Results 41 to 50 of about 121,460 (194)
Learning flexible representations of stochastic processes on graphs
, 2018 Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear ...
Balan, Radu +2 more
core +1 more source
Undirected connectivity of sparse Yao graphs [PDF]
Proceedings of the 7th ACM ACM SIGACT/SIGMOBILE International Workshop on Foundations of Mobile Computing, 2011 Given a finite set S of points in the plane and a real value d > 0, the d-radius disk graph G^d contains all edges connecting pairs of points in S that are within distance d of each other. For a given graph G with vertex set S, the Yao subgraph Y_k[G] with integer parameter k > 0 contains, for each point p in S, a shortest edge pq from G (if any)
Damian, Mirela, Kumbhar, Abhaykumar
openaire +2 more sources
Amplification on Undirected Population Structures: Comets Beat Stars
Scientific Reports, 2017 The fixation probability is the probability that a new mutant introduced in a homogeneous population eventually takes over the entire population.
Andreas Pavlogiannis +3 more
doaj +1 more source
Solving the undirected feedback vertex set problem by local search
, 2014 An undirected graph consists of a set of vertices and a set of undirected edges between vertices. Such a graph may contain an abundant number of cycles, then a feedback vertex set (FVS) is a set of vertices intersecting with each of these cycles ...
Qin, Shao-Meng, Zhou, Hai-Jun
core +1 more source
Steiner Forest Orientation Problems [PDF]
, 2011 We consider connectivity problems with orientation constraints. Given a directed graph $D$ and a collection of ordered node pairs $P$ let $P[D]=\{(u,v) \in P: D {contains a} uv{-path}}$.
Cygan, Marek, Kortsarz, Guy, Nutov, Zeev
core +6 more sources
Structural Pursuit Over Multiple Undirected Graphs [PDF]
Journal of the American Statistical Association, 2014 Gaussian graphical models are useful to analyze and visualize conditional dependence relationships between interacting units. Motivated from network analysis under di erent experimental conditions, such as gene networks for disparate cancer subtypes, we model structural changes over multiple networks with possible heterogeneities.
Yunzhang, Zhu, Xiaotong, Shen, Wei, Pan
openaire +2 more sources
Formation Control of Multi‐Agent System with Local Interaction and Artificial Potential Field
Advanced Robotics Research, EarlyView.This article proposes a local interaction‐based formation control method for Multi‐Agent system, integrating consensus and leader‐follower strategies with a stress response mechanism—artificial potential field to reduce communication overhead and enable obstacle avoidance. Experimental results on triangular, square, and hexagonal formations confirm its
Luoyin Zhao +3 more
wiley +1 more source
Eigenvalue Conditions for Induced Subgraphs
Discussiones Mathematicae Graph Theory, 2015 Necessary conditions for an undirected graph G to contain a graph H as induced subgraph involving the smallest ordinary or the largest normalized Laplacian eigenvalue of G are presented.
Harant Jochen +2 more
doaj +1 more source
Constructing Service Semantic Link Network Based on the Probabilistic Graphical Model [PDF]
International Journal of Computational Intelligence Systems, 2012 Automatic services collaboration calls for the development of semantically structured service network to maximize the utility of Web services. Service Semantic Link Network (S-SLN) is the semantic model for effectively managing Web service resources by ...
Anping Zhao, Yan Ma
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Quantum Experiments and Graphs: Multiparty States as coherent superpositions of Perfect Matchings [PDF]
, 2017 We show a surprising link between experimental setups to realize high-dimensional multipartite quantum states and Graph Theory. In these setups, the paths of photons are identified such that the photon-source information is never created.
Gu, Xuemei +2 more
core +3 more sources