baeseongsu / hyperbolic_embedding_papers

Papers on Hyperbolic embeddings and Hyperbolic Neural Network

Hyperbolic embedding and Hyperbolic Neural Network Papers

A collection of papers on Hyperbolic embedding and Hyperbolic Neural Network.

Hyperbolic Embedding

1. Poincaré Embeddings for Learning Hierarchical Representations. NIPS 2017. paper

   Maximilian Nickel, Douwe Kiela.

2. Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry. ICML 2018. paper

   Maximilian Nickel, Douwe Kiela.

3. Representation Tradeoffs for Hyperbolic Embeddings. ICML 2018. paper

   Frederic Sala, Christopher De Sa, Albert Gu, Christopher Re.

4. Hyperbolic entailment cones for learning hierarchical embeddings. ICML 2018. paper

   Octavian-Eugen Ganea, Gary Becigneul, Thomas Hofmann.

5. Learning mixed-curvature representations in product spaces. ICLR 2019. paper

   Albert Gu, Frederic Sala, Beliz Gunel, Christopher Re.

6. Multi-relational Poincaré Graph Embeddings. NIPS 2019. paper

   Ivana Balaževic, Carl Allen, Timothy Hospedales.

7. Hyperbolic Disk Embeddings for Directed Acyclic Graphs. ICML 2019. paper

   Ryota Suzuki, Ryusuke Takahama, Shun Onoda.

8. Hyperbolic Heterogeneous Information Network Embedding. AAAI 2019. paper

   Xiao Wang, Yiding Zhang, Chuan Shi.

9. Low-rank approximations of hyperbolic embeddings. arxiv 2019. paper

   Pratik Jawanpuria Mayank Meghwanshi Bamdev Mishra.

10. Hyperbolic Multiplex Network Embedding with Maps of Random Walk. arxiv 2019. paper

    Peiyua Sun.

11. Hyperbolic Node Embedding for Signed Networks. arxiv 2019. paper

    W Song, S Wang.

12. HEAT: Hyperbolic Embedding of Attributed Networks. arxiv 2019. paper

    David McDonald, Shan He.

13. A hyperbolic Embedding Model for Directed Networks. arxiv 2019. paper

    Zongning Wu, Zengru Di, Ying Fan.

Hyperbolic Neural Network

1. Hyperbolic Neural Networks. NIPS 2018. paper

   Octavian-Eugen Ganea, Gary Bécigneul.

2. Hyperbolic Graph Neural Networks. NIPS 2019. paper

   Qi Liu, Maximilian Nickel, Douwe Kiela.

3. Hyperbolic Graph Convolutional Neural Networks. NIPS 2019. paper

   Ines Chami, Rex Ying, Christopher Ré, Jure Leskovec.

4. Hyperbolic attention networks. ICLR 2019. paper

   Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas.

5. Hyperbolic Graph Attention Network. AAAI 2020. paper

   Yiding Zhang, Xiao Wang, Xunqiang Jiang, Chuan Shi, Yanfang Ye.

6. Geom-GCN: Geometric Graph Convolutional Networks. ICLR 2020. paper

   Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chan, Yu Le, Bo Yang.

Hyperbolic Knowledge Graph

1. Multi-relational Poincaré Graph Embeddings. NIPS 2019. paper

   Ivana Balaževic, Carl Allen, Timothy Hospedales.

2. HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion. arxiv 2019. paper

   Prodromos Kolyvakis, Alexandros Kalousis, Dimitris Kiritsis.

3. Low-Dimensional Knowledge Graph Embeddings via Hyperbolic Rotations. NIPS 2019 workshop. paper

   Ines Chami, Adva Wolf, Frederic Sala, Christopher Ré.

4. Low-Dimensional Hyperbolic Knowledge Graph Embeddings. arxiv 2020 May. paper

   Ines Chami1, Adva Wolf, Da-Cheng Juan, Frederic Sala, Sujith Ravi, Christopher Re.

Applications

Recommender Systems

1. HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems. WSDM 2020. paper

   Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, Xiaoli Li.

2. Scalable Hyperbolic Recommender Systems. WSDM 2020. paper

   Benjamin Paul Chamberlain, Stephen R. Hardwick, David R. Wardrope, Fabon Dzogang, Fabio Daolio, Saúl Vargas.

Word Embedding

1. Poincare Glove: Hyperbolic Word Embeddings. ICLR 2019. paper

   Alexandru Tifrea, Gary Becigneul, Octavian-Eugen Ganea.

2. Skip-gram word embeddings in hyperbolic space. arxiv 2019. paper

   Matthias Leimeister, Benjamin J. Wilson.

3. Embedding text in hyperbolic spaces. arxiv 2018. paper

   Bhuwan Dhingra, Christopher Shallue, Mohammad Norouzi, Andrew Dai, and George Dahl.