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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data
Kang Li, Jing Gao, Suxin Guo, et al.
(2014), pp. 300-309
Closed Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

node2vec
Aditya Grover, Jure Leskovec
(2016), pp. 855-864
Open Access | Times Cited: 9613
A systemic analysis of link prediction in social network
Sogol Haghani, Mohammad Reza Keyvanpour
Artificial Intelligence Review (2017) Vol. 52, Iss. 3, pp. 1961-1995
Closed Access | Times Cited: 89
Survey on graph embeddings and their applications to machine learning problems on graphs
Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, et al.
PeerJ Computer Science (2021) Vol. 7, pp. e357-e357
Open Access | Times Cited: 89
SIGNet: Scalable Embeddings for Signed Networks
Mohammad Raihanul Islam, B. Aditya Prakash, Naren Ramakrishnan
Lecture notes in computer science (2018), pp. 157-169
Open Access | Times Cited: 48
Combining Temporal Aspects of Dynamic Networks with Node2Vec for a more Efficient Dynamic Link Prediction
Sam De Winter, Tim Decuypere, Sandra Mitrović, et al.
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2018), pp. 1234-1241
Closed Access | Times Cited: 44
JONNEE: Joint Network Nodes and Edges Embedding
Ilya Makarov, Ksenia Korovina, Dmitrii Kiselev
IEEE Access (2021) Vol. 9, pp. 144646-144659
Open Access | Times Cited: 32
Paired Restricted Boltzmann Machine for Linked Data
Suhang Wang, Jiliang Tang, Fred Morstatter, et al.
(2016), pp. 1753-1762
Open Access | Times Cited: 27
Learning network representations
Luis G. Moyano
The European Physical Journal Special Topics (2017) Vol. 226, Iss. 3, pp. 499-518
Closed Access | Times Cited: 27
Graph representation learning with encoding edges
Qi Li, Zehong Cao, Jiang Zhong, et al.
Neurocomputing (2019) Vol. 361, pp. 29-39
Closed Access | Times Cited: 23
Scalable Probabilistic Matrix Factorization with Graph-Based Priors
Jonathan Strahl, Jaakko Peltonen, Hiroshi Mamitsuka, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2020) Vol. 34, Iss. 04, pp. 5851-5858
Open Access | Times Cited: 20
Collaborative Prediction for Multi-entity Interaction With Hierarchical Representation
Qiang Liu, Shu Wu, Liang Wang
(2015)
Closed Access | Times Cited: 12
Distributed Representations of Signed Networks
Mohammad Raihanul Islam, B. Aditya Prakash, Naren Ramakrishnan
arXiv (Cornell University) (2017)
Open Access | Times Cited: 11
Separating-Plane Factorization Models
Haolan Chen, Di Niu, Kunfeng Lai, et al.
(2016), pp. 669-678
Closed Access | Times Cited: 9
SysEvoRecomd: Network Reconstruction by Graph Evolution and Change Learning
Animesh Chaturvedi, Aruna Tiwari, Shubhangi Chaturvedi
IEEE Systems Journal (2020) Vol. 14, Iss. 3, pp. 4007-4014
Closed Access | Times Cited: 6
BalNode2Vec: Balanced Random Walk based Versatile Feature Learning for Networks
Amirreza Salamat, Xiao Luo, Ali Jafari
2022 International Joint Conference on Neural Networks (IJCNN) (2020), pp. 1-8
Closed Access | Times Cited: 6
Non-parametric learning of lifted Restricted Boltzmann Machines
Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
International Journal of Approximate Reasoning (2020) Vol. 120, pp. 33-47
Open Access | Times Cited: 4
node2vec: Scalable Feature Learning for Networks
Aditya Grover, Jure Leskovec
arXiv (Cornell University) (2016)
Closed Access | Times Cited: 3
LASAGNE: Locality And Structure Aware Graph Node Embedding
Evgeniy Faerman, Felix Borutta, Kimon Fountoulakis, et al.
arXiv (Cornell University) (2017)
Closed Access | Times Cited: 3
SIGNet: Scalable Embeddings for Signed Networks
Mohammad Raihanul Islam, B. Aditya Prakash, Naren Ramakrishnan
arXiv (Cornell University) (2017)
Closed Access | Times Cited: 1
Low-Norm Graph Embedding.
Yihan Gao, Chao Zhang, Jian Peng, et al.
arXiv (Cornell University) (2018)
Closed Access | Times Cited: 1
Smooth Deep Network Embedding
Mengyu Zheng, Chuan Zhou, Jia Wu, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2019), pp. 1-8
Closed Access | Times Cited: 1
Heterogeneous Graph Based Neural Network for Social Recommendations with Balanced Random Walk Initialization
Amirreza Salamat
(2020)
Closed Access | Times Cited: 1
Network Representation Learning in Social Media
Suhang Wang
(2018)
Closed Access
Non-Parametric Learning of Lifted Restricted Boltzmann Machines
Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
arXiv (Cornell University) (2020)
Closed Access
A Structure of Restricted Boltzmann Machine for Modeling System Dynamics
Guillaume Padiolleau, Olivier Bach, Alain Hugget, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2020), pp. 1-8
Open Access
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