Graph data is ubiquitous: Social networks, Semantic Web, pointer analysis in software engineering, and biological and chemical networks all rely on a graph representation of data. This paper makes the case for a native storage layer for graph data, rather than relying on relational or columnar stores. We propose a lightweight storage manager for graph data called G-Store. It exploits the struct...

Learning representation for graph classification turns a variable-size graph into a fixed-size vector (or matrix). Such a representation works nicely with algebraic manipulations. Here we introduce a simple method to augment an attributed graph with a virtual node that is bidirectionally connected to all existing nodes. The virtual node represents the latent aspects of the graph, which are not ...

Graphs are used in various application areas such as chemical, social or shareholder network analysis. Finding relevant graphs in large graph databases is thereby an important problem. Such search starts with the definition of the query object. Defining the query graph quickly and effectively so that it matches meaningful data in the database is difficult. In this paper, we introduce a system, ...

Similarity of graphs with labeled vertices and edges is naturally defined in terms of maximal common subgraphs. To avoid computation overload, a parameterized technique for approximation of graphs and their similarity is used. A lattice-based method of binarizing labeled graphs that respects the similarity operation on graph sets is proposed. This method allows one to compute graph similarity b...

Resource description framework (RDF) and Property Graph databases are emerging technologies that are used for storing graph-structured data. We compare these technologies through a molecular biology use case: glycan substructure search. Glycans are branched tree-like molecules composed of building blocks linked together by chemical bonds. The molecular structure of a glycan can be encoded into ...

The Merrifield-Simmons index of a graph is defined as the total number of the independent sets of the graph and the Hosoya index of a graph is defined as the total number of the matchings of the graph. In this paper, we give formula for Merrifield-Simmons and Hosoya indices of some classes of cartesian product of two graphs K{_2}×H, where H is a path graph P{_n}, cyclic graph C{_n}, or star gra...

Effective search for graph automorphisms allows identifying symmetries in many discrete structures, ranging from chemical molecules to microprocessor circuits. Using this type of structure can enhance visualization as well as speed up computational optimization and verification. Competitive algorithms for the graph automorphism problem are based on efficient partition refinement augmented with ...

Modeling molecules as undirected graphs and chemical reactions as graph rewriting operations is a natural and convenient approach to modeling chemistry. Graph grammar rules are most naturally employed to model elementary reactions like merging, splitting, and isomerisation of molecules. It is often convenient, in particular in the analysis of larger systems, to summarize several subsequent reac...

A general mathematical description, mostly in terms of graph theory, is given for reactions of organic chemistry. The corresponding computer program generates all products that can result from a given set of starting materials interacting according to a given set of reaction schemes. Example reactions from combinatorial chemistry, synthetic organic chemistry, and mass spectroscopic structure el...

A neural graph matcher based on Correlation Matrix Memories is evaluated in terms of efficiency and effectiveness against two maximum common subgraph (mcs) algorithms. The algorithm removes implausible solutions below a user-defined threshold and runs faster than conventional mcs methods on our database of chemical graphs while being slightly less effective.