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  • Graph Isomorphism > What kind of Graph is this?

    - by oodavid
    Essentially, this is a variation of Comparing Two Tree Structures, however I do not have "trees", but rather another type of graph. I need to know what kind of Graph I have in order to figure out if there's a Graph Isomorphism Special Case... As you can see, they are: Not Directed Not A Tree Cyclic Max 4 connections But I still don't know the correct terminology, nor the which Isomorphism algorithm to pursue, guidance appreciated.

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  • Subgraph isomorphism on disconnected graphs with connection rules

    - by Mac
    Hello I was wondering if anyone knows about a solution to the following problem: Given a graph g as query and a set of graphs B with connection rules R. The connection rules describe how two graphs out of B can be linked together. Linking points are marked vertexes. Find all combination of graphs in B that contain g as a subgraph. Regards Mac

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  • Algorithms for subgraph isomorphism detection

    - by Jack
    This a NP Complete problem. More info can be found here http://en.wikipedia.org/wiki/Subgraph_isomorphism_problem The most widely used algorithm is the one proposed by Ullman. Can someone please explain the algorithm to me. I read a paper by him and couldn't understand much. Also what other algorithms for this problem. I am working on an image processing project.

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  • What are the most interesting equivalences arising from the Curry-Howard Isomorphism?

    - by Tom
    I came upon the Curry-Howard Isomorphism relatively late in my programming life, and perhaps this contributes to my being utterly fascinated by it. It implies that for every programming concept there exists a precise analogue in formal logic, and vice versa. Here's an "obvious" list of such analogies, off the top of my head: program/definition | proof type/declaration | proposition inhabited type | theorem function | implication function argument | hypothesis/antecedent function result | conclusion/consequent function application | modus ponens recursion | induction identity function | tautology non-terminating function | absurdity tuple | conjunction (and) disjoint union | exclusive disjunction (xor) parametric polymorphism | universal quantification So, to my question: what are some of the more interesting/obscure implications of this isomorphism? I'm no logician so I'm sure I've only scratched the surface with this list. For example, here are some programming notions for which I'm unaware of pithy names in logic: currying | "((a & b) => c) iff (a => (b => c))" scope | "known theory + hypotheses" And here are some logical concepts which I haven't quite pinned down in programming terms: primitive type? | axiom set of valid programs? | theory ? | disjunction (or)

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  • Discuss: PLs are characterised by which (iso)morphisms are implemented

    - by Yttrill
    I am interested to hear discussion of the proposition summarised in the title. As we know programming language constructions admit a vast number of isomorphisms. In some languages in some places in the translation process some of these isomorphisms are implemented, whilst others require code to be written to implement them. For example, in my language Felix, the isomorphism between a type T and a tuple of one element of type T is implemented, meaning the two types are indistinguishable (identical). Similarly, a tuple of N values of the same type is not merely isomorphic to an array, it is an array: the isomorphism is implemented by the compiler. Many other isomorphisms are not implemented for example there is an isomorphism expressed by the following client code: match v with | ((?x,?y),?z = x,(y,z) // Felix match v with | (x,y), - x,(y,z) (* Ocaml *) As another example, a type constructor C of int in Felix may be used directly as a function, whilst in Ocaml you must write a wrapper: let c x = C x Another isomorphism Felix implements is the elimination of unit values, including those in tuples: Felix can do this because (most) polymorphic values are monomorphised which can be done because it is a whole program analyser, Ocaml, for example, cannot do this easily because it supports separate compilation. For the same reason Felix performs type-class dispatch at compile time whilst Haskell passes around dictionaries. There are some quite surprising issues here. For example an array is just a tuple, and tuples can be indexed at run time using a match and returning a value of a corresponding sum type. Indeed, to be correct the index used is in fact a case of unit sum with N summands, rather than an integer. Yet, in a real implementation, if the tuple is an array the index is replaced by an integer with a range check, and the result type is replaced by the common argument type of all the constructors: two isomorphisms are involved here, but they're implemented partly in the compiler translation and partly at run time.

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  • Java graph library for comparing 2 graphs

    - by user311909
    Hello, Does anyone know a good java library for graph comparing by searching maximal common subgraph isomorphism to get information about their similarity? I do not want to compare graphs based on node labels. Or is there any other way how to topologicaly compare graphs with good java library? Now I am using library SimPack and it is usefull but I need something more. Any suggestions will be very helpful. Thanks in advance

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  • Higher-order type constructors and functors in Ocaml

    - by sdcvvc
    Can the following polymorphic functions let id x = x;; let compose f g x = f (g x);; let rec fix f = f (fix f);; (*laziness aside*) be written for types/type constructors or modules/functors? I tried type 'x id = Id of 'x;; type 'f 'g 'x compose = Compose of ('f ('g 'x));; type 'f fix = Fix of ('f (Fix 'f));; for types but it doesn't work. Here's a Haskell version for types: data Id x = Id x data Compose f g x = Compose (f (g x)) data Fix f = Fix (f (Fix f)) -- examples: l = Compose [Just 'a'] :: Compose [] Maybe Char type Natural = Fix Maybe -- natural numbers are fixpoint of Maybe n = Fix (Just (Fix (Just (Fix Nothing)))) :: Natural -- n is 2 -- up to isomorphism composition of identity and f is f: iso :: Compose Id f x -> f x iso (Compose (Id a)) = a

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  • What Precalculus knowledge is required before learning Discrete Math Computer Science topics?

    - by Ein Doofus
    Below I've listed the chapters from a Precalculus book as well as the author recommended Computer Science chapters from a Discrete Mathematics book. Although these chapters are from two specific books on these subjects I believe the topics are generally the same between any Precalc or Discrete Math book. What Precalculus topics should one know before starting these Discrete Math Computer Science topics?: Discrete Mathematics CS Chapters 1.1 Propositional Logic 1.2 Propositional Equivalences 1.3 Predicates and Quantifiers 1.4 Nested Quantifiers 1.5 Rules of Inference 1.6 Introduction to Proofs 1.7 Proof Methods and Strategy 2.1 Sets 2.2 Set Operations 2.3 Functions 2.4 Sequences and Summations 3.1 Algorithms 3.2 The Growths of Functions 3.3 Complexity of Algorithms 3.4 The Integers and Division 3.5 Primes and Greatest Common Divisors 3.6 Integers and Algorithms 3.8 Matrices 4.1 Mathematical Induction 4.2 Strong Induction and Well-Ordering 4.3 Recursive Definitions and Structural Induction 4.4 Recursive Algorithms 4.5 Program Correctness 5.1 The Basics of Counting 5.2 The Pigeonhole Principle 5.3 Permutations and Combinations 5.6 Generating Permutations and Combinations 6.1 An Introduction to Discrete Probability 6.4 Expected Value and Variance 7.1 Recurrence Relations 7.3 Divide-and-Conquer Algorithms and Recurrence Relations 7.5 Inclusion-Exclusion 8.1 Relations and Their Properties 8.2 n-ary Relations and Their Applications 8.3 Representing Relations 8.5 Equivalence Relations 9.1 Graphs and Graph Models 9.2 Graph Terminology and Special Types of Graphs 9.3 Representing Graphs and Graph Isomorphism 9.4 Connectivity 9.5 Euler and Hamilton Ptahs 10.1 Introduction to Trees 10.2 Application of Trees 10.3 Tree Traversal 11.1 Boolean Functions 11.2 Representing Boolean Functions 11.3 Logic Gates 11.4 Minimization of Circuits 12.1 Language and Grammars 12.2 Finite-State Machines with Output 12.3 Finite-State Machines with No Output 12.4 Language Recognition 12.5 Turing Machines Precalculus Chapters R.1 The Real-Number System R.2 Integer Exponents, Scientific Notation, and Order of Operations R.3 Addition, Subtraction, and Multiplication of Polynomials R.4 Factoring R.5 Rational Expressions R.6 Radical Notation and Rational Exponents R.7 The Basics of Equation Solving 1.1 Functions, Graphs, Graphers 1.2 Linear Functions, Slope, and Applications 1.3 Modeling: Data Analysis, Curve Fitting, and Linear Regression 1.4 More on Functions 1.5 Symmetry and Transformations 1.6 Variation and Applications 1.7 Distance, Midpoints, and Circles 2.1 Zeros of Linear Functions and Models 2.2 The Complex Numbers 2.3 Zeros of Quadratic Functions and Models 2.4 Analyzing Graphs of Quadratic Functions 2.5 Modeling: Data Analysis, Curve Fitting, and Quadratic Regression 2.6 Zeros and More Equation Solving 2.7 Solving Inequalities 3.1 Polynomial Functions and Modeling 3.2 Polynomial Division; The Remainder and Factor Theorems 3.3 Theorems about Zeros of Polynomial Functions 3.4 Rational Functions 3.5 Polynomial and Rational Inequalities 4.1 Composite and Inverse Functions 4.2 Exponential Functions and Graphs 4.3 Logarithmic Functions and Graphs 4.4 Properties of Logarithmic Functions 4.5 Solving Exponential and Logarithmic Equations 4.6 Applications and Models: Growth and Decay 5.1 Systems of Equations in Two Variables 5.2 System of Equations in Three Variables 5.3 Matrices and Systems of Equations 5.4 Matrix Operations 5.5 Inverses of Matrices 5.6 System of Inequalities and Linear Programming 5.7 Partial Fractions 6.1 The Parabola 6.2 The Circle and Ellipse 6.3 The Hyperbola 6.4 Nonlinear Systems of Equations

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