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Adaptive graph convolutional clustering network with optimal probabilistic graph
Neural Networks, 2022The graph convolutional network (GCN)-based clustering approaches have achieved the impressive performance due to strong ability of exploiting the topological structure. The adjacency graph seriously affects the clustering performance, especially for non-graph data.
Jiayi, Zhao +5 more
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The Optimal Partitioning of Graphs
SIAM Journal on Applied Mathematics, 1976The problem considered in this paper is that of partitioning a link-weighted graph G into two parts, each of which is constrained in size by the (given) maximum number of vertices that the part can contain. This is a special case of the general partitioning problem of a graph into k parts with size constraints, which appears in a number of very diverse
Christofides, Nicos, Brooker, P.
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Measurement science and technology, 2021
Polarized skylight is as fundamental a constituent of passive navigation as the geomagnetic field. With regard to its applicability to outdoor robot localization, a polarized light-aided visual-inertial navigation system (VINS) modelization dedicated to ...
Linlin Xia +3 more
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Polarized skylight is as fundamental a constituent of passive navigation as the geomagnetic field. With regard to its applicability to outdoor robot localization, a polarized light-aided visual-inertial navigation system (VINS) modelization dedicated to ...
Linlin Xia +3 more
semanticscholar +1 more source
Optimizing Bull-Free Perfect Graphs
SIAM Journal on Discrete Mathematics, 2004Summary: A bull is a graph with five vertices \(a,b,c,d,e\) and five edges \(ab, ac, bc, da, eb\). Here we present polynomial-time combinatorial algorithms for the optimal weighted coloring and weighted clique problems in bull-free perfect graphs. The algorithms are based on a structural analysis and decomposition of bull-free perfect graphs.
de Figueiredo, Celina M. H. +1 more
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Optimizing DNN computation graph using graph substitutions
Proceedings of the VLDB Endowment, 2020Deep learning has achieved great success in various real-world applications. As deep neural networks (DNNs) are getting larger, the inference and training cost of DNNs increases significantly. Since one round of inference or one iteration in the training phase of a DNN is typically modeled as a computation graph, existing works propose to optimize ...
Jingzhi Fang +3 more
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Optimal Graph Search with Iterated Graph Cuts
Proceedings of the AAAI Conference on Artificial Intelligence, 2011Informed search algorithms such as A* use heuristics to focus exploration on states with low total path cost. To the extent that heuristics underestimate forward costs, a wider cost radius of suboptimal states will be explored. For many weighted graphs, however, a small distance in terms of cost may encompass a large fraction of the ...
David Burkett, David Hall, Dan Klein
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Unsupervised and Semisupervised Projection With Graph Optimization
IEEE Transactions on Neural Networks and Learning Systems, 2020Graph-based technique is widely used in projection, clustering, and classification tasks. In this article, we propose a novel and solid framework, named unsupervised projection with graph optimization (UPGO), for both dimensionality reduction and ...
F. Nie, Xia Dong, Xuelong Li
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Optimal constrained graph exploration
ACM Transactions on Algorithms, 2006We address the problem of constrained exploration of an unknown graph G = ( V , E ) from a given start node s with either a tethered robot or a robot with a fuel tank of limited capacity, the former being a tighter constraint. In both variations
Christian A. Duncan +2 more
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2016
The matching problem is an important combinatorial problem defined on a graph. Graphs provide very often a pictorial representation of the mathematical structure underlying combinatorial optimization problems. On the other hand, graph theory is by itself rich of elegant results that can give us useful insights on many combinatorial and physical ...
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The matching problem is an important combinatorial problem defined on a graph. Graphs provide very often a pictorial representation of the mathematical structure underlying combinatorial optimization problems. On the other hand, graph theory is by itself rich of elegant results that can give us useful insights on many combinatorial and physical ...
openaire +1 more source