Vertex (graph theory) ≈ Vertex (graph theory)
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Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs Open
A number of problems can be formulated as prediction on graph-structured\ndata. In this work, we generalize the convolution operator from regular grids\nto arbitrary graphs while avoiding the spectral domain, which allows us to\nhandle gra…
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Deep Neural Networks for Learning Graph Representations Open
In this paper, we propose a novel model for learning graph representations, which generates a low-dimensional vector representation for each vertex by capturing the graph structural information. Different from other previous research effor…
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Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection Open
Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts. In this pap…
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GraphGAN: Graph Representation Learning With Generative Adversarial Nets Open
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underl…
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Vertex models: from cell mechanics to tissue morphogenesis Open
Tissue morphogenesis requires the collective, coordinated motion and deformation of a large number of cells. Vertex model simulations for tissue mechanics have been developed to bridge the scales between force generation at the cellular le…
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Performance of<i>b</i>-jet identification in the ATLAS experiment Open
We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Aus…
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Dynamic Hypergraph Neural Networks Open
In recent years, graph/hypergraph-based deep learning methods have attracted much attention from researchers. These deep learning methods take graph/hypergraph structure as prior knowledge in the model. However, hidden and important relati…
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How Bright is the Proton? A Precise Determination of the Photon Parton Distribution Function Open
It has become apparent in recent years that it is important, notably for a range of physics studies at the Large Hadron Collider, to have accurate knowledge on the distribution of photons in the proton. We show how the photon parton distri…
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Revised and improved value of the QED tenth-order electron anomalous magnetic moment Open
In order to improve the theoretical prediction of the electron anomalous magnetic moment $a_e$ we have carried out a new numerical evaluation of the 389 integrals of Set V, which represent 6,354 Feynman vertex diagrams without lepton loops…
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VERSE Open
Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addressed the problem of extracting such embeddings by adopting m…
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GEMSEC Open
Modern graph embedding procedures can efficiently process graphs with millions of nodes. In this paper, we propose GEMSEC – a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their embedding. G…
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CANE: Context-Aware Network Embedding for Relation Modeling Open
Network embedding (NE) is playing a critical role in network analysis, due to its ability to represent vertices with efficient low-dimensional embedding vectors. However, existing NE models aim to learn a fixed context-free embedding for e…
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Landau gauge Yang-Mills correlation functions Open
We investigate Landau gauge $SU(3)$ Yang-Mills theory in a systematic vertex\nexpansion scheme for the effective action with the functional renormalisation\ngroup. Particular focus is put on the dynamical creation of the gluon mass gap\nat…
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A $C^1$ Virtual Element Method for the Cahn--Hilliard Equation with Polygonal Meshes Open
In this paper we develop an evolution of the C1 virtual elements of minimal degree for the approximation of the Cahn-Hilliard equation. The proposed method has the advantage of being conforming in H2 and making use of a very simple set of …
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Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding Open
Network embedding, as an approach to learn low-dimensional representations of vertices, has been proved extremely useful in many applications. Lots of state-of-the-art network embedding methods based on Skip-gram framework are efficient an…
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A Mathematical Bibliography of Signed and Gain Graphs and Allied Areas Open
A signed graph is a graph whose edges are labeled by signs. This is a bibliography of signed graphs and related mathematics.Several kinds of labelled graph have been called "signed" yet are mathematically very different. I distinguish four…
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Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search Open
We present a learning-based approach to computing solutions for certain NP-hard problems. Our approach combines deep learning techniques with useful algorithmic elements from classic heuristics. The central component is a graph convolution…
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Scalable Graph Embedding for Asymmetric Proximity Open
Graph Embedding methods are aimed at mapping each vertex into a low dimensional vector space, which preserves certain structural relationships among the vertices in the original graph. Recently, several works have been proposed to learn em…
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A $c^k n$ 5-Approximation Algorithm for Treewidth Open
We give an algorithm that for an input n-vertex graph G and integer κ > 0, in time 2O(κ)n, either outputs that the treewidth of G is larger than κ, or gives a tree decomposition of G of width at most 5κ + 4. This is the first algorithm pro…
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Learning a SAT Solver from Single-Bit Supervision Open
We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability. Although it is not competitive with state-of-the-art SAT solvers, NeuroSAT can solv…
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Learning Deep Network Representations with Adversarially Regularized Autoencoders Open
The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics…
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Epidemic spreading on complex networks with community structures Open
Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, …
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GraphGAN: Graph Representation Learning with Generative Adversarial Nets Open
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underl…
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Light-front higher-spin theories in flat space Open
We revisit the problem of interactions of higher-spin fields in flat space.\nWe argue that all no-go theorems can be avoided by the light-cone approach,\nwhich results in more interaction vertices as compared to the usual covariant\napproa…
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Identifying optimal targets of network attack by belief propagation Open
For a network formed by nodes and undirected links between pairs of nodes, the network optimal attack problem aims at deleting a minimum number of target nodes to break the network down into many small components. This problem is intrinsic…
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Jet flavor classification in high-energy physics with deep neural networks Open
Classification of jets as originating from light-flavor or heavy-flavor\nquarks is an important task for inferring the nature of particles produced in\nhigh-energy collisions. The large and variable dimensionality of the data\nprovided by …
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Mosaic Open
Processing a one trillion-edge graph has recently been demonstrated by distributed graph engines running on clusters of tens to hundreds of nodes. In this paper, we employ a single heterogeneous machine with fast storage media (e.g., NVMe …
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Vertex algebras at the corner Open
A bstract We introduce a class of Vertex Operator Algebras which arise at junctions of supersymmetric interfaces in $$ \mathcal{N} $$ = 4 Super Yang Mills gauge theory. These vertex algebras satisfy non-trivial duality relations inherited …
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On Sombor Index Open
The concept of Sombor index (SO) was recently introduced by Gutman in the chemical graph theory. It is a vertex-degree-based topological index and is denoted by Sombor index SO: SO=SO(G)=∑vivj∈E(G)dG(vi)2+dG(vj)2, where dG(vi) is the degre…
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ESCAPE Open
Counting the frequency of small subgraphs is a fundamental technique in network analysis across various domains, most notably in bioinformatics and social networks. The special case of triangle counting has received much attention. Getting…