Adjacency list

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

Interconnection network connecting operation-configurable nodes according to one or more levels of adjacency in multiple dimensions of communication in a multi-processor and a neural processor

Abstract: A Wings array system for communicating between nodes using store and load instructions is described. Couplings between nodes are made according to a 1 to N adjacency of connections in each dimension of a G×H matrix of nodes, where G≧N and H≧N and N is a positive odd integer. Also, a 3D Wings neural network processor is described as a 3D G×H×K network of neurons, each neuron with an N×N×N array of synaptic weight values stored in coupled memory nodes, where G≧N, H≧N, K≧N, and N is determined from a 1 to N adjacency of connections used in the G×H×K network. Further, a hexagonal processor array is organized according to an INFORM coordinate system having axes at 60 degree spacing. Nodes communicate on row paths parallel to an FM dimension of communication, column paths parallel to an IO dimension of communication, and diagonal paths parallel to an NR dimension of communication.

Complex Network Analysis of Evolutionary Algorithms Applied to Combinatorial Optimisation Problem

Abstract: This research analyses the development of a complex network in an evolutionary algorithm (EA). The main aim is to evaluate if a complex network is generated in an EA, and how the population can be evaluated when the objective is to optimise an NP-hard combinatorial optimisation problem. The population is evaluated as a complex network over a number of generations, and different attributes such as adjacency graph, minimal cut, degree centrality, closeness centrality, betweenness centrality, k-Clique, k-Club, k-Clan and community graph plots are analysed. From the results, it can be concluded that an EA population does behave like a complex network, and therefore can be analysed as such, in order to obtain information about population development.

Prose and poetry classification and boundary detection using word adjacency network analysis

Abstract: Word adjacency networks constructed from written works reflect differences in the structure of prose and poetry. We present a method to disambiguate prose and poetry by analyzing network parameters of word adjacency networks, such as the clustering coefficient, average path length and average degree. We determine the relevant parameters for disambiguation using linear discriminant analysis (LDA) and the effect size criterion. The accuracy of the method is 74.9 ± 2.9% for the training set and 73.7 ± 6.4% for the test set which are greater than the acceptable classifier requirement of 67.3%. This approach is also useful in locating text boundaries within a single article which falls within a window size where the significant change in clustering coefficient is observed. Results indicate that an optimal window size of 75 words can detect the text boundaries.

Analysis of spectral clustering algorithms for community detection: the general bipartite setting

Abstract: We consider spectral clustering algorithms for community detection under a general bipartite stochastic block model (SBM). A modern spectral clustering algorithm consists of three steps: (1) regularization of an appropriate adjacency or Laplacian matrix (2) a form of spectral truncation and (3) a k-means type algorithm in the reduced spectral domain. We focus on the adjacency-based spectral clustering and for the first step, propose a new data-driven regularization that can restore the concentration of the adjacency matrix even for the sparse networks. This result is based on recent work on regularization of random binary matrices, but avoids using unknown population level parameters, and instead estimates the necessary quantities from the data. We also propose and study a novel variation of the spectral truncation step and show how this variation changes the nature of the misclassification rate in a general SBM. We then show how the consistency results can be extended to models beyond SBMs, such as inhomogeneous random graph models with approximate clusters, including a graphon clustering problem, as well as general sub-Gaussian biclustering. A theme of the paper is providing a better understanding of the analysis of spectral methods for community detection and establishing consistency results, under fairly general clustering models and for a wide regime of degree growths, including sparse cases where the average expected degree grows arbitrarily slowly.