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Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse applications such as online recommendations, studies on disease contagion, organizational studies, etc.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Nonparametric link prediction in dynamic networks
Purnamrita Sarkar, Deepayan Chakrabarti, and Michael Jordan · 2012
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Convex adversarial collective classification
MohamadAli Torkamani and Daniel Lowd · 2013
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A deep learning approach to link prediction in dynamic networks
Xiaoyi Li, Nan Du, Hui Li, Kang Li, Jing Gao, and Aidong Zhang · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Link prediction in dynamic social networks by integrating different types of information
Nahla Mohamed Ahmed Ibrahim and Ling Chen · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Practical attacks against graph-based clustering
Yizheng Chen, Yacin Nadji, Athanasios Kountouras, Fabian Monrose, Roberto Perdisci, Manos Antonakakis, and Nikolaos Vasiloglou · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Attributed network embedding for learning in a dynamic environment
Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, and Huan Liu · 2017
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Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song · 2017
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Efficient incremental dynamic link prediction algorithms in social network
Zhongbao Zhang, Jian Wen, Li Sun, Qiaoyu Deng, Sen Su, and Pengyan Yao · 2017
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Fast gradient attack on network embedding
Jinyin Chen, Yangyang Wu, Xuanheng Xu, Yixian Chen, Haibin Zheng, and Qi Xuan · 2018
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Deep learning for link prediction in dynamic networks using weak estimators
Carter Chiu and Justin Zhan · 2018
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Adversarial attack on graph structured data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song · 2018
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Adversarial network embedding
Quanyu Dai, Qiang Li, Jian Tang, and Dan Wang · 2018
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Dyngem: Deep embedding method for dynamic graphs
Palash Goyal, Nitin Kamra, Xinran He, and Yan Liu · 2018
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Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al · 2018
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Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks
Xiaoke Ma, Penggang Sun, and Yu Wang · 2018
Time-aware gradient attack on dynamic network link prediction
Jinyin Chen, Jian Zhang, Zhi Chen, Min Du, Feifei Li, and Qi Xuan · 2019
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E-lstm-d: A deep learning framework for dynamic network link prediction
Jinyin Chen, Jian Zhang, Xuanheng Xu, Chenbo Fu, Dan Zhang, Qingpeng Zhang, and Qi Xuan · 2019
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Attacking graph-based classification via manipulating the graph structure
Binghui Wang and Neil Zhenqiang Gong · 2019
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Adversarial examples on graph data: Deep insights into attack and defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu · 2019
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Topology attack and defense for graph neural networks: An optimization perspective
Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin · 2019
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How powerful are graph neural networks?
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Continuous-time dynamic network embeddings
Giang Hoang Nguyen, John Boaz Lee, Ryan A Rossi, Nesreen K Ahmed, Eunyee Koh, and Sungchul Kim · 2018
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Data poisoning attack against unsupervised node embedding methods
Mingjie Sun, Jian Tang, Huichen Li, Bo Li, Chaowei Xiao, Yao Chen, and Dawn Song · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Bing Yu, Haoteng Yin, and Zhanxing Zhu · 2018
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Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks
Wenchao Yu, Wei Cheng, Charu C Aggarwal, Kai Zhang, Haifeng Chen, and Wei Wang · 2018
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Dynamic network embedding by modeling triadic closure process
Le-kui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang · 2018
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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T-gcn: A temporal graph convolutional network for traffic prediction
Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li · 2019
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Adversarial attacks on graph neural networks via meta learning
Daniel Zügner and Stephan Günnemann · 2019
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A restricted black-box adversarial framework towards attacking graph embedding models
Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, and Junzhou Huang · 2020
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dyngraph2vec: Capturing network dynamics using dynamic graph representation learning
Palash Goyal, Sujit Rokka Chhetri, and Arquimedes Canedo · 2020
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Dynamic graph convolutional networks
Franco Manessi, Alessandro Rozza, and Mario Manzo · 2020
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Evolvegcn: Evolving graph convolutional networks for dynamic graphs
Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B Schardl, and Charles E Leiserson · 2020
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Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach
Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, and Vasant Honavar · 2020
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Backdoor attacks to graph neural networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong · 2020
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