Collective Classification in Network Data

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Multiscale Emotion Representation Learning for Affective Image Recognition

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What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured Data

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Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-Supervision

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Black-box Adversarial Attack and Defense on Graph Neural Networks

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Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification

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Mutual Boost Network for Attributed Graph Clustering

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Scalable Attributed-Graph Subspace Clustering

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A Black-Box Adversarial Attack Method via Nesterov Accelerated Gradient and Rewiring Towards Attacking Graph Neural Networks

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Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization

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Adversarially Robust Neural Architecture Search for Graph Neural Networks

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Toward Effective Semi-supervised Node Classification with Hybrid Curriculum Pseudo-labeling

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Paired Restricted Boltzmann Machine for Linked Data

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Early-Bird GCNs: Graph-Network Co-optimization towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery Tickets

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From Community to Role-based Graph Embeddings.Preprint

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Maximum Entropy Weighted Independent Set Pooling for Graph Neural NetworksPreprint

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BOURNE: Bootstrapped Self-Supervised Learning Framework for Unified Graph Anomaly Detection

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Can Graph Neural Networks be Adequately Explained? A Survey

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Distance metric learning using dropout

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Quantification in social networks

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Differentially private graph publishing with degree distribution preservation

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Learnable Hypergraph Laplacian for Hypergraph Learning

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Balancing topology structure and node attribute in evolutionary multi-objective community detection for attributed networks

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Semi-supervised classification via full-graph attention neural networks

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A general framework for quantifying aleatoric and epistemic uncertainty in graph neural networks

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SAIRUS: Spatially-aware identification of risky users in social networks

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Continual graph learning: A survey

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LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity

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A community detection model using node embedding approach and graph convolutional network with clustering technique

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Rank Consistency based Multi-View Learning

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Edge Weight Regularization over Multiple Graphs for Similarity Learning

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A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering

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A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations

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Bayesian Graph Convolutional Neural Networks via Tempered MCMC

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GraphAttacker: A General Multi-Task Graph Attack Framework

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Beltrami Flow and Neural Diffusion on GraphsPreprint

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Geometric graphs from data to aid classification tasks with Graph Convolutional Networks

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EATSA-GNN: Edge-Aware and Two-Stage attention for enhancing graph neural networks based on teacher–student mechanisms for graph node classification

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GZOO: Black-Box Node Injection Attack on Graph Neural Networks via Zeroth-Order Optimization

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FG-SMOTE: Towards Fair Node Classification with Graph Neural Network

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Incomplete graph learning: A comprehensive survey

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Latent Space Model for Multi-Modal Social Data

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HINMINE: heterogeneous information network mining with information retrieval heuristics

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G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency

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Continuous Encoding for Overlapping Community Detection in Attributed Network

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DGSLN: Differentiable graph structure learning neural network for robust graph representations

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Tackling Over-Smoothing in Graph Convolutional Networks With EM-Based Joint Topology Optimization and Node Classification

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Learning to Quantify

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Graph Neural Networks with Diverse Spectral Filtering

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Structure-Aware DropEdge Toward Deep Graph Convolutional Networks

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Correntropy-Induced Wasserstein GCN: Learning Graph Embedding via Domain Adaptation

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Joint orthogonal symmetric non-negative matrix factorization for community detection in attribute network

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Inferring the Travel Purposes of Passenger Groups for Better Understanding of Passengers

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Combination of links and node contents for community discovery using a graph regularization approach

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Encoding robust representation for graph generation

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Improving social and behavior recommendations via network embedding

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Active Learning for Node Classification: An Evaluation

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Graph neural networks with multiple kernel ensemble attention

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Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees

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Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation LearningPreprint

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Learning What to Defer for Maximum Independent SetsPreprint

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Incomplete Graph Representation and Learning via Partial Graph Neural NetworksPreprint

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Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph

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Deep semi-supervised learning via dynamic anchor graph embedding in latent space

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Adaptive Kernel Graph Neural Network

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The trade-off between topology and content in community detection: An adaptive encoder–decoder-based NMF approach

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Graph representation learning based on deep generative gaussian mixture models

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DropAGG: Robust Graph Neural Networks via Drop Aggregation

Jiang, B., Chen, Y., Wang, B., Xu, H., & Luo, B. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.03.022 · Cited by 21

Multi-view enhanced zero-shot node classification

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Detecting communities with multiple topics in attributed networks via self-supervised adaptive graph convolutional network

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Defending adversarial attacks in Graph Neural Networks via tensor enhancement

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Efficient Link Prediction via GNN Layers Induced by Negative Sampling

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Case-Based Collective Inference for Maritime Object Classification

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OMNI-Prop: Seamless Node Classification on Arbitrary Label Correlation

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Collective Traffic Prediction with Partially Observed Traffic History using Location-Based Social Media

Liu, X., Kong, X., & Li, Y. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983662 · Cited by 21

Opinion Spam Detection Based on Heterogeneous Information Network

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Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification

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Brief Industry Paper: optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms

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Multi-Scale Graph Convolutional Network With Spectral Graph Wavelet Frame

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GCNFusion: An efficient graph convolutional network based model for information diffusion

Fatemi, B., Molaei, S., Pan, S., & Rahimi, S. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117053 · Cited by 21

Search-Convolutional Neural NetworksPreprint

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DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph AutoencoderPreprint

Zhang, A., & Ma, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08900 · Cited by 21

Community Detection method based on Random walk and Multi objective Evolutionary algorithm in complex networks

Dabaghi‐Zarandi, F., Afkhami, M. M., & Ashoori, M. H. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.104070 · Cited by 20

M-GWNN: Multi-granularity graph wavelet neural networks for semi-supervised node classification

Zheng, W., Qian, F., Zhao, S. G., & Zhang, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.033 · Cited by 21

Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors

Wang, Z., Hao, Z., Wang, Z., Su, H., & Zhu, J. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/108 · Cited by 20

General graph generators: experiments, analyses, and improvements

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Exploiting node-feature bipartite graph in graph convolutional networks

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A learnable sampling method for scalable graph neural networks

Zhao, W., Guo, T., Yu, X., & Han, C. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.03.015 · Cited by 20

Lifelong learning on evolving graphs under the constraints of imbalanced classes and new classes

Galke, L. P. A., Vagliano, I., Franke, B., Zielke, T., Hoffmann, M., & Scherp, A. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.04.022 · Cited by 23

Collaborative Graph Neural Networks for Attributed Network Embedding

Tan, Q., Zhang, X., Huang, X. X., Chen, H., Li, J., & Hu, X. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3298002 · Cited by 21

GCNH: A Simple Method For Representation Learning On Heterophilous Graphs

Cavallo, A., Grohnfeldt, C., Russo, M., Lovisotto, G., & Vassio, L. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191196 · Cited by 20

FL-GNNs: Robust Network Representation via Feature Learning Guided Graph Neural Networks

Wang, B., Jiang, B., & Ding, C. H. Q. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3306545 · Cited by 20

Turning Strengths into Weaknesses: A Certified Robustness Inspired Attack Framework against Graph Neural Networks

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