Collective Classification in Network Data

Cited by (1,004)

Items 201–300 of 1,004

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2. Is Homophily a Necessity for Graph Neural Networks?
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3. Privacy in Social Networks: A Survey
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4. Bilinear Graph Neural Network with Neighbor Interactions
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5. Hybrid Graph Convolutional Networks for Semi-Supervised Classification
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6. Homophily, Structure, and Content Augmented Network Representation Learning
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7. Integrating Prior Knowledge into Deep Learning
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8. Column Networks for Collective Classification
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9. Amalgamating Knowledge from Heterogeneous Graph Neural Networks
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10. Research Commentary —Information in Digital, Economic, and Social Networks
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11. Subgraph Federated Learning with Missing Neighbor Generation
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12. InfoGCL: Information-Aware Graph Contrastive Learning
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13. Social network analytics for churn prediction in telco: Model building, evaluation and network architecture
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14. Learning latent representations of nodes for classifying in heterogeneous social networks
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15. Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
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16. All-optical graph representation learning using integrated diffractive photonic computing units
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17. Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
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18. Graph Regularized Nonnegative Matrix Factorization for Community Detection in Attributed Networks
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19. Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
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20. FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference
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21. HNHN: Hypergraph Networks with Hyperedge Neurons
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22. GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
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23. MIPaaL: Mixed Integer Program as a Layer
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24. Deep Learning on Graphs
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25. A Modular Machine Learning System for Flow-Level Traffic Classification in Large Networks
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26. Paper2vec: Combining Graph and Text Information for Scientific Paper Representation
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27. Measuring and Improving the Use of Graph Information in Graph Neural Networks
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28. Non-Negative Matrix Factorizations for Multiplex Network Analysis
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29. Joint Identification of Network Communities and Semantics via Integrative Modeling of Network Topologies and Node Contents
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30. Relation regularized matrix factorization
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31. Semi-supervised Embedding in Attributed Networks with Outliers
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32. Community detection in incomplete information networks
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33. Adversarial Training Methods for Network Embedding
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34. Search to aggregate neighborhood for graph neural network
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35. GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
    Marc Brockschmidt · 2019 · arXiv (Cornell University)
36. Directed Graph Convolutional Network
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37. SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
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38. Individual Fairness for Graph Neural Networks
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39. Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks
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40. On Proximity and Structural Role-based Embeddings in Networks
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41. Flow2GNN: Flexible Two-Way Flow Message Passing for Enhancing GNNs Beyond Homophily
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42. Linked Document Embedding for Classification
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43. Flow2Vec: value-flow-based precise code embedding
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44. On the integration of symbolic and sub-symbolic techniques for XAI: A survey
    Roberta Calegari, Giovanni Ciatto, Andrea Omicini · 2020 · Intelligenza Artificiale
45. Reinforcement Learning on Graphs: A Survey
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46. Vision HGNN: An Image is More than a Graph of Nodes
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47. Graph convolutional neural networks via scattering
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48. SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
    Bahare Fatemi, Layla El Asri, Seyed Mehran Kazemi · 2021 · arXiv (Cornell University)
49. Collective Classification of Congressional Floor-Debate Transcripts
    Clint Burfoot, Steven Bird, Timothy Baldwin · 2011
50. Community aware random walk for network embedding
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51. Adversarial Privacy-Preserving Graph Embedding Against Inference Attack
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52. Graph Relearn Network: Reducing performance variance and improving prediction accuracy of graph neural networks
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53. Understanding and bridging the gaps in current GNN performance optimizations
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54. Simplifying approach to node classification in Graph Neural Networks
    Sunil Kumar Maurya, Xin Liu, Tsuyoshi Murata · 2022 · Journal of Computational Science
55. A graph neural network-based node classification model on class-imbalanced graph data
    Zhenhua Huang, Yinhao Tang, Yunwen Chen · 2022 · Knowledge-Based Systems
56. Hierarchical message-passing graph neural networks
    Zhiqiang Zhong, Cheng–Te Li, Jun Pang · 2022 · Data Mining and Knowledge Discovery
57. SDAC-DA: Semi-Supervised Deep Attributed Clustering Using Dual Autoencoder
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58. Graph OLAP: a multi-dimensional framework for graph data analysis
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59. Lifted graphical models: a survey
    Angelika Kimmig, Lilyana Simeonova Mihalkova, Lise C. Getoor · 2014 · Machine Learning
60. Towards feature selection in network
    Quanquan Gu, Jiawei Han · 2011 · ACM International Conference on Information and Knowledge Management (CIKM)
61. Cross View Link Prediction by Learning Noise-resilient Representation Consensus
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62. Policy-GNN: Aggregation Optimization for Graph Neural Networks
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63. On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
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64. Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering
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65. Multiple location profiling for users and relationships from social network and content
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66. Fuzzy Clustering in a Complex Network Based on Content Relevance and Link Structures
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67. User Profile Preserving Social Network Embedding
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68. Graph Normalizing Flows
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69. Independence Promoted Graph Disentangled Networks
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70. Semi-Implicit Graph Variational Auto-Encoders
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71. Echo state graph neural networks with analogue random resistive memory arrays
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72. Structural property-aware multilayer network embedding for latent factor analysis
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73. DGCNN: Disordered graph convolutional neural network based on the Gaussian mixture model
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74. Adaptive Structural Fingerprints for Graph Attention Networks
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75. A Hyperbolic-to-Hyperbolic Graph Convolutional Network
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76. Representation Learning for Natural Language Processing
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77. CensNet: Convolution with Edge-Node Switching in Graph Neural Networks
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78. Model Stealing Attacks Against Inductive Graph Neural Networks
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79. On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
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80. Node Attribute-enhanced Community Detection in Complex Networks
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81. Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
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82. Tackling Over-Smoothing for General Graph Convolutional Networks
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83. Collective Stance Classification of Posts in Online Debate Forums
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84. Graph-Revised Convolutional Network
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85. Adversarial Attack on Large Scale Graph
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86. Unnoticeable Backdoor Attacks on Graph Neural Networks
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87. From statistical relational to neurosymbolic artificial intelligence: A survey
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88. Scalable attack on graph data by injecting vicious nodes
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89. Explainability-based Backdoor Attacks Against Graph Neural Networks
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90. FA-GNN: Filter and Augment Graph Neural Networks for Account Classification in Ethereum
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91. Hypergraph Structure Learning for Hypergraph Neural Networks
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92. A novel relation aware wrapper method for feature selection
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93. Polarized message-passing in graph neural networks
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94. Certifiable Robustness to Graph Perturbations
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95. Towards Self-Explainable Graph Neural Network
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96. The Evolution of Distributed Systems for Graph Neural Networks and Their Origin in Graph Processing and Deep Learning: A Survey
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97. Community Detection Based on Structure and Content: A Content Propagation Perspective
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98. PPNE: Property Preserving Network Embedding
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99. Neural Graph Learning
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100. Fast Haar Transforms for Graph Neural Networks
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