Collective Classification in Network Data

Cited by (1,005)

Items 101–200 of 1,005

1. Graph Clustering with Graph Neural Networks
   Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller · 2020 · arXiv (Cornell University)
2. Adversarial Network Embedding
   Quanyu Dai, Qiang Li, Jian Tang, Dan Wang · 2018 · Proceedings of the AAAI Conference on Artificial Intelligence
3. Design Space for Graph Neural Networks
   Jiaxuan You, Rex Ying, Jure Leskovec · 2020 · arXiv (Cornell University)
4. Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification
   Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay · 2019 · Proceedings of the AAAI Conference on Artificial Intelligence
5. A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
   Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, Suhang Wang · 2024 · Machine Intelligence Research
6. GMNN: Graph Markov Neural Networks
   Meng Qu, Yoshua Bengio, Jian Tang · 2019 · arXiv (Cornell University)
7. Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs
   Chen, Zhikai, Mao, Haitao, Li, Hang, Jin, Wei, Wen, Hongzhi, Wei, Xiaochi, Wang, Shuaiqiang, Yin, Dawei, Fan, Wenqi, Liu, Hui et al. · 2024 · ACM SIGKDD Explorations Newsletter
8. Graph U-Nets
   Hongyang Gao, Shuiwang Ji · 2019 · arXiv (Cornell University)
9. Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
   Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Lamar Yang, Danai Koutra · 2022 · Proceedings
10. Self-supervised Learning on Graphs: Deep Insights and New Direction
    Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, Jiliang Tang · 2020 · arXiv (Cornell University)
11. Large Language Models on Graphs: A Comprehensive Survey
    Bowen Jin, Gang Liu, Chi Han, Meng Jiang, Heng Ji, Jiawei Han · 2024 · IEEE Transactions on Knowledge and Data Engineering
12. A survey of graph neural networks in various learning paradigms: methods, applications, and challenges
    Lilapati Waikhom, Ripon Patgiri · 2022 · Artificial Intelligence Review
13. Active Learning Query Strategies for Classification, Regression, and Clustering: A Survey
    P. R. Kumar, Atul Kumar Gupta · 2020 · Journal of Computer Science and Technology
14. Combining Graph Convolutional Neural Networks and Label Propagation
    Hongwei Wang, Jure Leskovec · 2021 · ACM Transactions on Information Systems
15. GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
    X. D. Zhang, Marinka Žitnik · 2020 · arXiv (Cornell University)
16. Transferring Robustness for Graph Neural Network Against Poisoning Attacks
    Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang · 2020 · ACM International Conference on Web Search and Data Mining (WSDM)
17. Graph Attention Auto-Encoders
    Amin Salehi, Hasan Davulcu · 2020 · IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
18. Auto-GNN: Neural architecture search of graph neural networks
    Kaixiong Zhou, Xiao Xia Huang, Qingquan Song, Rui Chen, Xia Hu · 2022 · Frontiers in Big Data
19. Label Efficient Semi-Supervised Learning via Graph Filtering
    Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan · 2019 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
20. Evaluating explainability for graph neural networks
    Agarwal, Chirag, Queen, Owen, Lakkaraju, Himabindu, Zitnik, Marinka · 2023 · Scientific Data
21. Co-Embedding Attributed Networks
    Zaiqiao Meng, Shangsong Liang, Hongyan Bao, Xiangliang Zhang · 2019 · ACM International Conference on Web Search and Data Mining (WSDM)
22. PaGraph
    Zhiqi Lin, Cheng Li, Youshan Miao, Yunxin Liu, Yinlong Xu · 2020 · ACM Symposium on Cloud Computing (SoCC)
23. Multi-View Attribute Graph Convolution Networks for Clustering
    Jiafeng Cheng, Qianqian Wang, Zhiqiang Tao, Deyan Xie, Quanxue Gao · 2020 · International Joint Conference on Artificial Intelligence
24. How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
    Kim, Dongkwan, Alice H. Oh · 2022 · arXiv (Cornell University)
25. Graph neural networks in node classification: survey and evaluation
    Shunxin Xiao, Shiping Wang, Yuanfei Dai, Wenzhong Guo · 2021 · Machine Vision and Applications
26. Deep Recursive Network Embedding with Regular Equivalence
    Tu, Ke, Cui, Peng, Wang, Xiao, Yu, Philip S., Zhu, Wenwu · 2018 · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
27. Understanding over-squashing and bottlenecks on graphs via curvature
    Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein · 2021 · arXiv (Cornell University)
28. Low-rank kernel learning for graph-based clustering
    Zhao Kang, Liangjian Wen, Wenyu Chen, Zenglin Xu · 2018 · Knowledge-Based Systems
29. Non-Local Graph Neural Networks
    Meng Liu, Zhengyang Wang, Shuiwang Ji · 2021 · IEEE Transactions on Pattern Analysis and Machine Intelligence
30. Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
    Jayanta Mandi, James Kotary, Senne Berden, Maxime Mulamba, Víctor Bucarey L., Tias Guns, Ferdinando Fioretto · 2024 · Journal of Artificial Intelligence Research
31. Graph Information Bottleneck
    Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec · 2020 · arXiv (Cornell University)
32. FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
    Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Yang, Carl, Xie, Han, Sun, Lichao, He, Lifang, Yang, Liangwei, Yu, Philip S., Rong, Yu et al. · 2021 · arXiv (Cornell University)
33. Large-Scale Representation Learning on Graphs via Bootstrapping
    Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Veličković, Michal Valko · 2021 · arXiv (Cornell University)
34. Spotting Fake Reviews via Collective Positive-Unlabeled Learning
    Huayi Li, Zhiyuan Chen, Bing Liu, Xiaokai Wei, Jidong Shao · 2014 · Proceedings
35. Learning Combinatorial Optimization on Graphs: A Survey With Applications to Networking
    Natalia Vassileva, Rebecca Steinert, Daniel F. Pérez-Ramírez, Magnus Boman · 2020 · IEEE Access
36. One-class graph neural networks for anomaly detection in attributed networks
    Xu-Hong Wang, Baihong Jin, Ying Du, Ping Cui, Ying-Shui Tan, Yu-Pu Yang · 2020 · Neural computing & applications (Print)
37. EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
    Shengwen Liang, Ying Wang, Cheng Liu, Lei He, Huawei Li, Dawen Xu, Xiaowei Li · 2020 · IEEE Transactions on Computers
38. Multi-hop Attention Graph Neural Networks
    Guangtao Wang, Rex Ying, Jing Huang, Jure Leskovec · 2021 · International Joint Conference on Artificial Intelligence
39. Uncovering collusive spammers in Chinese review websites
    Chang Xu, Jie Zhang, Kuiyu Chang, Chong Long · 2013 · ACM International Conference on Information and Knowledge Management (CIKM)
40. Identity-aware Graph Neural Networks
    Jiaxuan You, Jonathan M. Gomes-Selman, Rex Ying, Jure Leskovec · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
41. Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
    Sheng Wan, Shirui Pan, Jian Jun Yang, Chen Gong · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
42. Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning
    Bingbing Xu, Huawei Shen, Qi Cao, Keting Cen, Xueqi Cheng · 2019 · International Joint Conference on Artificial Intelligence
43. GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks
    Jiajun Li, Ahmed Louri, Avinash Karanth, Răzvan Bunescu · 2021 · IEEE International Symposium on High-Performance Computer Architecture (HPCA)
44. Graphite: Iterative Generative Modeling of Graphs
    Aditya Grover, Aaron Zweig, Stefano Ermon · 2018 · arXiv (Cornell University)
45. Active Learning for Networked Data
    Mustafa Bilgic, Lilyana Simeonova Mihalkova, Lise C. Getoor · 2010
46. A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories
    Cheng Hong Yang, Maosong Sun, Wayne Xin Zhao, Zhiyuan Liu, Edward Y. Chang · 2017 · ACM Transactions on Information Systems
47. Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
    Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, Jian Tang · 2021 · arXiv (Cornell University)
48. Fast Network Embedding Enhancement via High Order Proximity Approximation
    Cheng Hong Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu · 2017 · International Joint Conference on Artificial Intelligence
49. Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
    Jia Li, Yu Rong, Hong Cheng, Helen M. L. Meng, Wenbing Huang, Junzhou Huang · 2019 · International World Wide Web Conference (WWW)
50. Using ghost edges for classification in sparsely labeled networks
    Brian J. Gallagher, Hanghang Tong, Tina Eliassi‐Rad, Christos Faloutsos · 2008 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
51. Robust Spatial Filtering With Graph Convolutional Neural Networks
    Felipe Petroski Such, Shagan Sah, Miguel Domínguez, Suhas Pillai, Chao Zhang, Andrew Michael, Nathan D. Cahill, Raymond Ptucha · 2017 · IEEE Journal of Selected Topics in Signal Processing
52. Fairness in Graph Mining: A Survey
    Yushun Dong, Jing Ma, Song Wang, Chen Chen, Jundong Li · 2023 · IEEE Transactions on Knowledge and Data Engineering
53. A Survey on Deep Active Learning: Recent Advances and New Frontiers
    Dongyuan Li, Zhen Rong Wang, Yankai Chen, Renhe Jiang, Weiping Ding, Manabu Okumura · 2024 · IEEE Transactions on Neural Networks and Learning Systems
54. Deep Collective Classification in Heterogeneous Information Networks
    Yizhou Zhang, Yun Xiong, Xiangnan Kong, Shanshan Li, Jinhong Mi, Yangyong Zhu · 2018 · International World Wide Web Conference (WWW)
55. Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay
    Fan Zhou, Chengtai Cao · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
56. Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View
    Jingcan Duan, Siwei Wang, Pei Zhang, En Zhu, Jingtao Hu, Hu Jin, Yue Liu, Zhibin Dong · 2023 · Proceedings of the AAAI Conference on Artificial Intelligence
57. SIDE
    Jung Hwan Kim, Haekyu Park, Jieun Lee, U Kang · 2018 · International World Wide Web Conference (WWW)
58. Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
    Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec · 2020 · arXiv (Cornell University)
59. MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network
    Wang, Hao, Xu, Tong, Liu, Qi, Lian, Defu, Chen, Enhong, Du, Dongfang, Wu, Han, Su, Wen · 2019 · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
60. Meta path-based collective classification in heterogeneous information networks
    Xiangnan Kong, Philip S. Yu, Ying Ding, David J. Wild · 2012 · ACM International Conference on Information and Knowledge Management (CIKM)
61. A Unified View on Graph Neural Networks as Graph Signal Denoising
    Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, Neil Shah · 2021 · ACM International Conference on Information and Knowledge Management (CIKM)
62. Network Structure Inference, A Survey
    Ivan Brugere, Brian J. Gallagher, Tanya Y. Berger-Wolf · 2018 · ACM Computing Surveys
63. 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, Junzhou Huang · 2020 · Proceedings of the AAAI Conference on Artificial Intelligence
64. Embedding of Embedding (EOE)
    Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu · 2017 · ACM International Conference on Web Search and Data Mining (WSDM)
65. Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey
    Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan · 2021 · IEEE/CAA Journal of Automatica Sinica
66. From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
    Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu · 2021 · arXiv (Cornell University)
67. NeMa
    Arijit Khan, Yinghui Wu, Charų C. Aggarwal, Xifeng Yan · 2013 · Proceedings of the VLDB Endowment
68. Certifiable Robustness and Robust Training for Graph Convolutional Networks
    Daniel Zügner, Stephan Günnemann · 2019 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
69. One Embedder, Any Task: Instruction-Finetuned Text Embeddings
    Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf E, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, Tao Yu · 2023 · Findings of the Association for Computational Linguistics: ACL (Findings of ACL)
70. Neighbor Contrastive Learning on Learnable Graph Augmentation
    Xiao Shen, Dewang Sun, Shirui Pan, Xi Zhou, Laurence Yang · 2023 · Proceedings of the AAAI Conference on Artificial Intelligence
71. Differentiable Graph Module (DGM) for Graph Convolutional Networks
    Anees Kazi, Luca Cosmo, Seyed‐Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein · 2022 · IEEE Transactions on Pattern Analysis and Machine Intelligence
72. Learning on Attribute-Missing Graphs
    Xu Chen, Siheng Chen, Jiangchao Yao, Huangjie Zheng, Ya Zhang, Ivor Wai-Hung Tsang · 2020 · IEEE Transactions on Pattern Analysis and Machine Intelligence
73. Edge Contraction Pooling for Graph Neural Networks
    Frederik Diehl · 2019 · arXiv (Cornell University)
74. Federated Graph Machine Learning
    Xingbo Fu, Binchi Zhang, Yushun Dong, Chen Chen, Jundong Li · 2022 · ACM SIGKDD Explorations Newsletter
75. Algorithmic fairness datasets: the story so far
    Alessandro Fabris, Stefano Messina, Gianmaria Silvello, Gian Antonio Susto · 2022 · Data Mining and Knowledge Discovery
76. Generative Adversarial Networks for Spatio-temporal Data: A Survey
    Gao, Nan, Xue, Hao, Shao, Wei, Zhao, Sichen, Qin, Kyle Kai, Prabowo, Arian, Rahaman, Mohammad Saiedur, Salim, Flora D. · 2022 · ACM Transactions on Intelligent Systems and Technology
77. Survey on graph embeddings and their applications to machine learning problems on graphs
    Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Šubelj · 2021 · PeerJ Computer Science
78. Powerful Graph Convolutional Networks with Adaptive Propagation Mechanism for Homophily and Heterophily
    Tao Wang, Di Jin, Rui Wang, Dongxiao He, Yuxiao Huang · 2022 · Proceedings of the AAAI Conference on Artificial Intelligence
79. Hyperbolic Graph Attention Network
    Yiding Zhang, Xiao Wang, Chuan Shi, Xunqiang Jiang, Yanfang Fanny Ye · 2021 · IEEE Transactions on Big Data
80. ImGAGN
    Liang Qu, Huaisheng Zhu, Ruiqi Zheng, Yuhui Shi, Hongzhi Yin · 2021 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
81. Graph convolutional networks for graphs containing missing features
    Hibiki Taguchi, Xin Liu, Tsuyoshi Murata · 2020 · Future Generation Computer Systems
82. A modified DeepWalk method for link prediction in attributed social network
    Kamal Berahmand, Elahe Nasiri, Mehrdad Rostami, Saman Forouzandeh · 2021 · Computing
83. AGL
    Dalong Zhang, Xin Sunny Huang, Ziqi Liu, Jun Zhou, Zhiyang Hu, Xianzheng Song, Zhibang Ge, Lin Wang, Zhiqiang Zhang, Yuan Qi · 2020 · Proceedings of the VLDB Endowment
84. Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks
    Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charų C. Aggarwal, Prasenjit Mitra, Suhang Wang · 2020 · ACM International Conference on Information and Knowledge Management (CIKM)
85. Detecting Phishing Scams on Ethereum Based on Transaction Records
    Qi Yuan, Baoying Huang, Jie Zhang, Jiajing Wu, Haonan Zhang, Xi Zhang · 2020 · IEEE International Symposium on Circuits and Systems proceedings
86. Heterogeneous Hypergraph Embedding for Graph Classification
    Xiangguo Sun, Hongzhi Yin, Bo Liu, Hongxu Chen, Jiuxin Cao, Yingxia Shao, Quoc Viet Hung Nguyen · 2021 · ACM International Conference on Web Search and Data Mining (WSDM)
87. Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic
    Shobeir Fakhraei, Bert Huang, Louiqa Raschid, Lise C. Getoor · 2014 · IEEE Transactions on Computational Biology and Bioinformatics
88. Graph Attention Multi-Layer Perceptron
    Wentao Zhang, Ziqi Yin, Zeang Sheng, Yang Li, Wen Ouyang, Xiaosen Li, Yangyu Tao, Zhi Chun Yang, Bin Cui · 2022 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
89. GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
    Binghui Wang, Ang Li, Meng Pang, Hai Helen Li, Yiran Chen · 2022 · Proceedings
90. A Unified Framework for Community Detection and Network Representation Learning
    Cunchao Tu, Xiangkai Zeng, Hao Wang, Zhengyan Zhang, Zhiyuan Liu, Maosong Sun, Bo Zhang, Leyu Lin · 2018 · IEEE Transactions on Knowledge and Data Engineering
91. Adversarial Attacks on Graph Neural Networks
    Daniel Zügner, Oliver Borchert, Amir Akbarnejad, Stephan Günnemann · 2020 · ACM Transactions on Knowledge Discovery from Data
92. Co-Embedding of Nodes and Edges With Graph Neural Networks
    Xiaodong Jiang, Ronghang Zhu, Pengsheng Ji, Sheng Li · 2020 · IEEE Transactions on Pattern Analysis and Machine Intelligence
93. Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
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94. Embedding Graph Auto-Encoder for Graph Clustering
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95. Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
    Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Xin Wu, Bingzhe Wu, Ziqi Liu, Li Wang, Xiaolin Zheng · 2022 · International Joint Conference on Artificial Intelligence
96. S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking
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97. Learning statistical models from relational data
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98. BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
    Mingguo He, Zhewei Wei, Zengfeng Huang, Hongteng Xu · 2021 · arXiv (Cornell University)
99. Graph Neural Ordinary Differential Equations
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100. Graph Neural Network Encoding for Community Detection in Attribute Networks
     Jianyong Sun, Wei Zheng, Qingfu Zhang, Zongben Xu · 2022 · IEEE Transactions on Cybernetics