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An important part of many machine learning workflows on graphs is vertex representation learning, i.e., learning a low-dimensional vector representation for each vertex in the graph.
Modern Graph Theory
B Bollobás. 1998 · 1998
Earlier work this paper cites.
Automating the construction of internet portals with machine learning
Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. 2000 · 2000
Earlier work this paper cites.
Nonlinear dimensionality reduction by locally linear embedding
Sam T Roweis and Lawrence K Saul. 2000 · 2000
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
Joshua B Tenenbaum, Vin De Silva, and John C Langford. 2000 · 2000
Earlier work this paper cites.
Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in neural information processing systems
Mikhail Belkin and Partha Niyogi. 2002 · 2002
Earlier work this paper cites.
Multi-label classification: An overview
Grigorios Tsoumakas and Ioannis Katakis. 2007 · 2007
Earlier work this paper cites.
Social computing data repository at ASU
Reza Zafarani and Huan Liu. 2009 · 2009
Earlier work this paper cites.
Spark: Cluster computing with working sets
Matei Zaharia, Mosharaf Chowdhury, Michael J Franklin, Scott Shenker, and Ion Stoica. 2010 · 2010
Earlier work this paper cites.
Supervised random walks: predicting and recommending links in social networks. In Proceedings of the fourth ACM international conference on Web search and data mining
Lars Backstrom and Jure Leskovec. 2011 · 2011
Earlier work this paper cites.
It’s who you know: graph mining using recursive structural features. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Keith Henderson, Brian Gallagher, Lei Li, Leman Akoglu, Tina Eliassi-Rad, Hanghang Tong, and Christos Faloutsos. 2011 · 2011
Earlier work this paper cites.
Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning
Yoshua Bengio. 2012 · 2012
Earlier work this paper cites.
Unsupervised feature selection for linked social media data. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Jiliang Tang and Huan Liu. 2012 · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a · 2013
Earlier work this paper cites.
Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
Cited alongside, same era.
Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015 · 2015
Cited alongside, same era.
Microsoft Academic Graph - KDD cup 2016
2016 · 2016
Cited alongside, same era.
Tensorflow: a system for large-scale machine learning.. In OSDI
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining
Aditya Grover and Jure Leskovec. 2016 · 2016
Cited alongside, same era.
Attributed network embedding for learning in a dynamic environment. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, and Huan Liu. 2017 · 2017
Later among the works it cites.
DBLP graphs
2018 · 2018
Later among the works it cites.
Deep Neural Networks Based Approaches for Graph Embeddings
2018 · 2018
Later among the works it cites.
Massive network data
2018 · 2018
Later among the works it cites.
Sub2Vec: Feature Learning for Subgraphs
B Adhikari, Y Zhang, N Ramakrishnan Pacific-Asia Conference, and 2018. [n. d.] · 2018
Later among the works it cites.
Combining Temporal Aspects of Dynamic Networks with Node2Vec for a more Efficient Dynamic Link Prediction. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle. 2016 · 2016
Cited alongside, same era.
The spacey random walk: A stochastic process for higher-order data
Austin R Benson, David F Gleich, and Lek-Heng Lim. 2017 · 2017
Cited alongside, same era.
Streaming recommender systems. In Proceedings of the 26th International Conference on World Wide Web
Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A Hasegawa-Johnson, and Thomas S Huang. 2017 · 2017
Cited alongside, same era.
Task-guided and path-augmented heterogeneous network embedding for author identification. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining
Ting Chen and Yizhou Sun. 2017 · 2017
Cited alongside, same era.
metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017 · 2017
Cited alongside, same era.
Representation Learning on Graphs: Methods and Applications
William L Hamilton, Rex Ying, and Jure Leskovec. 2017b · 2017
Cited alongside, same era.
Sam De Winter, Tim Decuypere, Sandra Mitrovic, Bart Baesens, and Jochen De Weerdt. [n. d.] · 2018
Later among the works it cites.
Streaming link prediction on dynamic attributed networks. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
Jundong Li, Kewei Cheng, Liang Wu, and Huan Liu. 2018 · 2018
Later among the works it cites.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018 · 2018
Later among the works it cites.
VERSE: Versatile Graph Embeddings from Similarity Measures
Anton Tsitsulin, Davide Mottin, Panagiotis Karras, and Emmanuel Müller. 2018 · 2018
Later among the works it cites.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Later among the works it cites.
Network representation learning: a survey
Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang. 2018 · 2018
Later among the works it cites.
Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding.. In IJCAI
Lun Du, Yun Wang, Guojie Song, Zhicong Lu, and Junshan Wang. 2018 · 2092
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