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Simplicial complexes form an important class of topological spaces that are frequently used in many application areas such as computer-aided design, computer graphics, and simulation.
Laplacian eigenmaps and spectral techniques for embedding and clustering
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Deformation transfer for triangle meshes
Robert W Sumner and Jovan Popović · 2004
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Algebraic topology
Allen Hatcher · 2005
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Survey of graph database models
Renzo Angles and Claudio Gutierrez · 2008
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Distributed large-scale natural graph factorization
Amr Ahmed, Nino Shervashidze, Shravan Narayanamurthy, Vanja Josifovski, and Alexander J Smola · 2013
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Geodesics in heat: A new approach to computing distance based on heat flow
Keenan Crane, Clarisse Weischedel, and Max Wardetzky · 2013
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Efficient estimation of word representations in vector space
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Deep metric learning using triplet network
Elad Hoffer and Nir Ailon · 2015
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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graph2vec: Learning distributed representations of graphs
Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal · 2017
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A survey on network embedding
Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu · 2018
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A simple baseline algorithm for graph classification
Nathan de Lara and Edouard Pineau · 2018
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Visual detection of structural changes in time-varying graphs using persistent homology
Mustafa Hajij, Bei Wang, Carlos Scheidegger, and Paul Rosen · 2018
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Regal: Representation learning-based graph alignment
Mark Heimann, Haoming Shen, Tara Safavi, and Danai Koutra · 2018
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Netlsd: hearing the shape of a graph
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Hierarchical graph representation learning with differentiable pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
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Mesh learning using persistent homology on the laplacian eigenfunctions
Yunhao Zhang, Haowen Liu, Paul Rosen, and Mustafa Hajij · 2019
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Simplicial neural networks
Stefania Ebli, Michaël Defferrard, and Gard Spreemann · 2020
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k-simplex2vec: a simplicial extension of node2vec
Celia Hacker · 2020
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Cell complex neural networks
Mustafa Hajij, Kyle Istvan, and Ghada Zamzmi · 2020
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Fast and scalable complex network descriptor using pagerank and persistent homology
Mustafa Hajij, Elizabeth Munch, and Paul Rosen · 2020
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Unsupervised inductive graph-level representation learning via graph-graph proximity
Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, and Wei Wang · 2019
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Simplex2vec embeddings for community detection in simplicial complexes
Jacob Charles Wright Billings, Mirko Hu, Giulia Lerda, Alexey N Medvedev, Francesco Mottes, Adrian Onicas, Andrea Santoro, and Giovanni Petri · 2019
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Gl2vec: Graph embedding enriched by line graphs with edge features
Hong Chen and Hisashi Koga · 2019
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Nonparametric estimation of probability density functions of random persistence diagrams
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Bayesian topological learning for brain state classification
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Effective learning is accompanied by high-dimensional and efficient representations of neural activity
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Ivan Marin · 2020
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Random walks on simplicial complexes and the normalized hodge 1-laplacian
Michael T Schaub, Austin R Benson, Paul Horn, Gabor Lippner, and Ali Jadbabaie · 2020
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Principled simplicial neural networks for trajectory prediction
Nicholas Glaze, T Mitchell Roddenberry, and Santiago Segarra · 2021
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E R Love, B Filippenko, V Maroulas, and G Carlsson · 2021
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Bayesian Topological Learning for Classifying the Structure of Biological Networks
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