#Deltagraph windows verification#
#Deltagraph windows code#

Indexing strategies extract graph features at a sufficient granularity level for performing a powerful filtering step.

#Deltagraph windows verification#

The expensive verification phase is then performed only on the subset of promising targets. Filter-and-verification methods that use indexing approaches provide a fast pruning of target graphs or parts of them that do not contain the query. Indexing techniques are also exploited in the context of searching in a collection of target graphs, referred to as one-to-many subgraph problem. Heuristics and indexing techniques can be applied to facilitate the search. The problem is referred to as one-to-one subgraph search, and it is known to be NP-complete. A common task is the search of one substructure within one graph, called target. On top of these representations, several analyses are performed. Graphs are mathematical structures widely used for expressing relationships among elements when representing biomedical and biological information.

Our experiments show that the computation time increased exponentially as the distance threshold increased, but increased sublinearly with the number of graphs in the database. With a distance threshold of zero, our algorithm is equivalent to an existing efficient algorithm for exact supergraph search.

#Deltagraph windows code#

Our algorithm represents the set of candidate subgraphs by a code tree, which it uses to efficiently compute edit distance. The problem is to identify all graphs in a database that are similar to any subgraph of a query graph, where similarity is defined as edit distance. We propose a novel problem, called similar supergraph search, and design an efficient algorithm to solve it. However, the existing supergraph search methods can only discover compounds that match exactly. Represented as graphs, such compounds are similar to a subgraph of favipiravir. For example, the chemical structure of favipiravir-an antiviral treatment for influenza-resembles the structure of some components of RNA. Subgraph and supergraph search methods are promising techniques for the development of new drugs. Experiments show that our approach outperforms state-of-the-art subgraph search and subgraph matching algorithms by up to several orders of magnitude with respect to query processing time. These techniques for subgraph search also lead to an improved algorithm for subgraph matching. In this paper, we propose a new subgraph search algorithm using equivalences of vertices in order to reduce search space: (1) static equivalence of vertices in a query graph that leads to an efficient matching order of the vertices and (2) dynamic equivalence of candidate vertices in a data graph, which enables us to capture and remove redundancies in search space. Despite the efforts, existing algorithms showed limited running time and scalability in dealing with large and/or many graphs. A lot of efforts have been made to develop practical solutions for these problems. Subgraph query processing (also known as subgraph search) and subgraph matching are fundamental graph problems in many application domains. Our experimental studies demonstrate that (Tree+Вў) has a compact index structure, achieves an order of magnitude better performance in index construc- tion, and most importantly, outperforms up-to-date graph- based indexing methods: gIndex and C-Tree, in graph con- tainment query processing. It has two im- plications: (1) the index construction by (Tree+Вў) is ef- ficient, and (2) the graph containment query processing by (Tree+Вў) is efficient. Our study verifies that (Tree+Вў) is a better choice than graph for indexing purpose, denoted (Tree+Вў ВёGraph), to ad- dress the graph containment query problem. In order to achieve better pruning ability than existing graph-based indexing meth- ods, we select, in addition to frequent tree-features (Tree), a small number of discriminative graphs (Вў) on demand, without a costly graph mining process beforehand. We analyze the effectiveness and efficiency of tree as indexing feature from three critical aspects: feature size, feature se- lection cost, and pruning power. In this pa- per, we propose a new cost-effective graph indexing method based on frequent tree-features of the graph database. Due to the vast number of graphs in G and the nature of complexity for subgraph isomorphism testing, it is desirable to make use of high-quality graph indexing mecha- nisms to reduce the overall query processing cost. Given a graph database G, and a query graph q, the graph contain- ment query is to retrieve all graphs in G which contain q as subgraph(s). As a result, it is of special interest to process graph contain- ment queries effectively on large graph databases. Recent scientific and technological advances have witnessed an abundance of structural patterns modeled as graphs.