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Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference [article]

Ali Lotfi Rezaabad, Rahi Kalantari, Sriram Vishwanath, Mingyuan Zhou, Jonathan Tamir
2021 arXiv   pre-print
We show that the existing semi-implicit variational inference objective provably reduces information in the observed graph.  ...  To address the naive posterior latent distribution assumptions in classical variational inference, we use semi-implicit hierarchical variational Bayes to implicitly capture posteriors of given graph data  ...  Conclusion We have introduced an enhanced semi-implicit variational auto-encoder for relational graph data with a hyperbolic latent embedding, termed ESI-HGE.  ... 
arXiv:2011.00194v2 fatcat:firbvdui4ve63mmwmeht7kksny

Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations [article]

Joy Hsu, Jeffrey Gu, Gong-Her Wu, Wah Chiu, Serena Yeung
2021 arXiv   pre-print
We propose utilizing a 3D hyperbolic variational autoencoder with a novel gyroplane convolutional layer to map from the embedding space back to 3D images.  ...  To that end, we consider encoder-decoder architectures with a hyperbolic latent space, to explicitly capture hierarchical relationships present in subvolumes of the data.  ...  Low distortion delaunay embedding of trees in hyperbolic plane. In International Symposium on Graph Drawing, pp. 355-366. Springer, 2011. Abraham A Ungar.  ... 
arXiv:2012.01644v3 fatcat:fjwughxgonbzdmxeiiqhuvz26i

Poincaré Wasserstein Autoencoder [article]

Ivan Ovinnikov
2020 arXiv   pre-print
We demonstrate the model in the visual domain to analyze some of its properties and show competitive results on a graph link prediction task.  ...  By assuming the latent space to be hyperbolic, we can use its intrinsic hierarchy to impose structure on the learned latent space representations.  ...  Real world datasets often possess a notion of structure such as object hierarchies within images or implicit graphs.  ... 
arXiv:1901.01427v2 fatcat:3kjpkg77mjbmnjgwh63xgsj6wq

Machine Learning on Graphs: A Model and Comprehensive Taxonomy [article]

Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy
2022 arXiv   pre-print
The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning.  ...  The first, network embedding (such as shallow graph embedding or graph auto-encoders), focuses on learning unsupervised representations of relational structure.  ...  ] , and unsupervised graph embedding with variational inference [62] .  ... 
arXiv:2005.03675v3 fatcat:6eoicgprdvfbze732nsmpaumqe

Hyperbolic Deep Neural Networks: A Survey [article]

Wei Peng, Tuomas Varanka, Abdelrahman Mostafa, Henglin Shi, Guoying Zhao
2021 arXiv   pre-print
Recently, there has been a rising surge of momentum for deep representation learning in hyperbolic spaces due to theirhigh capacity of modeling data like knowledge graphs or synonym hierarchies, possessing  ...  It also presents current applicationsaround various machine learning tasks on several publicly available datasets, together with insightful observations and identifying openquestions and promising future  ...  We also want to thank Emile Mathieu, from University of Oxford, for the explanation regarding the gyroplane layer in their Poincaré Variational Auto-Encoder.  ... 
arXiv:2101.04562v3 fatcat:yqj4zohrqjbplpsdy5f5uglnbu

Dynamic network data exploration through semi-supervised functional embedding

Alexei Pozdnoukhov
2009 Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '09  
The method provides a functional embedding based on a neural network optimizing the graph-based cost function.  ...  The semi-supervised schemes are introduced to guide the method with precisely defined locations, pairwise distances or norms of the selected data samples in the embedded space.  ...  SEMI-SUPERVISED EMBEDDING Sometimes �for better visual perception, for instance), a low-dimensional embedding might need to follow some desired properties to enhance the visualization and at the same time  ... 
doi:10.1145/1653771.1653822 dblp:conf/gis/Pozdnoukhov09 fatcat:uwv4e4ni3vd65gtnhvdnfbxt3e

Expanding Taxonomies with Implicit Edge Semantics

Emaad Manzoor, Rui Li, Dhananjay Shrouty, Jure Leskovec
2020 Proceedings of The Web Conference 2020  
Arborist learns latent representations of the edge semantics along with embeddings of the taxonomy nodes to measure taxonomic relatedness between node pairs.  ...  We also explore the ability of Arborist to infer nodes' taxonomic-roles, without explicit supervision on this task.  ...  Since the embeddings are derived from the given knowledge-graph, they cannot be inferred for unseen nodes or unobserved edge types.  ... 
doi:10.1145/3366423.3380271 dblp:conf/www/ManzoorLSL20 fatcat:omax2qru7bgofctkapferot7gi

To Embed or Not: Network Embedding as a Paradigm in Computational Biology

Walter Nelson, Marinka Zitnik, Bo Wang, Jure Leskovec, Anna Goldenberg, Roded Sharan
2019 Frontiers in Genetics  
In this review, we survey traditional and new approaches for graph embedding and compare their application to fundamental problems in network biology with using the networks directly.  ...  Since biological networks are notoriously complex and hard to decipher, a growing body of work applies graph embedding techniques to simplify, visualize, and facilitate the analysis of the resulting networks  ...  In Fan et al. (2017) , the cross-species embedding is utilized to infer protein function.  ... 
doi:10.3389/fgene.2019.00381 pmid:31118945 pmcid:PMC6504708 fatcat:t4h5izbezrfdbawvvcfutjyzlu

Graph Neural Networks: Taxonomy, Advances and Trends [article]

Yu Zhou, Haixia Zheng, Xin Huang, Shufeng Hao, Dengao Li, Jumin Zhao
2022 arXiv   pre-print
Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic.  ...  First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 400 relevant literatures to show the panorama of the graph neural networks.  ...  Note that the semi-implicit variational inference is employed to approximate the posterior in practice so as to generate the node embedding. GRNNs based on Vanilla RNNs.  ... 
arXiv:2012.08752v3 fatcat:xj2kambrabfj3g5ldenfyixzu4

Recommender systems based on graph embedding techniques: A review

Yue Deng
2022 IEEE Access  
the conventional models can still overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the comparative weakness of graph embedding-based recommendation  ...  In light of that, equipping recommender systems with graph embedding techniques has been widely studied these years, appearing to outperform conventional recommendation implemented directly based on graph  ...  In the same way, in the hyperbolic space Hyper-Know [372] builds an aggregator that can learn hyperbolic attention with Einstein midpoint.  ... 
doi:10.1109/access.2022.3174197 fatcat:s267xaasovh6ffaomi7l32pqyi

An ontology evolution method based on folksonomy

Shufeng Wang, Wen Wang, Yanbin Zhuang, Xianju Fei
2015 Journal of Applied Research and Technology  
It was implemented and tested in a visual review/enhancement tool. All Rights Reserved  ...  These "social taxonomies", which emerge from collaborative tagging, contrast with the formalism and the systematic creation process applied to ontologies.  ...  The data is further used in a visual tool that supports ontology review and enhancement. Relate work Many approaches to automatically or semi-automatically develop ontologies were proposed.  ... 
doi:10.1016/j.jart.2015.06.015 fatcat:s6t5qfctefbfxaamohkfmewcfa

A Graph Framework for Manifold-Valued Data

Ronny Bergmann, Daniel Tenbrinck
2018 SIAM Journal of Imaging Sciences  
In order to translate variational models and partial differential equations to a graph, certain operators have been investigated and successfully applied to real-world applications involving graph models  ...  We introduce the basic calculus needed to formulate variational models and partial differential equations for manifold-valued functions and discuss the proposed graph framework for two particular families  ...  A general CFL condition for a broad class of graph operators can be found in [33] for the case M = R n . Semi-implicit scheme by linearization.  ... 
doi:10.1137/17m1118567 fatcat:eaqed7qypjb4ffmwdd3jlghewi

Representation Learning for Fine-Grained Change Detection

Niall O'Mahony, Sean Campbell, Lenka Krpalkova, Anderson Carvalho, Joseph Walsh, Daniel Riordan
2021 Sensors  
We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change  ...  As a result, many recent technologies that leverage big data and deep learning struggle with this task.  ...  The "variational" in VAE comes from a concept called variational inference, which refers to a technique for approximating probability densities through machine learning.  ... 
doi:10.3390/s21134486 pmid:34209075 fatcat:2shbcyvutvfbhhqrsmjipjdrzi

A Survey on Knowledge Graphs: Representation, Acquisition and Applications [article]

Shaoxiong Ji and Shirui Pan and Erik Cambria and Pekka Marttinen and Philip S. Yu
2021 IEEE Transactions on Neural Networks and Learning Systems   accepted
For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed.  ...  Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information.  ...  Another "hyperplane" setting is introduced to enhance the model with intersected embeddings.  ... 
doi:10.1109/tnnls.2021.3070843 pmid:33900922 arXiv:2002.00388v4 fatcat:4l2yxnf3wbg4zpzdumduvyr4he

Non-Euclidean geometry in nature [article]

Sergei Nechaev
2017 arXiv   pre-print
Specifically, I consider the formation of equilibrium shapes of plants and statistics of sparse random graphs.  ...  For these systems I discuss the following interlinked questions: (i) the optimal embedding of plants leaves in the three-dimensional space, (ii) the spectral statistics of sparse random matrix ensembles  ...  It is known that the exponential graphs possess hyperbolic metrics, meaning that they can be isometrically (with fixed branch lengths and angles between adjacent branches) embedded into a hyperbolic plane  ... 
arXiv:1705.08013v2 fatcat:3kfvnofjujdynmgdvofcrgvlyu
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