Graph (abstract data type)

About: Graph (abstract data type) is a research topic. Over the lifetime, 69988 publications have been published within this topic receiving 1218314 citations. The topic is also known as: graph.

Graph-set analysis of hydrogen-bond patterns: some mathematical concepts. Erratum.

Abstract: Owing to a serious printing error, this article [Grell et al. (1999). Acta Cryst. B55, 1030–1043] was printed incorrectly. Pages 1029–1044 have thus been reprinted and included as a loose insert with this issue of the journal.

Machine Learning Approach in Mutation Testing

Abstract: This paper deals with an approach based on the similarity of mutants. This similarity is used to reduce the number of mutants to be executed. In order to calculate such a similarity among mutants their structure is used. Each mutant is converted into a hierarchical graph, which represents the program’s flow, variables and conditions. On the basis of this graph form a special graph kernel is defined to calculate similarity among programs. It is then used to predict whether a given test would detect a mutant or not. The prediction is carried out with the help of a classification algorithm. This approach should help to lower the number of mutants which have to be executed. An experimental validation of this approach is also presented in this paper. An example of a program used in experiments is described and the results obtained, especially classification errors, are presented.

Methods for detecting and remedying missed opportunities in a social network

Abstract: A method for identifying friend relationship in one or more on-line social networks includes creating a graph representing friend relationships among multiple participants of the social networks, in which the nodes of the graph represent the participants and each edge of the graph represents an existing friend relationship in the social networks between two of the participants. The resulting graph is then analyzed using, for example, a graph-theoretical technique to identify pairs of nodes that are unconnected in the graph. A score is then assigned between each identified pair of nodes. The score represents the likelihood that the participants corresponding to the identified pair of nodes are real life friends. The score for each identified pair may be computed based on the connectedness of a subgraph of the graph that includes the identified pair of nodes. One example of such a subgraph is a 4-node subgraph. The score may be computed based on the number of nodes connected to each node in the identified pair, or a variety of factors (e.g., profile information of the participants), in which each factor is provided a weight reflecting the contribution of the factor relative to the other factors. The weights may be adaptive. In one implementation, the graph is built based on collecting subgraphs of friend relationships for each participant, one participant at a time.

A graph theoretic approach to problem formulation for multidisciplinary design analysis and optimization

Abstract: The formulation of multidisciplinary design, analysis, and optimization (MDAO) problems has become increasingly complex as the number of analysis tools and design variables included in typical studies has grown. This growth in the scale and scope of MDAO problems has been motivated by the need to incorporate additional disciplines and to expand the parametric design space to enable the exploration of unconventional design concepts. In this context, given a large set of disciplinary analysis tools, the problem of determining a feasible data flow between tools to produce a specified set of system-level outputs is combinatorially challenging. The difficulty is compounded in multi-fidelity problems, which are of increasing interest to the MDAO community. In this paper, we propose an approach for addressing this problem based on the formalism of graph theory. The approach begins by constructing the maximal connectivity graph (MCG) describing all possible interconnections between a set of analysis tools. Graph operations are then conducted to reduce the MCG to a fundamental problem graph (FPG) that describes the connectivity of analysis tools needed to solve a specified system-level design problem. The FPG does not predispose a particular solution procedure; any relevant MDO solution architecture could be selected to implement the optimization. Finally, the solution architecture can be represented in a problem solution graph (PSG). The graph approach is applied to an example problem based on a commercial aircraft MDAO study.