Fetching the paper…
Reading the bibliography…
Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.
Predicting multicellular function through multi-layer tissue networks
Zitnik, M. and Leskovec, J · 1903
Earlier work this paper cites.
Highly scalable parallel algorithms for sparse matrix factorization
Gupta, A., Karypis, G., and Kumar, V · 1997
Earlier work this paper cites.
The webgraph framework i: compression techniques
Boldi, P. and Vigna, S · 2004
Earlier work this paper cites.
Group formation in large social networks: membership, growth, and evolution
Backstrom, L., Huttenlocher, D., Kleinberg, J., and Lan, X · 2006
Earlier work this paper cites.
Mining graph data
Cook, D. J. and Holder, L. B · 2006
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Koren, Y., Bell, R., and Volinsky, C · 2009
Earlier work this paper cites.
Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters
Leskovec, J., Lang, K. J., Dasgupta, A., and Mahoney, M. W · 2009
Earlier work this paper cites.
Scalable learning of collective behavior based on sparse social dimensions
Tang, L. and Liu, H · 2009
Earlier work this paper cites.
What is twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., and Moon, S · 2010
Earlier work this paper cites.
Layered label propagation: A multiresolution coordinate-free ordering for compressing social networks
Boldi, P., Rosa, M., Santini, M., and Vigna, S · 2011
Earlier work this paper cites.
Learning structured embeddings of knowledge bases
Bordes, A., Weston, J., Collobert, R., Bengio, Y., et al · 2011
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., and Singer, Y · 2011
Earlier work this paper cites.
Large-scale matrix factorization with distributed stochastic gradient descent
Gemulla, R., Nijkamp, E., Haas, P. J., and Sismanis, Y · 2011
Earlier work this paper cites.
A three-way model for collective learning on multi-relational data
Nickel, M., Tresp, V., and Kriegel, H.-P · 2011
Cited alongside, same era.
Hogwild: A lock-free approach to parallelizing stochastic gradient descent
Recht, B., Re, C., Wright, S., and Niu, F · 2011
Cited alongside, same era.
Large scale distributed deep networks
Dean, J., Corrado, G. S., Monga, R., Chen, K., Devin, M., Le, Q. V., Mao, M. Z., Ranzato, M., Senior, A., Tucker, P., Yang, K., and Ng, A. Y · 2012
Cited alongside, same era.
Translating embeddings for modeling multi-relational data
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O · 2013
Cited alongside, same era.
Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
Cited alongside, same era.
SNAP Datasets: Stanford large network dataset collection
Leskovec, J. and Krevl, A · 2014
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
Later among the works it cites.
Network-efficient distributed word2vec training system for large vocabularies
Ordentlich, E., Yang, L., Feng, A., Cnudde, P., Grbovic, M., Djuric, N., Radosavljevic, V., and Owens, G · 2016
Later among the works it cites.
Swivel: Improving embeddings by noticing what’s missing
Shazeer, N., Doherty, R., Evans, C., and Waterson, C · 2016
Later among the works it cites.
Complex embeddings for simple link prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., and Bouchard, G · 2016
Later among the works it cites.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Scaling distributed machine learning with the parameter server
Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A., Josifovski, V., Long, J., Shekita, E. J., and Su, B.-Y · 2014
Cited alongside, same era.
Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S · 2014
Cited alongside, same era.
Embedding entities and relations for learning and inference in knowledge bases
Yang, B., Yih, W.-t., He, X., Gao, J., and Deng, L · 2014
Cited alongside, same era.
One trillion edges: Graph processing at facebook-scale
Ching, A., Edunov, S., Kabiljo, M., Logothetis, D., and Muthukrishnan, S · 2015
Cited alongside, same era.
Type-constrained representation learning in knowledge graphs
Krompaß, D., Baier, S., and Tresp, V · 2015
Cited alongside, same era.
A review of relational machine learning for knowledge graphs
Nickel, M., Murphy, K., Tresp, V., and Gabrilovich, E · 2015
Cited alongside, same era.
Freebase data dumps
Google · 2018
Later among the works it cites.
Canonical tensor decomposition for knowledge base completion
Lacroix, T., Usunier, N., and Obozinski, G · 2018
Later among the works it cites.
Mile: A multi-level framework for scalable graph embedding
Liang, J., Gurukar, S., and Parthasarathy, S · 2018
Later among the works it cites.
Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., and Welling, M · 2018
Later among the works it cites.
Billion-scale commodity embedding for e-commerce recommendation in alibaba
Wang, J., Huang, P., Zhao, H., Zhang, Z., Zhao, B., and Lee, D. L · 2018
Later among the works it cites.
Starspace: Embed all the things!
Wu, L. Y., Fisch, A., Chopra, S., Adams, K., Bordes, A., and Weston, J · 2018
Later among the works it cites.
Graph convolutional neural networks for web-scale recommender systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J · 2018
Later among the works it cites.