the most common sources of program errors. In this talk, I will describe our techniques for automatically verifying the absence of such pointer errors, which we have been developing for the past three years, based on a new program logic called separation logic. This talk has two goals. The first is to show, by demo, the current status of techniques for automatically verifying pointer safety. The second is to present interesting instances of the interplay between automatic verification and program logic. In order to reduce the complexity of formal (manual) verification of programs, separation logic has unusual proof rules that exploit programming disciplines used by skilled software developers. I will explain how such rules have been used to improve the performance of our automatic verification techniques. Regarding the influence of automatic verification on program logic, I will describe new types of theorem proving questions on separation logic that were motivated by automatic verification.

We present two linear-time algorithms for computing a regular edge labeling of 4-connected planar triangular graphs. This labeling is used to compute in linear time a rectangular dual of this class of planar graphs. The two algorithms are based on totally different frameworks, and both are conceptually simpler than the previous known algorithm and are of independent interests. The first algorithm is based on edge contraction. The second algorithm is based on the canonical ordering. This ordering can also be used to compute more compact visibility representations for this class of planar graphs.KeywordsPlanar GraphOutgoing EdgeIncoming EdgeInterior VertexEdge ContractionThese 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.

Recent interests on XML, Semantic Web, and Web ontology, among other topics, have sparked a renewed interest on graph-structured databases. A fundamental query on graphs is the reachability test of nodes. This thesis includes a survey on various indexes to optimize reachability tests. The focus of this thesis is 2-hop labeling indexing method which was recently proposed to index large collections of XML and/or graphs for efficient reachability tests. In particular, we note that there has been few works on 2-hop labeling in dynamic setting where the graph is frequently updated. This is compounded by the fact that data may change over time. In response to these, this thesis studies the incremental maintenance of 2-hop labeling. We analyze the main reason for the inefficiency of updates of existing 2-hop labels. Based on our analysis, we propose node-separation property that leads to efficient incremental maintenance. Subsequently, we present novel incremental maintenance algorithms for 2-hop labels. Wepropose three updatable 2-hop labelings, hybrids of 2-hop labeling that satisfies node-separation property. The proposed 2-hop labeling is derived from graph connectivity, as opposed to SET COVER which is used by all previous work. Our experimental evaluation illustrates the space efficiency and update performance of various kinds of 2-hop labeling. Our results show that the incremental maintenance algorithm can be two orders of magnitude faster than previous methods and the size of our 2-hop labeling can be comparable to the existing 2-hop labeling.

A graph is called Hamiltonian connected if it contains a Hamiltonian path between any two distinct vertices. In the past, we proved the Hamiltonian path and cycle problems for general supergrid graphs to be NP-complete. However, they are still open for solid supergrid graphs. In this paper, first we will verify the Hamiltonian cycle property of C-shaped supergrid graphs, which are a special case of solid supergrid graphs. Next, we show that C-shaped supergrid graphs are Hamiltonian connected except in a few conditions. For these excluding conditions of Hamiltonian connectivity, we compute their longest paths. Then, we design a linear-time algorithm to solve the longest path problem in these graphs. The Hamiltonian connectivity of C-shaped supergrid graphs can be applied to compute the optimal stitching trace of computer embroidery machines, and construct the minimum printing trace of 3D printers with a C-like component being printed.

W E use the definitions of (11). However, in deference to some recent attempts to unify the terminology of graph theory we replace the term 'circuit' by 'polygon', and 'degree' by 'valency'. A graph G is 3-connected (nodally 3-connected) if it is simple and non-separable and satisfies the following condition; if G is the union of two proper subgraphs H and K such that HnK consists solely of two vertices u and v, then one of H and K is a link-graph (arc-graph) with ends u and v. It should be noted that the union of two proper subgraphs H and K of G can be the whole of G only if each of H and K includes at least one edge or vertex not belonging to the other. In this paper we are concerned mainly with nodally 3-connected graphs, but a specialization to 3-connected graphs is made in § 12. In § 3 we discuss conditions for a nodally 3-connected graph to be planar, and in § 5 we discuss conditions for the existence of Kuratowski subgraphs of a given graph. In §§ 6-9 we show how to obtain a convex representation of a nodally 3-connected graph, without Kuratowski subgraphs, by solving a set of linear equations. Some extensions of these results to general graphs, with a proof of Kuratowski's theorem, are given in §§ 10-11. In § 12 we discuss the representation in the plane of a pair of dual graphs, and in § 13 we draw attention to some unsolved problems.

Industrial applications tend to rely increasingly on large datasets for regular operations. In order to facilitate that need, we unite the increasingly available hardware resources with fundamental problems found in classical algorithms. We show solutions to the following problems: power flow and island detection in power networks, and the more general graph sparsification. At their core lie respectively algorithms for solving systems of linear equations, graph connectivity and matrix multiplication, and spectral sparsification of graphs, which are applicable on their own to a far greater spectrum of problems. The novelty of our approach lies in developing the first open source and distributed solutions, capable of handling large datasets. Such solutions constitute a toolkit, which, aside from the initial purpose, can be used for the development of unrelated applications and for educational purposes in the study of distributed algorithms.

Sparse recovery can recover sparse signals from a set of underdetermined linear measurements. Motivated by the need to monitor the key characteristics of large-scale networks from a limited number of measurements, this paper addresses the problem of recovering sparse signals in the presence of network topological constraints. Unlike conventional sparse recovery where a measurement can contain any subset of the unknown variables, we use a graph to characterize the topological constraints and allow an additive measurement over nodes (unknown variables) only if they induce a connected subgraph. We provide explicit measurement constructions for several special graphs, and the number of measurements by our construction is less than that needed by existing random constructions. Moreover, our construction for a line network is provably optimal in the sense that it requires the minimum number of measurements. A measurement construction algorithm for general graphs is also proposed and evaluated. For any given graph G with n nodes, we derive bounds of the minimum number of measurements needed to recover any k-sparse vector over G (M_(k,n)^G). Using the Erdõs-Rényi random graph as an example, we characterize the dependence of M_(k,n)^G on the graph structure. This paper suggests that M_(k,n)^G may serve as a graph connectivity metric.

Improved visibility representation of plane graphs

Given distinct vertices s 1,s 2,t 1, and t 2 the 2-vertex-disjoint paths problem (2-VDPP) consists in determining two vertex-disjoint paths p 1, from s 1 to t 1, and p 2, from s 2 to t 2, if such paths exist. We show that by using some kind of sparsification technique the previously best known time bound of O(n + mα(m,n)) can be reduced to O(m + nα(n,n)), where α denotes the inverse of the Ackermann function. Moreover, we extend the very practical and simple algorithm of Hagerup for solving the 2-VDPP on 3-connected planar graphs to a simple linear time algorithm for the 2-VDPP on general planar graphs thereby avoiding the computation of planar embeddings or triconnected components.

We present a new distributed model for graph computations motivated by limited information sharing. Two or more independent entities have collected large social graphs. They wish to compute the result of running graph algorithms on the entire set of relationships. Because the information is sensitive or economically valuable, they do not wish to simply combine the information in a single location. We consider two models for computing the solution to graph algorithms in this setting: 1) limited-sharing: the two entities can share only a poly logarithmic size subgraph, 2) low-trust: the entities must not reveal any information beyond the query answer, assuming they are all honest but curious. We believe this model captures realistic constraints on cooperating autonomous data centres' have results for both models for s-t connectivity, one of the simplest graph problems that requires global information in the worst case. In the limited-sharing model, our results exploit social network structure. Standard communication complexity gives polynomial lower bounds on s-t connectivity for general graphs. However, if the graph for each data centre has a giant component and these giant components intersect, then we can overcome this lower bound, computing-t connectivity while exchanging O(log ^2 n) bits for a constant number of data centers. We can also test the assumption that the giant components overlap using O(log ^2 n) bits provided the (unknown) overlap is sufficiently large. The second result is in the low trust model. We give a secure multi-party computation (MPC) algorithm that 1) does not make cryptographic assumptions when there are 3 or more entities, and 2) is efficient, especially when compared to the usual garbled circuit approach. The entities learn only the yes/no answer. No party learns anything about the others' graph, not even node names. This algorithm does not require any special graph structure. This secure MPC result for s-t connectivity is one of the first that involves a few parties computing on large inputs, instead of many parties computing on a few local values.

Entropy of the ensemble of acyclic graphs

Impaired functional default mode network in patients with mild neurological Wilson’s disease

Accurately representing the structure and manipulation of heap-allocated data structures is a key challenge for automatic verification of nontrivial code. Capturing this information is central to proving properties such as the absence of invalid memory accesses (for instance, deallocating memory twice or dereferencing dangling or NULL pointers), the absence of memory leaks, and data-structure integrity properties (for instance, that a doubly-linked list structure is properly linked in both directions).