A survey of graph theoretic computer program complexity measures

A computer program complexity measure is a measure of how easy the program is to understand, test, modify, maintain, etc. Many of these measures are derived from the control or flow graph of the program. We describe these measures graph theoretically, indicate what aspect or aspects of the program they measure and compare their strengths and weaknesses.KEYWORDS Graph theory, psychological complexity, complexity measures, flow graph, program graph, call grap

The repulsive lattice gas, the independent-set polynomial, and the Lov\'asz local lemma

We elucidate the close connection between the repulsive lattice gas in equilibrium statistical mechanics and the Lovasz local lemma in probabilistic combinatorics. We show that the conclusion of the Lovasz local lemma holds for dependency graph G and probabilities {p_x} if and only if the independent-set polynomial for G is nonvanishing in the polydisc of radii {p_x}. Furthermore, we show that the usual proof of the Lovasz local lemma -- which provides a sufficient condition for this to occur -- corresponds to a simple inductive argument for the nonvanishing of the independent-set polynomial in a polydisc, which was discovered implicitly by Shearer and explicitly by Dobrushin. We also present some refinements and extensions of both arguments, including a generalization of the Lovasz local lemma that allows for "soft" dependencies. In addition, we prove some general properties of the partition function of a repulsive lattice gas, most of which are consequences of the alternating-sign property for the Mayer coefficients. We conclude with a brief discussion of the repulsive lattice gas on countably infinite graphs.Comment: LaTex2e, 97 pages. Version 2 makes slight changes to improve clarity. To be published in J. Stat. Phy

On the Expressivity of Persistent Homology in Graph Learning

Persistent homology, a technique from computational topology, has recently shown strong empirical performance in the context of graph classification. Being able to capture long range graph properties via higher-order topological features, such as cycles of arbitrary length, in combination with multi-scale topological descriptors, has improved predictive performance for data sets with prominent topological structures, such as molecules. At the same time, the theoretical properties of persistent homology have not been formally assessed in this context. This paper intends to bridge the gap between computational topology and graph machine learning by providing a brief introduction to persistent homology in the context of graphs, as well as a theoretical discussion and empirical analysis of its expressivity for graph learning tasks

Is the five-flow conjecture almost false?

The number of nowhere zero Z_Q flows on a graph G can be shown to be a polynomial in Q, defining the flow polynomial \Phi_G(Q). According to Tutte's five-flow conjecture, \Phi_G(5) > 0 for any bridgeless G.A conjecture by Welsh that \Phi_G(Q) has no real roots for Q \in (4,\infty) was recently disproved by Haggard, Pearce and Royle. These authors conjectured the absence of roots for Q \in [5,\infty). We study the real roots of \Phi_G(Q) for a family of non-planar cubic graphs known as generalised Petersen graphs G(m,k). We show that the modified conjecture on real flow roots is also false, by exhibiting infinitely many real flow roots Q>5 within the class G(nk,k). In particular, we compute explicitly the flow polynomial of G(119,7), showing that it has real roots at Q\approx 5.0000197675 and Q\approx 5.1653424423. We moreover prove that the graph families G(6n,6) and G(7n,7) possess real flow roots that accumulate at Q=5 as n\to\infty (in the latter case from above and below); and that Q_c(7)\approx 5.2352605291 is an accumulation point of real zeros of the flow polynomials for G(7n,7) as n\to\infty.Comment: 44 pages (LaTeX2e). Includes tex file, three sty files, and a mathematica script polyG119_7.m. Many improvements from version 3, in particular Sections 3 and 4 have been mostly re-writen, and Sections 7 and 8 have been eliminated. (This material can now be found in arXiv:1303.5210.) Final version published in J. Combin. Theory

Generator-Composition for Aspect-Oriented Domain-Specific Languages

Software systems are complex, as they must cover a diverse set of requirements describing functionality and the environment. Software engineering addresses this complexity with Model-Driven Engineering ( MDE ). MDE utilizes different models and metamodels to specify views and aspects of a software system. Subsequently, these models must be transformed into code and other artifacts, which is performed by generators. Information systems and embedded systems are often used over decades. Over time, they must be modified and extended to fulfill new and changed requirements. These alterations can be triggered by the modeling domain and by technology changes in both the platform and programming languages. In MDE these alterations result in changes of syntax and semantics of metamodels, and subsequently of generator implementations. In MDE, generators can become complex software applications. Their complexity depends on the semantics of source and target metamodels, and the number of involved metamodels. Changes to metamodels and their semantics require generator modifications and can cause architecture and code degradation. This can result in errors in the generator, which have a negative effect on development costs and time. Furthermore, these errors can reduce quality and increase costs in projects utilizing the generator. Therefore, we propose the generator construction and evolution approach GECO, which supports decoupling of generator components and their modularization. GECO comprises three contributions: (a) a method for metamodel partitioning into views, aspects, and base models together with partitioning along semantic boundaries, (b) a generator composition approach utilizing megamodel patterns for generator fragments, which are generators depending on only one source and one target metamodel, (c) an approach to modularize fragments along metamodel semantics and fragment functionality. All three contributions together support modularization and evolvability of generators

Metabolic Network Alignments and their Applications

The accumulation of high-throughput genomic and proteomic data allows for the reconstruction of the increasingly large and complex metabolic networks. In order to analyze the accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks. But even finding similar networks becomes computationally challenging. The dissertation addresses these challenges with discrete optimization and the corresponding algorithmic techniques. Based on the property of the gene duplication and function sharing in biological network,we have formulated the network alignment problem which asks the optimal vertex-to-vertex mapping allowing path contraction, vertex deletion, and vertex insertions. We have proposed the first polynomial time algorithm for aligning an acyclic metabolic pattern pathway with an arbitrary metabolic network. We also have proposed a polynomial-time algorithm for patterns with small treewidth and implemented it for series-parallel patterns which are commonly found among metabolic networks. We have developed the metabolic network alignment tool for free public use. We have performed pairwise mapping of all pathways among five organisms and found a set of statistically significant pathway similarities. We also have applied the network alignment to identifying inconsistency, inferring missing enzymes, and finding potential candidates

Proceedings of the 1994 Monterey Workshop, Increasing the Practical Impact of Formal Methods for Computer-Aided Software Development: Evolution Control for Large Software Systems Techniques for Integrating Software Development Environments

Office of Naval Research, Advanced Research Projects Agency, Air Force Office of Scientific Research, Army Research Office, Naval Postgraduate School, National Science Foundatio