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Results 1 - 10
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496
Finding community structure in networks using the eigenvectors of matrices
, 2006
"... We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible div ..."
Abstract
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Cited by 502 (0 self)
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divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a
Loopy belief propagation for approximate inference: An empirical study. In:
- Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon-limit performanc ..."
Abstract
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Cited by 676 (15 self)
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in a more gen eral setting? We compare the marginals com puted using loopy propagation to the exact ones in four Bayesian network architectures, including two real-world networks: ALARM and QMR. We find that the loopy beliefs of ten converge and when they do, they give a good approximation
Generic Factor-Based Node Marginalization and Edge Sparsification for Pose-Graph SLAM
"... Abstract—This paper reports on a factor-based method for node marginalization in simultaneous localization and mapping (SLAM) pose-graphs. Node marginalization in a pose-graph induces fill-in and leads to computational challenges in performing inference. The proposed method is able to produce a new ..."
Abstract
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Cited by 11 (6 self)
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the proposed method over several realworld SLAM graphs and show that it outperforms other stateof-the-art methods in terms of Kullback-Leibler divergence. I.
Computing communities in large networks using random walks
- J. of Graph Alg. and App. bf
, 2004
"... Dense subgraphs of sparse graphs (communities), which appear in most real-world complex networks, play an important role in many contexts. Computing them however is generally expensive. We propose here a measure of similarities between vertices based on random walks which has several important advan ..."
Abstract
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Cited by 226 (3 self)
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Dense subgraphs of sparse graphs (communities), which appear in most real-world complex networks, play an important role in many contexts. Computing them however is generally expensive. We propose here a measure of similarities between vertices based on random walks which has several important
Monocular SLAM as a Graph of Coalesced Observations
"... We present a monocular SLAM system that avoids inconsistency by coalescing observations into independent local coordinate frames, building a graph of the local frames, and optimizing the resulting graph. We choose coordinates that minimize the nonlinearity of the updates in the nodes, and suggest a ..."
Abstract
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Cited by 38 (3 self)
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We present a monocular SLAM system that avoids inconsistency by coalescing observations into independent local coordinate frames, building a graph of the local frames, and optimizing the resulting graph. We choose coordinates that minimize the nonlinearity of the updates in the nodes, and suggest a
Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters
, 2008
"... A large body of work has been devoted to defining and identifying clusters or communities in social and information networks, i.e., in graphs in which the nodes represent underlying social entities and the edges represent some sort of interaction between pairs of nodes. Most such research begins wit ..."
Abstract
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Cited by 208 (17 self)
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and information networks, and we come to several striking conclusions. Rather than defining a procedure to extract sets of nodes from a graph and then attempt to interpret these sets as a “real ” communities, we employ approximation algorithms for the graph partitioning problem to characterize as a function
PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs
"... Large-scale graph-structured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graph-parallel abstractions including Pregel and GraphLab. However, the natural graphs commonly found in the real-world have highly ..."
Abstract
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Cited by 128 (4 self)
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Large-scale graph-structured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graph-parallel abstractions including Pregel and GraphLab. However, the natural graphs commonly found in the real-world have highly
A BRANCH-AND-CUT ALGORITHM FOR THE RESOLUTION OF LARGE-SCALE SYMMETRIC TRAVELING SALESMAN PROBLEMS
, 1991
"... An algorithm is described for solving large-scale instances of the Symmetric Traveling Salesman Problem (STSP) to optimality. The core of the algorithm is a "polyhedral" cutting-plane procedure that exploits a subset of the system of linear inequalities defining the convex hull of the in ..."
Abstract
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Cited by 205 (7 self)
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of the incidence vectors of the hamiltonian cycles of a complete graph. The cuts are generated by several identification procedures that have been described in a companion paper. Whenever the cutting-plane procedure does not terminate with an optimal solution the algorithm uses a treesearch strategy that
Scale Drift-Aware Large Scale Monocular SLAM
"... Abstract—State of the art visual SLAM systems have recently been presented which are capable of accurate, large-scale and real-time performance, but most of these require stereo vision. Important application areas in robotics and beyond open up if similar performance can be demonstrated using monocu ..."
Abstract
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Cited by 65 (4 self)
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the system’s new image processing front-end which is able accurately to track hundreds of features per frame, and a filter-based approach for feature initialisation within keyframe-based SLAM. Our approach is proven via large-scale simulation and real-world experiments where a camera completes large looped
Conservative Edge Sparsification for Graph SLAM Node Removal
"... Abstract—This paper reports on optimization-based methods for producing a sparse, conservative approximation of the dense potentials induced by node marginalization in simultaneous localization and mapping (SLAM) factor graphs. The proposed methods start with a sparse, but overconfident, Chow-Liu tr ..."
Abstract
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Cited by 3 (3 self)
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Abstract—This paper reports on optimization-based methods for producing a sparse, conservative approximation of the dense potentials induced by node marginalization in simultaneous localization and mapping (SLAM) factor graphs. The proposed methods start with a sparse, but overconfident, Chow
Results 1 - 10
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496
