Adjacency list

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

Spectra of graph operations based on R-graph

Abstract: For a regular graph and an arbitrary graph , we determine the adjacency (respectively, Laplacian and signless Laplacian) spectra of four types of graph operations on and involving the R-graph of , obtained from by adding a new vertex for each edge of and joining each new vertex to the end vertices of the corresponding edge. These results are then used to construct infinitely many pairs of adjacency (respectively, Laplacian and signless Laplacian) cospectral graphs.

Exploiting color and topological features for region segmentation with recursive fuzzy C-means

Abstract: In this paper we define a novel approach to image segmentation into regions which focuses on both visual and topological cues, namely color similarity, inclusion and spatial adjacency. Many color clustering algorithms have been proposed in the past for skin lesion images but none exploits explicitly the inclusion properties between regions. Our algorithm is based on a recursive version of fuzzy c-means (FCM) clustering algorithm in the 2D color histogram constructed by Principal Component Analysis (PCA) of the color space. The distinctive feature of the proposal is that recursion is guided by evaluation of adjacency and mutual inclusion properties of extracted regions; then, the recursive analysis addresses only included regions or regions with a not-negligible size. This approach allows a coarse-to-fine segmentation which focuses attention on the inner parts of the images, in order to highlight the internal structure of the object depicted in the image. This could be particularly useful in many applications, especially in biomedical image analysis. In this work we apply the technique to segmentation of skin lesions in dermatoscopic images. It could be a suitable support for diagnosis of skin melanoma, since dermatologists are interested in analysis of spatial relations, symmetrical positions and inclusion of regions.

Resource placement with multiple adjacency constraints in k-ary n-cubes

Abstract: The problem of placing resources in a k-ary n-cube (k>2) is considered in this paper. For a given j/spl ges/1, resources are placed such that each nonresource node is adjacent to j resource nodes. We first prove that perfect j-adjacency placements are impossible in k-ary n-cubes if n >

Force-directed approaches to sensor localization

Abstract: As the number of applications of sensor networks increases, so does the interest in sensor network localization, that is, in recovering the correct position of each node in a network of sensors from partial connectivity information such as adjacency, range, or angle between neighboring nodes. In this article, we consider the anchor-free localization problem in sensor networks that report possibly noisy range information and angular information about the relative order of each sensor's neighbors. Previously proposed techniques seem to successfully reconstruct the original positions of the nodes for relatively small networks with nodes distributed in simple regions. However, these techniques do not scale well with network size and yield poor results with nonconvex or nonsimple underlying topology. Moreover, the distributed nature of the problem makes some of the centralized techniques inapplicable in distributed settings. To address these problems we describe a multiscale dead-reckoning (MSDR) algorithm that scales well for large networks, can reconstruct complex underlying topologies, and is resilient to noise. The MSDR algorithm takes its roots from classic force-directed graph layout computation techniques. These techniques are augmented with a multiscale extension to handle the scalability issue and with a dead-reckoning extension to overcome the problems arising with nonsimple topologies. Furthermore, we show that the distributed version of the MSDR algorithm performs as well as, if not better than, its centralized counterpart, as shown by the quality of the layout, measured in terms of the accuracy of the computed pairwise distances between sensors in the network.

Constructing And Selecting Adjacency Constraints

Abstract: Maintaining spatial integrity is an important concern in both the tactical and operational levels of forestry planning. Spatial relationships are typically represented by adjacency constraints. The number of needed adjacency constraints for even a small number of planning units, if not kept to a minimum, may be too large to include in a mathematical programming formulation. Several approaches have been developed to “minimize” the number of adjacency constraints used. These approaches involve either constraint subset selection or constraint aggregation. We demonstrate that with constraint aggregation the theoretical minimum of necessary adjacency constraints is one. However, the range of coefficients of one aggregated adjacency constraint is impractical for actual application. As an alternative, we explore the approach of identifying a minimal subset of a class of structural adjacency constraints. As a part of this approach, we develop a two-stage procedure to identify and fine tune a minimal subse...