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Graph-based semi-supervised node classification (GraphSSC) has wide applications, ranging from networking and security to data mining and machine learning, etc.
Link-based classification
Qing Lu and Lise Getoor · 2003
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Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf · 2003
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Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty · 2003
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani · 2006
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Sampling from large graphs
Jure Leskovec and Christos Faloutsos · 2006
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SybilGuard: Defending against Sybil attacks via social networks
H. Yu, M. Kaminsky, P. B. Gibbons, and A. Flaxman · 2006
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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SybilLimit: A near-optimal social network defense against Sybil attacks
H. Yu, P. B. Gibbons, M. Kaminsky, and F. Xiao · 2008
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Review graph based online store review spammer detection
Guan Wang, Sihong Xie, Bing Liu, and S Yu Philip · 2011
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Aiding the detection of fake accounts in large scale social online services
Qiang Cao, Michael Sirivianos, Xiaowei Yang, and Tiago Pregueiro · 2012
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Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on twitter
Chao Yang, Robert Harkreader, Jialong Zhang, Seungwon Shin, and Guofei Gu · 2012
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Opinion fraud detection in online reviews by network effects
Leman Akoglu, Rishi Chandy, and Christos Faloutsos · 2013
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Sybilbelief: A semi-supervised learning approach for structure-based sybil detection
Neil Zhenqiang Gong, Mario Frank, and Prateek Mittal · 2014
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Spotting fake reviews via collective positive-unlabeled learning
Huayi Li, Zhiyuan Chen, Bing Liu, Xiaokai Wei, and Jidong Shao · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Linearized and single-pass belief propagation
Wolfgang Gatterbauer, Stephan Günnemann, Danai Koutra, and Christos Faloutsos · 2015
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Collective opinion spam detection: Bridging review networks and metadata
Shebuti Rayana and Leman Akoglu · 2015
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You are who you know and how you behave: Attribute inference attacks via users’ social friends and behaviors
Neil Zhenqiang Gong and Bin Liu · 2016
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Anomaly detection in bitcoin network using unsupervised learning methods
Thai Pham and Steven Lee · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Federated meta-learning with fast convergence and efficient communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2018
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Attribute inference attacks in online social networks
Neil Zhenqiang Gong and Bin Liu · 2018
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 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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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Random walk based fake account detection in online social networks
Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong · 2017
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Attriinfer: Inferring user attributes in online social networks using markov random fields
Jinyuan Jia, Binghui Wang, Le Zhang, and Neil Zhenqiang Gong · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2017
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
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Graph-based security and privacy analytics via collective classification with joint weight learning and propagation
Binghui Wang, Jinyuan Jia, and Neil Zhenqiang Gong · 2019
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Structure-based sybil detection in social networks via local rule-based propagation
Binghui Wang, Jinyuan Jia, Le Zhang, and Neil Zhenqiang Gong · 2019
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Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics
Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I Weidele, Claudio Bellei, Tom Robinson, and Charles E Leiserson · 2019
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Simplifying graph convolutional networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr, Christopher Fifty, Tao Yu, and Kilian Q Weinberger · 2019
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Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2020
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