scispace - formally typeset
Search or ask a question
Topic

Adjacency list

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


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, the adjacency, incidence and Laplacian matrices of a complex unit gain graph are studied and eigenvalue bounds for the adjACency matrix are derived.

101 citations

Patent
29 Mar 2004
TL;DR: In this article, a method for providing BGP route updates in MPLS networks is described, where route update is performed at a router having a forwarding information table containing BGP routes and an internal label, and an adjacency table containing IGP/VPN labels and said internal label.
Abstract: A method for providing BGP route updates in an MPLS network is disclosed The route update is performed at a router having a forwarding information table containing BGP routes and an internal label, and an adjacency table containing BGP/VPN labels and said internal label The internal label corresponds to at least one IGP route and has an adjacency associated therewith The method includes updating the adjacency associated with the internal label following an IGP route change

101 citations

Proceedings ArticleDOI
24 Apr 2000
TL;DR: Topology-based maps are proposed as a new representation of the workspace of a mobile robot that captures the structure of the free space in the environment in terms of the basic topological notions of connectivity and adjacency.
Abstract: We propose topology-based maps as a new representation of the workspace of a mobile robot. These maps capture the structure of the free space in the environment in terms of the basic topological notions of connectivity and adjacency. Topology-based maps can be automatically extracted from an occupancy grid built from sensor data using techniques borrowed from the image processing field. Since these techniques can be soundly defined on fuzzy values, our approach is well suited to deal with the uncertainty inherent in the sensor data. Topology-based maps are fairly robust with respect to sensor noise and to small environmental changes, and have nice computational properties.

100 citations

Journal ArticleDOI
TL;DR: A novel adjacency coefficient representation is proposed, which does not only capture the category information between different samples, but also reflects the continuity between similar samples and the similarity betweenDifferent samples.
Abstract: This paper develops a new dimensionality reduction method, named biomimetic uncorrelated locality discriminant projection (BULDP), for face recognition. It is based on unsupervised discriminant projection and two human bionic characteristics: principle of homology continuity and principle of heterogeneous similarity. With these two human bionic characteristics, we propose a novel adjacency coefficient representation, which does not only capture the category information between different samples, but also reflects the continuity between similar samples and the similarity between different samples. By applying this new adjacency coefficient into the unsupervised discriminant projection, it can be shown that we can transform the original data space into an uncorrelated discriminant subspace. A detailed solution of the proposed BULDP is given based on singular value decomposition. Moreover, we also develop a nonlinear version of our BULDP using kernel functions for nonlinear dimensionality reduction. The performance of the proposed algorithms is evaluated and compared with the state-of-the-art methods on four public benchmarks for face recognition. Experimental results show that the proposed BULDP method and its nonlinear version achieve much competitive recognition performance.

100 citations

Proceedings ArticleDOI
23 Apr 2006
TL;DR: This scheme uses no geographic information, makes few assumptions on the network model, and achieves better load balancing and structured data processing and aggregation even for sensor fields with complex geometric shapes and non-trivial topology.
Abstract: For a wide variety of sensor network environments, location information is unavailable or expensive to obtain. We propose a location-free, lightweight, distributed, and data-centric storage/retrieval scheme for information producers and information consumers in sensor networks. Our scheme is built upon the Gradient Landmark-Based Distributed Routing protocol (GLIDER) [8], a two-level routing scheme where sensor nodes are partitioned into tiles by their graph distances to a small set of local landmarks so that localized and efficient routing can be achieved inside and across tiles. Our information storage and retrieval scheme uses two ideas on top of the GLIDER hierarchy — a distributed hash table on the combinatorial tile adjacency graph and a double-ruling scheme within each tile. Queries follow a path that will provably reach the data replicated by the producer(s). We show that this scheme compares favorably with previously proposed schemes, such as Geographic Hash Tables (GHT), providing comparable data storage performance and better locality-aware data retrieval performance. More importantly, this scheme uses no geographic information, makes few assumptions on the network model, and achieves better load balancing and structured data processing and aggregation even for sensor fields with complex geometric shapes and non-trivial topology.

100 citations


Network Information
Related Topics (5)
Optimization problem
96.4K papers, 2.1M citations
82% related
Probabilistic logic
56K papers, 1.3M citations
82% related
Cluster analysis
146.5K papers, 2.9M citations
81% related
Matrix (mathematics)
105.5K papers, 1.9M citations
81% related
Robustness (computer science)
94.7K papers, 1.6M citations
80% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023209
2022439
2021283
2020280
2019296
2018232