Adjacency list

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

Parallel greedy graph matching using an edge partitioning approach

Abstract: We present a parallel version of the Karp-Sipser graph matching heuristic for the maximum cardinality problem. It is bulk-synchronous, separating computation and communication, and uses an edge-based partitioning of the graph, translated from a two-dimensional partitioning of the corresponding adjacency matrix. It is shown that the communication volume of Karp-Sipser graph matching is proportional to that of parallel sparse matrix-vector multiplication (SpMV), so that efficient partitioners developed for SpMV can be used. The algorithm is presented using a small basic set of 7 message types, which are discussed in detail. Experimental results show that for most matrices, edge-based partitioning is superior to vertex-based partitioning, in terms of both parallel speedup and matching quality. Good speedups are obtained on up to 64 processors.

A Modified DBSCAN Clustering Method to Estimate Retail Center Extent

Abstract: This research introduces a new method for the identification of local retail agglomerations within Great Britain, implementing a modification of the established density based spatial clustering of applications with noise (DBSCAN) method that improves local sensitivity to variable point densities The variability of retail unit density can be related to both the type and function of retail centers, but also to characteristics such as size and extent of urban areas, population distribution, or property values The suggested method implements a sparse graph representation of the retail unit locations based on a distance-constrained k-nearest neighbor adjacency list that is subsequently decomposed using the Depth First Search algorithm DBSCAN is iteratively applied to each subgraph to extract the clusters with point density closer to an overall density for each study area This innovative approach has the advantage of adjusting the radius parameter of DBSCAN at the local scale, thus improving the clustering output A comparison of the estimated retail clusters against a sample of existing boundaries of retail areas shows that the suggested methodology provides a simple yet accurate and flexible way to automate the process of identifying retail clusters of varying shapes and densities across large areas; and by extension, enables their automated update over time

MB3-Miner: mining eMBedded subTREEs using Tree Model Guided candidate generation

Abstract: Tree mining has many useful applications in areas such as Bioinformatics, XML mining, Web mining, etc. In general, most of the formally represented information in these domains is a tree structured form. In this paper we focus on mining frequent embedded subtrees from databases of rooted labeled ordered subtrees. We propose a novel and unique embedding list representation that is suitable for describing embedded subtrees. This representation is completely different from the string-like or conventional adjacency list representation previously utilized for trees. We present the mathematical model of a breadth-first-search Tree Model Guided (TMG) candidate generation approach previously introduced in [8]. The key characteristic of the TMG approach is that it enumerates fewer candidates by ensuring that only valid candidates that conform to the structural aspects of the data are generated as opposed to the join approach. Our experiments with both synthetic and real-life datasets provide comparisons against one of the state-of-the-art algorithms, TreeMiner [15], and they demonstrate the effectiveness and the efficiency of the technique.