Experimental Evaluation of Subgraph Isomorphism Solvers

International audienceSubgraph Isomorphism (SI) is an NP-complete problem which is at the heart of many structural pattern recognition tasks as it involves finding a copy of a pattern graph into a target graph. In the pattern recognition community, the most well-known SI solvers are VF2, VF3, and RI. SI is also widely studied in the constraint programming community, and many constraint-based SI solvers have been proposed since Ullman, such as LAD and Glasgow, for example. All these SI solvers can solve very quickly some large SI instances, that involve graphs with thousands of nodes. However, McCreesh et al. have recently shown how to randomly generate SI instances the hardness of which can be controlled and predicted, and they have built small instances which are computationally challenging for all solvers. They have also shown that some small instances, which are predicted to be easy and are easily solved by constraint-based solvers, appear to be challenging for VF2 and VF3. In this paper, we widen this study by considering a large test suite coming from eight benchmarks. We show that, as expected for an NP-complete problem, the solving time of an instance does not depend on its size, and that some small instances coming from real applications are not solved by any of the considered solvers. We also show that, if RI and VF3 can solve very quickly a large number of easy instances, for which Glasgow or LAD need more time, they fail at solving some other instances that are quickly solved by Glasgow or LAD, and they are clearly outperformed by Glasgow on hard instances. Finally, we show that we can easily combine solvers to take benefit of their complementarity

Between Subgraph Isomorphism and Maximum Common Subgraph

When a small pattern graph does not occur inside a larger target graph, we can ask how to find "as much of the pattern as possible" inside the target graph. In general, this is known as the maximum common subgraph problem, which is much more computationally challenging in practice than subgraph isomorphism. We introduce a restricted alternative, where we ask if all but k vertices from the pattern can be found in the target graph. This allows for the development of slightly weakened forms of certain invariants from subgraph isomorphism which are based upon degree and number of paths. We show that when k is small, weakening the invariants still retains much of their effectiveness. We are then able to solve this problem on the standard problem instances used to benchmark subgraph isomorphism algorithms, despite these instances being too large for current maximum common subgraph algorithms to handle. Finally, by iteratively increasing k, we obtain an algorithm which is also competitive for the maximum common subgraph

Efficient Algorithms for Graph Optimization Problems

A doktori értekezés hatékony algoritmusokat mutat be gráfokon értelmezett nehéz kombinatorikus optimalizálási feladatok megoldására. A kutatás legfontosabb eredményét különböző megoldási módszerekhez kidolgozott javítások jelentik, amelyek magukban foglalnak új heurisztikákat, valamint gráfok és fák speciális reprezentációit is. Az elvégzett elemzések igazolták, hogy a szerző által adott leghatékonyabb algoritmusok az esetek többségében gyorsabbak, illetve jobb eredményeket adnak, mint más elérhető implementációk. A dolgozat első fele hét különböző algoritmust és számos hasznos javítást mutat be a minimális költségű folyam feladatra, amely a legtöbbet vizsgált és alkalmazott gráfoptimalizálási problémák egyike. Az implementációinkat egy átfogó tapasztalati elemzés keretében összehasonlítottuk nyolc másik megoldóprogrammal, köztük a leggyakrabban használt és legelismertebb implementációkkal. A hálózati szimplex algoritmusunk lényegesen hatékonyabbnak és robusztusabbnak bizonyult, mint a módszer más implementációi, továbbá a legtöbb tesztadaton ez az algoritmus a leggyorsabb. A bemutatott költségskálázó algoritmus szintén rendkívül hatékony; nagy méretű ritka gráfokon felülmúlja a hálózati szimplex implementációkat. Az értekezésben tárgyalt másik optimalizálási feladat a legnagyobb közös részgráf probléma. Ezt a feladatot kémiai alkalmazások szempontjából vizsgáltuk. Hatékony heurisztikákat dolgoztunk ki, amelyek jelentősen javítják két megoldási módszer pontosságát és sebességét, valamint kémiailag relevánsabb módon rendelik egymáshoz molekulagráfok atomjait és kötéseit. Az algoritmusainkat összehasonlítottuk két ismert megoldóprogrammal, amelyeknél lényegesen jobb eredményeket sikerült elérnünk. A kifejlesztett implementációk bekerültek a ChemAxon Kft. több szoftvertermékébe, melyek vezető nemzetközi gyógyszercégek használatában állnak. Ezen kívül az értekezés röviden bemutatja a LEMON nevű nyílt forrású C++ gráfoptimalizációs programkönyvtárat, amely magában foglalja a minimális költségű folyam feladatra adott algoritmusokat. Ezek az implementációk nagy mértékben hozzájárultak a programcsomag népszerűségének növekedéséhez

SAT-Based Algorithms for Regular Graph Pattern Matching

Graph matching is a fundamental problem in pattern recognition, with many applications such as software analysis and computational biology. One well-known type of graph matching problem is graph isomorphism, which consists of deciding if two graphs are identical. Despite its usefulness, the properties that one may check using graph isomorphism are rather limited, since it only allows strict equality checks between two graphs. For example, it does not allow one to check complex structural properties such as if the target graph is an arbitrary length sequence followed by an arbitrary size loop. We propose a generalization of graph isomorphism that allows one to check such properties through a declarative specification. This specification is given in the form of a Regular Graph Pattern (ReGaP), a special type of graph, inspired by regular expressions, that may contain wildcard nodes that represent arbitrary structures such as variable-sized sequences or subgraphs. We propose a SAT-based algorithm for checking if a target graph matches a given ReGaP. We also propose a preprocessing technique for improving the performance of the algorithm and evaluate it through an extensive experimental evaluation on benchmarks from the CodeSearchNet dataset.Comment: Shorter version accepted for publication at AAAI 202

Certifying Correctness for Combinatorial Algorithms : by Using Pseudo-Boolean Reasoning

Over the last decades, dramatic improvements in combinatorialoptimisation algorithms have significantly impacted artificialintelligence, operations research, and other areas. These advances,however, are achieved through highly sophisticated algorithms that aredifficult to verify and prone to implementation errors that can causeincorrect results. A promising approach to detect wrong results is touse certifying algorithms that produce not only the desired output butalso a certificate or proof of correctness of the output. An externaltool can then verify the proof to determine that the given answer isvalid. In the Boolean satisfiability (SAT) community, this concept iswell established in the form of proof logging, which has become thestandard solution for generating trustworthy outputs. The problem isthat there are still some SAT solving techniques for which prooflogging is challenging and not yet used in practice. Additionally,there are many formalisms more expressive than SAT, such as constraintprogramming, various graph problems and maximum satisfiability(MaxSAT), for which efficient proof logging is out of reach forstate-of-the-art techniques.This work develops a new proof system building on the cutting planesproof system and operating on pseudo-Boolean constraints (0-1 linearinequalities). We explain how such machine-verifiable proofs can becreated for various problems, including parity reasoning, symmetry anddominance breaking, constraint programming, subgraph isomorphism andmaximum common subgraph problems, and pseudo-Boolean problems. Weimplement and evaluate the resulting algorithms and a verifier for theproof format, demonstrating that the approach is practical for a widerange of problems. We are optimistic that the proposed proof system issuitable for designing certifying variants of algorithms inpseudo-Boolean optimisation, MaxSAT and beyond