Approximating Network Centrality Measures Using Node Embedding and
Machine Learning

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Approximating Network Centrality Measures Using Node Embedding and Machine Learning

29 June 2020

Matheus R. F. Mendonça

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Papers citing "Approximating Network Centrality Measures Using Node Embedding and Machine Learning"

11 / 11 papers shown

Title
Machine Learning in Network Centrality Measures: Tutorial and Outlook F. Grando L. Granville Luís C. Lamb 42 56 0 28 Oct 2018
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning Palash Goyal Sujit Rokka Chhetri A. Canedo 77 402 0 07 Sep 2018
Graph Convolutional Neural Networks for Web-Scale Recommender Systems Rex Ying Ruining He Kaifeng Chen Pong Eksombatchai William L. Hamilton J. Leskovec GNN BDL 263 3,540 0 06 Jun 2018
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications Hongyun Cai V. Zheng Kevin Chen-Chuan Chang AI4TS 105 1,793 0 22 Sep 2017
Representation Learning on Graphs: Methods and Applications William L. Hamilton Rex Ying J. Leskovec GNN 174 1,976 0 17 Sep 2017
Graph Convolutional Matrix Completion Rianne van den Berg Thomas Kipf Max Welling GNN 117 1,259 0 07 Jun 2017
Graph Embedding Techniques, Applications, and Performance: A Survey Palash Goyal Emilio Ferrara GNN AI4TS 137 1,727 0 08 May 2017
struc2vec: Learning Node Representations from Structural Identity Leonardo F. R. Ribeiro Pedro H. P. Saverese Daniel R. Figueiredo 70 1,158 0 11 Apr 2017
Learning Combinatorial Optimization Algorithms over Graphs H. Dai Elias Boutros Khalil Yuyu Zhang B. Dilkina Le Song 111 1,469 0 05 Apr 2017
Semi-Supervised Classification with Graph Convolutional Networks Thomas Kipf Max Welling GNN SSL 644 29,076 0 09 Sep 2016
Discriminative Embeddings of Latent Variable Models for Structured Data H. Dai Bo Dai Le Song BDL 122 695 0 17 Mar 2016

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