On Testability of First-Order Properties in Bounded-Degree Graphs

The regularity lemma of Szemeredi turned out to be the most powerful tool for studying the testability of graph properties in the dense graph model. In fact, as we argue in this paper, this lemma can be used in order to prove (essentially) all the previous results in this area. More precisely, a barrier for obtaining an efficient testing algorithm for a graph property P was having an efficient regularity lemma for graphs satisfying P. The problem is that for many natural graph properties (e.g. triangle freeness) it is known that a graph can satisfy P and still only have regular partitions of tower-type size. This means that there was no viable path for obtaining reasonable bounds on the query complexity of testing such properties. In this paper we consider the property of being induced C_4-free, which also suffers from the fact that a graph might satisfy this property but still have only regular partitions of tower-type size. By developing a new approach for this problem we manage to overcome this barrier and thus obtain a merely exponential bound for testing this property. This is the first substantial progress on a problem raised by Alon in 2001, and more recently by Alon, Conlon and Fox. We thus obtain the first example of an efficient testing algorithm that cannot be derived from an efficient version of the regularity lemma.

Consider property testing on bounded degree graphs and let $\\varepsilon>0$\ndenote the proximity parameter. A remarkable theorem of Newman-Sohler (SICOMP\n2013) asserts that all properties of planar graphs (more generally hyperfinite)\nare testable with query complexity only depending on $\\varepsilon$. Recent\nadvances in testing minor-freeness have proven that all additive and monotone\nproperties of planar graphs can be tested in $poly(\\varepsilon^{-1})$ queries.\nSome properties falling outside this class, such as Hamiltonicity, also have a\nsimilar complexity for planar graphs. Motivated by these results, we ask: can\nall properties of planar graphs can be tested in $poly(\\varepsilon^{-1})$\nqueries? Is there a uniform query complexity upper bound for all planar\nproperties, and what is the "hardest" such property to test? We discover a\nsurprisingly clean and optimal answer. Any property of bounded degree planar\ngraphs can be tested in $\\exp(O(\\varepsilon^{-2}))$ queries. Moreover, there is\na matching lower bound, up to constant factors in the exponent. The natural\nproperty of testing isomorphism to a fixed graph needs\n$\\exp(\\Omega(\\varepsilon^{-2}))$ queries, thereby showing that (up to\npolynomial dependencies) isomorphism to an explicit fixed graph is the hardest\nproperty of planar graphs. The upper bound is a straightforward adapation of\nthe Newman-Sohler analysis that tracks dependencies on $\\varepsilon$ carefully.\nThe main technical contribution is the lower bound construction, which is\nachieved by a special family of planar graphs that are all mutually far from\neach other. We can also apply our techniques to get analogous results for\nbounded treewidth graphs. We prove that all properties of bounded treewidth\ngraphs can be tested in $\\exp(O(\\varepsilon^{-1}\\log \\varepsilon^{-1}))$\nqueries. Moreover, testing isomorphism to a fixed forest requires\n$\\exp(\\Omega(\\varepsilon^{-1}))$ queries.\n

The elimination distance to some target graph property P is a general graph modification parameter introduced by Bulian and Dawar. We initiate the study of elimination distances to graph properties expressible in first-order logic. We delimit the problem’s fixed-parameter tractability by identifying sufficient and necessary conditions on the structure of prefixes of first-order logic formulas. Our main result is the following meta-theorem: For every graph property P expressible by a first order-logic formula \( \varphi \in \Sigma _3 \) , that is, of the form \( \begin{equation*} \varphi =\exists x_1\exists x_2\cdots \exists x_r\ \ \forall y_{1}\forall y_{2}\cdots \forall y_{s}\ \ \exists z_1\exists z_2\cdots \exists z_t~~ \psi ,\end{equation*} \) where \( \psi \) is a quantifier-free first-order formula, checking whether the elimination distance of a graph to P does not exceed \( k \) , is fixed-parameter tractable parameterized by \( k \) . Properties of graphs expressible by formulas from \( \Sigma _3 \) include being of bounded degree, excluding a forbidden subgraph, or containing a bounded dominating set. We complement this theorem by showing that such a general statement does not hold for formulas with even slightly more expressive prefix structure: There are formulas \( \varphi \in \Pi _3 \) , for which computing elimination distance is \( {\sf W}[2] \) -hard.

In this research, we designed a universal graph model. We targeted this work on improving graph data organization representation, flexible formation of graph substructures for complex system modeling and generic processing of data organization. We form coherently composed graph modeling features including inheritance hierarchy of basic graph types, data participating graphs of multiple aspects, hierarchically composed and connected graphs, and property-based graph models based on existing graph models. Design cases are used to illustrate its achievement of our design goal. Keywords—graph theory; graph model, multi-aspect graph; hierarchical graph I. INTRODUCTION Computer modeling plays a key role to apply computer's capability in various fields of applications. Effective support of computer modeling should consist of data representation capability, data access and navigation capability, and generic operations for forming more general and reusable generic algorithms. As computer applications keep growing complex in wider areas, we need enhanced computer models capable of supporting these applications. Graph data models are one of the most extensively used discrete mathematical models in computer modeling of various applications from VLSI circuit design, computer system design and modeling, network modeling, etc. Traditional graph libraries such as LEDA(1), Boost Graph Library(2), LINK(3), and GraphStream(4) focuses on single- level directed and undirected graph modeling. Lee's work(5) utilized decorator and visitor patterns to add new vertex data and attach processing during graph traversal. Pizzonia's (6) employed static object-oriented extension of graph property classes associated with corresponding graph operations. Bertault(7) employed an extensible graph property class hierarchy and dynamic property checking. Directed and undirected hypergraphs are not supported in these approaches. Hyperedges in hypergraphs can be represented as vertices in ordinary graphs. Hypergraphs cannot be treated as super types of directed and undirected graphs to support generic algorithms with ordinary graphs. On data organization point of view, these approaches provide various graph organization types but only as single- level specialized graph type. Each of them should be utilized distinctly. Once these graph models need to be combined in a complex data model, designers should add artificial data structures to join them together. Still, such combined data model or its selected subparts cannot be viewed and utilized as if in a single graph model. This requires designers to add more application-specific gluing data structures, processing codes of distinct graph substructures and their gluing logic, and semantically similar but redundant non-generic codes. It thus hinders effective development and reuse of computer models for various complex applications. From our literature survey, we did not find any previous work providing combinational features for flexibly composing graph structures. Our design goal lies in three improvement directions, representation of complex system data organization, flexible formation of graph substructures for processing, and generic processing of data organization as well as individual data. Our main approach is to form coherently composed graph modeling features including inheritance hierarchy of basic graph types, vertex data participating in graphs of multiple aspects, hierarchically composed and connected graphs, and property-based graph models based on existing graph models. It forms a universal graph model of combinations of complex graph-oriented data organizations, flexibly forms various virtual graph substructures for complex system modeling and processing, and support generic processing capability at the graph structure levels and at the leaf data levels. In section 2, we describe refined design goals and the approach of our work. In the following sections, we in sequence introduce planned composed graph modeling features of our universal graph model, including generic accesses among basic graph models, joining multiple graphs into a multi-aspect graph model, generic structuring of graph models, and generic accesses of property-based graph models. In section 7, we introduce its software design and a design case. The last section concludes this research.

Liveness properties are traditionally proven using a ranking function that maps system states to some well-founded set. Carrying out such proofs in first-order logic enables automation by SMT solvers. However, reasoning about many natural ranking functions is beyond reach of existing solvers. To address this, we introduce the notion of implicit rankings — first-order formulas that soundly approximate the reduction of some ranking function without defining it explicitly. We provide recursive constructors of implicit rankings that can be instantiated and composed to induce a rich family of implicit rankings. Our constructors use quantifiers to approximate reasoning about useful primitives such as cardinalities of sets and unbounded sums that are not directly expressible in first-order logic. We demonstrate the effectiveness of our implicit rankings by verifying liveness properties of several intricate examples, including Dijkstra’s k-state, 4-state and 3-state self-stabilizing protocols.

Subgraph isomorphism, log-bounded fragmentation, and graphs of (locally) bounded treewidth

We study tree languages that can be defined in Δ 2 . These are tree languages definable by a first-order formula whose quantifier prefix is $\exists^*\forall^*$, and simultaneously by a first-order formula whose quantifier prefix is $\forall^*\exists^*$, both formulas over the signature with the descendant relation. We provide an effective characterization of tree languages definable in Δ 2 . This characterization is in terms of algebraic equations. Over words, the class of word languages definable in Δ 2 forms a robust class, which was given an effective algebraic characterization by Pin and Weil [11].

A successor-invariant first-order formula is a formula that has access to an\nauxiliary successor relation on a structure's universe, but the model relation\nis independent of the particular interpretation of this relation. It is well\nknown that successor-invariant formulas are more expressive on finite\nstructures than plain first-order formulas without a successor relation. This\nnaturally raises the question whether this increase in expressive power comes\nat an extra cost to solve the model-checking problem, that is, the problem to\ndecide whether a given structure together with some (and hence every) successor\nrelation is a model of a given formula. It was shown earlier that adding\nsuccessor-invariance to first-order logic essentially comes at no extra cost\nfor the model-checking problem on classes of finite structures whose underlying\nGaifman graph is planar [Engelmann et al., 2012], excludes a fixed minor\n[Eickmeyer et al., 2013] or a fixed topological minor [Eickmeyer and\nKawarabayashi, 2016; Kreutzer et al., 2016]. In this work we show that the\nmodel-checking problem for successor-invariant formulas is fixed-parameter\ntractable on any class of finite structures whose underlying Gaifman graphs\nform a class of bounded expansion. Our result generalises all earlier results\nand comes close to the best tractability results on nowhere dense classes of\ngraphs currently known for plain first-order logic.\n

A successor-invariant first-order formula is a formula that has access to an auxiliary successor relation on a structure's universe, but the model relation is independent of the particular interpretation of this relation. It is well known that successor-invariant formulas are more expressive on finite structures than plain first-order formulas without a successor relation. This naturally raises the question whether this increase in expressive power comes at an extra cost to solve the model-checking problem, that is, the problem to decide whether a given structure together with some (and hence every) successor relation is a model of a given formula. It was shown earlier that adding successor-invariance to first-order logic essentially comes at no extra cost for the model-checking problem on classes of finite structures whose underlying Gaifman graph is planar [1], excludes a fixed minor [2] or a fixed topological minor [3], [4]. In this work we show that the model-checking problem for successor-invariant formulas is fixed-parameter tractable on any class of finite structures whose underlying Gaifman graphs form a class of bounded expansion. Our result generalises all earlier results and comes close to the best tractability results on nowhere dense classes of graphs currently known for plain first-order logic.

We consider the question of whether adaptivity can improve the complexity of property testing algorithms in the dense graphs model. It is known that there can be at most a quadratic gap between adaptive and non-adaptive testers in this model, but it was not known whether any gap indeed exists. In this work we reveal such a gap.Specifically, we focus on the well studied property of bipartiteness. Bogdanov and Trevisan (IEEE Symposium on Computational Complexity, 2004) proved a lower bound of Ω(1/ε 2) on the query complexity of non-adaptive testing algorithms for bipartiteness. This lower bound holds for graphs with maximum degree O(εn). Our main result is an adaptive testing algorithm for bipartiteness of graphs with maximum degree O(εn) whose query complexity is \(\tilde{O}(1/\epsilon^{3/2})\). A slightly modified version of our algorithm can be used to test the combined property of being bipartite and having maximum degree O(εn). Thus we demonstrate that adaptive testers are stronger than non-adaptive testers in the dense graphs model.We note that the upper bound we obtain is tight up-to polylogarithmic factors, in view of the Ω(1/ε 3/2) lower bound of Bogdanov and Trevisan for adaptive testers. In addition we show that \(\tilde{O}(1/\epsilon^{3/2})\) queries also suffice when (almost) all vertices have degree \(\Omega(\sqrt \epsilon \cdot n)\). In this case adaptivity is not necessary.KeywordsQuery ComplexityTesting AlgorithmAdaptive TesterDense GraphNeighbor QueryThese 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.

Access to realistic, complex graph datasets is critical to research on social networking systems and applications. Simulations on graph data provide critical evaluation of new systems and applications ranging from community detection to spam filtering and social web search. Due to the high time and resource costs of gathering real graph datasets through direct measurements, researchers are anonymizing and sharing a small number of valuable datasets with the community. However, performing experiments using shared real datasets faces three key disadvantages: concerns that graphs can be de-anonymized to reveal private information, increasing costs of distributing large datasets, and that a small number of available social graphs limits the statistical confidence in the results.The use of measurement-calibrated graph models is an attractive alternative to sharing datasets. Researchers can fit a graph model to a real social graph, extract a set of model parameters, and use them to generate multiple synthetic graphs statistically similar to the original graph. While numerous graph models have been proposed, it is unclear if they can produce synthetic graphs that accurately match the properties of the original graphs. In this paper, we explore the feasibility of measurement-calibrated synthetic graphs using six popular graph models and a variety of real social graphs gathered from the Facebook social network ranging from 30,000 to 3 million edges. We find that two models consistently produce synthetic graphs with common graph metric values similar to those of the original graphs. However, only one produces high fidelity results in our application-level benchmarks. While this shows that graph models can produce realistic synthetic graphs, it also highlights the fact that current graph metrics remain incomplete, and some applications expose graph properties that do not map to existing metrics.

The goal of property testing is to quickly distinguish between objects which satisfy a property and objects that are ϵ-far from satisfying the property. There are now several general results in this area which show that natural properties of combinatorial objects can be tested with constant query complexity, depending only on ϵ and the property, and not on the size of the object being tested. The upper bound on the query complexity coming from the proof techniques are often enormous and impractical. It remains a major open problem if better bounds hold.Hoppen, Kohayakawa, Moreira, and Sampaio conjectured and Klimosova and Kral' proved that hereditary permutation properties are strongly testable, i.e., can be tested with respect to Kendall's tau distance. The query complexity bound coming from this proof is huge. Even for testing a single forbidden subpermutation it is of Ackermann-type in 1/ϵ. We give a new proof which gives a polynomial bound in 1/ϵ for testing a single forbidden subpermutation.Maybe surprisingly, for testing with respect to the rectangular distance, we prove there is a universal (not depending on the property), polynomial in 1/ϵ query complexity bound for two-sided testing hereditary properties of sufficiently large permutations. We further give a nearly linear bound with respect to a closely related metric which also depends on the smallest forbidden subpermutation for the property. Finally, we show that several different permutation metrics of interest are related to the rectangular distance, yielding similar results for testing with respect to these metrics.

Modern cryptography fundamentally relies on the assumption that the adversary trying to break the scheme is computationally bounded. This assumption lets us construct cryptographic protocols and primitives that are known to be impossible otherwise. In this work we explore the effect of bounding the adversary’s power in other information theoretic proof-systems and show how to use this assumption to bypass impossibility results. We first consider the question of constructing succinct PCPs. These are PCPs whose length is polynomial only in the length of the original NP witness (in contrast to standard PCPs whose length is proportional to the non-deterministic verification time). Unfortunately, succinct PCPs are known to be impossible to construct under standard complexity assumptions. Assuming the sub-exponential hardness of the learning with errors (LWE) problem, we construct succinct probabilistically checkable arguments or PCAs (Kalai and Raz 2009), which are PCPs in which soundness is guaranteed against efficiently generated false proofs. Our PCA construction is for every NP relation that can be verified by a small-depth circuit (e.g., SAT, clique, TSP, etc.) and in contrast to prior work is publicly verifiable and has constant query complexity. Curiously, we also show, as a proof-of-concept, that such publicly-verifiable PCAs can be used to derive hardness of approximation results. Second, we consider the notion of Instance Compression (Harnik and Naor, 2006). An instance compression scheme lets one compress, for example, a CNF formula φ on m variables and n ≫ m clauses to a new formula φ' with only poly(m) clauses, so that φ is satisfiable if and only if φ' is satisfiable. Instance compression has been shown to be closely related to succinct PCPs and is similarly highly unlikely to exist. We introduce a computational analog of instance compression in which we require that if φ is unsatisfiable then φ' is effectively unsatisfiable, in the sense that it is computationally infeasible to find a satisfying assignment for φ' (although such an assignment may exist). Assuming the same sub-exponential LWE assumption, we construct such computational instance compression schemes for every bounded-depth NP relation. As an application, this lets one compress k formulas ϕ₁,… ,ϕ_k into a single short formula ϕ that is effectively satisfiable if and only if at least one of the original formulas was satisfiable.

We propose a general model for testing graph properties, which extends and simplifies the bounded degree model of Goldreich and Ron [Property Testing in Bounded Degree Graphs, Proc. 31st Annual ACM Symposium on the Theory of Computing, 1997, pp. 406–415.] In this model, we present a family of algorithms that test whether the diameter of a graph is bounded by a given parameterD, or is ϵ‐far from any graph with diameter at most β(D). The function β(D) ranges betweenD+4 and 4D+2, depending on the algorithm. All our algorithms run in time polynomial in 1/ϵ. © 2002 Wiley Periodicals, Inc. Random Struct. Alg. 20: 165–183, 2002

We consider various extensions of first-order logic. Informally, a logic 𝓛 is an extension of first-order logic if every sentence of first-order logic is also a sentence of 𝓛. We also require that 𝓛 is closed under conjunction and negation and has other basic properties of a logic. In Section 9.4, we list the properties that formally define the notion of an extension of first-order logic. Prior to Section 9.4, we provide various natural examples of such extensions. In Sections 9.1–9.3, we consider, respectively, second-order logic, infinitary logics, and logics with fixed-point operators. We do not provide a thorough treatment of any one of these logics. Indeed, we could easily devote an entire chapter to each. Rather, we define each logic and provide examples that demonstrate the expressive power of the logics. In particular, we show that none of these logics has compactness. In the final Section 9.4, we prove that if a proper extension of first-order logic has compactness, then the Downward Löwenhiem–Skolem theorem must fail for that logic. This is Lindstrom’s theorem. The Compactness theorem and Downward Löwenheim–Skolem theorem are two crucial results for model theory. Every property of first-order logic from Chapter 4 is a consequence of these two theorems. Lindström’s theorem implies that the only extension of first-order logic possessing these properties is first-order logic itself. Second-order logic is the extension of first-order logic that allows quantification of relations. The symbols of second-order logic are the same symbols used in first-order logic. The syntax of second-order logic is defined by adding one rule to the syntax of first-order logic. The additional rule makes second-order logic far more expressive than first-order logic. Specifically, the syntax of second-order logic is defined as follows. Any atomic first-order formula is a formula of second-order logic. Moreover, we have the following four rules: (R1) If φ is a formula then so is ￢φ. (R2) If φ and ψ are formulas then so is φ ∧ ψ. (R3) If φ is a formula, then so is ∃x φ for any variable x.