demonstrating the potential of deep learning to dramatically speed up graph layout. The key to our approach are Graph Neural Networks (GNN)14,15, which we use to reparametrize the energy-based optimization prob
A. et al. JEDI-net: a jet identification algorithm based on interaction networks. Eur. Phys. J. C 80, 58 (2020). Article ADS Google Scholar Chakraborty, A., Lim, S. H., Nojiri, M. M. & Takeuchi, M. Neural network-based top tagger with two-point energy correlations and ...
AliGraph: A Comprehensive Graph Neural Network Platform Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou VLDB 2019 Deep Graph Library DGL Team AmpliGraph Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof Euler Al...
Focusing on the problems, we propose a leader-follower flocking algorithm based on a novel reinforcement learning (RL) model. We construct a homogeneous graph neural network (GNN) based multi-agent deep deterministic policy gradient (herein HGNN-MADDPG) algorithm model for multi-agent flocking ...
An algorithm is proposed for matching graphs on the basis of a system constructed with gradient-descent dynamics in quadric potential and a small superposed noise. In our approach a unique absolutely stable attractor corresponding to the solution can be obtained in the phase space by a special ...
On this basis, we provide an incremental learning algorithm to ensure the applicability of our model in streaming graph data. 我们设计了一个生成模型,基于重启随机游走生成节点邻域,并提供理论保证它能够保留图数据的完整分布。在此基础上,我们提出了一种增量学习算法,以确保我们的模型在流图数据中的适用性。
Graph Learning Network: A Structure Learning Algorithm Darwin Saire Pilco, Adín Ramírez Rivera ICML 2019 Workshop Learning Discrete Structures for Graph Neural Networks Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He ICML 2019
1、Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks 作者:Wei-Lin Chiang; Xuanqing Liu; Si Si; Yang Li; Samy Bengio; Cho-Jui Hsieh; 推荐理由:这篇论文提出了一种新的基于图...
larger and larger. How to efficiently train GNN models with billions or even tens of billions of edges at a reasonable cost has become an urgent problem to be solved. We solve this problem by optimizing the memory footprint of a single machine and optimizing the subgraph sampling algorithm. ...
A Dynamic Algorithm for Local Community Detection in Graphs--阅读笔记 community)。 通常算法:在静态图中用贪婪(greedy)、聚集(agglomerative)的算法来求种子集扩展。 新算法:针对不断变化的动态图,提出一种动态种子集扩展的算法,随着底层图形的变化,逐渐更新社区。 Introduction 全局社区检测(Global community detec...