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Predictive Network Representation Learning for Link Prediction
2017
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17
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In this paper, we propose a predictive network representation learning (PNRL) model to solve the structural link prediction problem. ...
By jointly optimizing the two objectives, the model can not only enhance the predictive ability of node representations but also learn additional link prediction knowledge in the representation space. ...
Network Representation Learning for Hidden Link Prediction The objective of this component is to improve predictive ability of node representations for link prediction and directly learn the link prediction ...
doi:10.1145/3077136.3080692
dblp:conf/sigir/WangCL17
fatcat:vjidi6bs4ff4jkvlkz7gmeagqy
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?
[article]
2020
arXiv
pre-print
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In this paper, we aim to shed light on the state-of-the-art of network embedding methods for link prediction and show, using a consistent evaluation pipeline, that only thin progress has been made over ...
Network embedding methods map a network's nodes to vectors in an embedding space, in such a way that these representations are useful for estimating some notion of similarity or proximity between pairs ...
We provide an in-depth empirical analysis of the state-of-the-art on network representation learning for LP. ...
arXiv:2002.11522v5
fatcat:w4fjl7rkgbcfzihyzlnqmt7jhu
selfRL: Two-Level Self-Supervised Transformer Representation Learning for Link Prediction of Heterogeneous Biomedical Networks
[article]
2020
bioRxiv
pre-print
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Therefore, this study proposes a two-level self-supervised representation learning, namely selfRL, for link prediction in heterogeneous biomedical networks. ...
However, the self-supervised representation learning for link prediction in HBNs has been slightly explored in previous researches. ...
In addition, these representation approaches is not specially designed for link prediction, thus resulting in learning an inexplicit representation for link prediction. ...
doi:10.1101/2020.10.20.347153
fatcat:j627pkkdlvgwzot6s4nh3lb3fa
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2010.16103v4 fatcat:zoqe4hbdmfb3hlppyvxtvfuzrqOther Versions
Experiments on one important two-node representation learning task, link prediction, verified our theory. ...
In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such ...
In this paper, we show that GNNs combined with labeling trick can as well learn structural link representations, which reassures using GNNs for link prediction. ...
arXiv:2010.16103v5
fatcat:w6nh42wktbaevor32pjkg657pe
Multi-Task Network Representation Learning
2020
Frontiers in Neuroscience
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for learning desirable network representations. ...
The original network and the incomplete network share a unified embedding layer followed by node classification and link prediction tasks that simultaneously perform on the embedding vectors. ...
ACKNOWLEDGMENTS We would like to thank Yu Zhang and Yiming Fan for thoughtful comments on the manuscript and language revision. We were grateful to all study participants for their time and effort. ...
doi:10.3389/fnins.2020.00001
pmid:32038151
pmcid:PMC6989613
fatcat:xeuo264qsne73aiddfdne6xm6y
Link Prediction via Ranking Metric Dual-Level Attention Network Learning
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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Link prediction is a challenging problem for complex network analysis, arising in many disciplines such as social networks and telecommunication networks. ...
We propose a novel ranking metric network learning framework by jointly exploiting both node-level and path-level attentional proximity of the endpoints for link prediction. ...
to learn the discriminative joint representation of the endpoints for link prediction. ...
doi:10.24963/ijcai.2017/493
dblp:conf/ijcai/ZhaoGZCHZ17
fatcat:loiunhr4mvexlev5f4o3xqoziq
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective
[article]
2021
arXiv
pre-print
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Existing representation learning methods on graphs have achieved state-of-the-art performance on various graph-related tasks such as node classification, link prediction, etc. ...
For instance, one can accurately infer the links (or node identity) in a graph from a node classifier (or link predictor) trained on the learnt node representations by existing methods. ...
We thank the anonymous reviewers for their constructive comments. This work is supported by the Amazon Research Award. ...
arXiv:2107.01475v1
fatcat:qavrgjvxzbfzfg56x5ngxv3ioe
Edge-Nodes Representation Neural Machine for Link Prediction
2019
Algorithms
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Link prediction is a task predicting whether there is a link between two nodes in a network. ...
Other popular methods tend to learn the link's representation, but they cannot represent the link fully. ...
SEAL learns link's formation mechanism from subgraph, node embedding, attributes and trains a graph neural network(GNN) for link prediction. ...
doi:10.3390/a12010012
fatcat:mvhzpmmuarfvraq2i26busw47y
Cross View Link Prediction by Learning Noise-resilient Representation Consensus
2017
Proceedings of the 26th International Conference on World Wide Web - WWW '17
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Link Prediction has been an important task for social and information networks. Existing approaches usually assume the completeness of network structure. ...
We aim to bridge the information gap by learning a robust consensus for link-based and attribute-based representations so that nodes become comparable in the latent space. ...
LINK-BASED REPRESENTATION LEARN-ING We aim to learn representations for the network nodes by preserving structural information. ...
doi:10.1145/3038912.3052575
dblp:conf/www/WeiXCY17
fatcat:eqsfsd6lobfc5epvkdq6lacxte
Link Prediction Based on Orbit Counting and Graph Auto-Encoder
2020
IEEE Access
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In order to answer RQ1, 5 traditional methods for link prediction are compared to OC-GAE. For RQ2, 3 network representation learning methods are compared. ...
the final learned representation to predict the link. ...
doi:10.1109/access.2020.3045529
fatcat:bvkov65ufnd2nbi7ok72qvqdii
Learning Universal Network Representation via Link Prediction by Graph Convolutional Neural Network
2021
Journal of Social Computing
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This demonstrates the potential of deep learning algorithms to be applied for node classification and link prediction tasks by converting network structures into low-dimensional vector representations. ...
In this paper, we propose a novel network representation model, named Link Prediction based Network Representation (LPNR). This approach extends the GAT algorithm [14] to the link prediction task. ...
doi:10.23919/jsc.2021.0001
fatcat:q4vwqeuudbglzdykpwdhst2mwa
Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction
2021
Complexity
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In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. ...
Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. ...
representation learning for link prediction [42] [43] [44] . ...
doi:10.1155/2021/1277579
fatcat:ccvuy3b7hvgeplnnujy4sbsxpa
Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction
[article]
2021
arXiv
pre-print
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In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. ...
Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. ...
(61901247, 61803047), Natural Science Foundation of Shandong Province ZR2019BF032, Major Project of The National Social Science Foundation of China (19ZDA149, 19ZDA324) and Fundamental Research Funds for ...
arXiv:2111.07027v1
fatcat:vypyaz6ib5d2hhtlz27bds45bi
Network Embedding For Link Prediction in Bipartite Networks
2021
European Journal of Science and Technology
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A network embedding and a supervised learning based link prediction model has been presented for bipartite networks. ...
Ensemble learning algorithms have been applied for supervised link prediction. ...
It has been shown that the learned node representations are used successfully in link prediction on different types of networks such as Collaborative networks (Wang et al., 2016 ) , social networks (Ou ...
doi:10.31590/ejosat.937722
fatcat:fgl3ran6lzdfxps3guzuqddu2i
Link Prediction via Graph Attention Network
[article]
2019
arXiv
pre-print
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Link prediction aims to infer missing links or predicting the future ones based on currently observed partial networks, it is a fundamental problem in network science with tremendous real-world applications ...
Instead of learning node representation with the node label information, DeepLinker uses the links as supervised information. ...
suitable for link prediction task. ...
arXiv:1910.04807v3
fatcat:tuwucrnz3feaxdfkkhcqqbcega
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