Adjacency list

About: Adjacency list is a research topic. Over the lifetime, 4419 publications have been published within this topic receiving 78449 citations.

Hybrid feature recognition method for setup planning from STEP AP-203

Abstract: For seamless automation, computer-aided design and manufacturing activities have to be linked by computer-aided process planning (CAPP). An important subtask in CAPP is setup planning, in which a setup plan must be generated ideally from a given 3-D model of the component. In this paper, a hybrid approach that effectively uses volume subtraction and face adjacency graph is proposed to recognize manufacturing features from 3-D model data in STEP AP-203 format. The proposed feature recognition is generic in nature and is capable of recognizing intersecting features also with relative ease. The manufacturing features are clustered based on preferential base for machining and a setup sequence is obtained by alternative rating and ranking. Finally, locating and clamping for each setup are determined considering intermediate shapes of the workpiece. This setup planning method reduces the number of alternatives for evaluation and thereby the computational effort.

Algorithmic Aspects of Graph Connectivity

Abstract: Algorithmic Aspects of Graph Connectivity is the first comprehensive book on this central notion in graph and network theory, emphasizing its algorithmic aspects. Because of its wide applications in the fields of communication, transportation, and production, graph connectivity has made tremendous algorithmic progress under the influence of the theory of complexity and algorithms in modern computer science. The book contains various definitions of connectivity, including edge-connectivity and vertex-connectivity, and their ramifications, as well as related topics such as flows and cuts. The authors comprehensively discuss new concepts and algorithms that allow for quicker and more efficient computing, such as maximum adjacency ordering of vertices. Covering both basic definitions and advanced topics, this book can be used as a textbook in graduate courses in mathematical sciences, such as discrete mathematics, combinatorics, and operations research, and as a reference book for specialists in discrete mathematics and its applications.

On the generalized adjacency, Laplacian and signless Laplacian spectra of the weighted edge corona networks

Abstract: Many problems in real world, either natural or man-made, can be usefully represented by graphs or networks. Along with a complex topological structure, the weight is a vital factor in characterizing some properties of real networks. In this paper, we define a class of the weighted edge corona product networks. The generalized adjacency (resp., Laplacian and signless Laplacian) spectra with two different structures are determined. As applications, the number of spanning trees and Kirchhoff index of the weighted edge corona product networks are computed.

Beyond Link Prediction: Predicting Hyperlinks in Adjacency Space.

Abstract: This paper addresses the hyperlink prediction problem in hypernetworks. Different from the traditional link prediction problem where only pairwise relations are considered as links, our task here is to predict the linkage of multiple nodes, i.e., hyperlink. Each hyperlink is a set of an arbitrary number of nodes which together form a multiway relationship. Hyperlink prediction is challenging – since the cardinality of a hyperlink is variable, existing classifiers based on a fixed number of input features become infeasible. Heuristic methods, such as the common neighbors and Katz index, do not work for hyperlink prediction, since they are restricted to pairwise similarities. In this paper, we formally define the hyperlink prediction problem, and propose a new algorithm called Coordinated Matrix Minimization (CMM), which alternately performs nonnegative matrix factorization and least square matching in the vertex adjacency space of the hypernetwork, in order to infer a subset of candidate hyperlinks that are most suitable to fill the training hypernetwork. We evaluate CMM on two novel tasks: predicting recipes of Chinese food, and finding missing reactions of metabolic networks. Experimental results demonstrate the superior performance of our method over many seemingly promising baselines.