The DOI matches its Crossref record; 72 of the 73 references it cites were found in a registry or an index.
Collective Classification in Network Data
Open accessAbstract
Many real‐world applications produce networked data such as the worldwide web (hypertext documents connected through hyperlinks), social networks (such as people connected by friendship links), communication networks (computers connected through communication links), and biological networks (such as protein interaction networks). A recent focus in machine‐learning research has been to extend traditional machine‐learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real‐world data.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The Cora data set contains 2708 machine learning publications grouped into seven classes.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [117]
In the citing paper
“Each paper in Pubmed is represented by a term frequency-inverse document frequency (TF-IDF) vector.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [117]
In the citing paper
“The Pubmed data set contains 19717 diabetes-related publications.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [117]
In the citing paper
“Each paper in Cora and Citeseer is represented by a one-hot vector indicating the presence or absence of a word from a dictionary.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [117]
In the citing paper
“The Citeseer data set contains 3327 scientific papers grouped into six classes.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [117]
In the citing paper
“Exact inference in the collective classification problem is NP-hard, and solutions have focused on the use of approximate inference algorithm which may not be guaranteed to converge [37].”
From DeepWalk: online learning of social representations (2014) · cites this paper as [37]
In the citing paper
“Exact inference in the collective classification problem is NP-hard, and solutions have focused on the use of approximate inference algorithm which may not be guaranteed to converge [37].”
From DeepWalk: online learning of social representations (2014) · cites this paper as [37]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Advances in Neural Information Processing Systems 14
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,187
Markov Chain Monte Carlo in Practice
Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Factor graphs and the sum-product algorithm
Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910572 · Cited by 6,486
Near Shannon limit error-correcting coding and decoding : Turbo-codes
Berrou, C. (1993) · International Conference on Communications · Cited by 4,264
On the Statistical Analysis of Dirty Pictures
Besag, J. E. (1986) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1986.tb01412.x · Cited by 4,204
Flagged first, then by citations
1 of the 73 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
- Craven M., 1998, Proceedings of the Fifteenth National Conference on Artificial Intelligence
- Lu Q., 2003, Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003)
- Macskassy S., 2007, Proceedings of the Twenty‐Second Conference on Artificial Intelligence
- Neville J., 2000, Learning Statistical Models from Relational Data: Papers from the AAAI Workshop
- Taskar B., 2001, Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence
- Taskar B., 2003, Proceedings of the Seventeenth Annual Neural Information Processing Systems Conference
- Yanover C., 2002, Proceedings of the Sixteenth Annual Neural Information Processing Systems Conference
Cited by
Sorted by citations
Items 401–500 of 1,005
Multi-Label Collective Classification
Kong, X., Shi, X., & Yu, P. S. (2011) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972818.53 · Cited by 56
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential EquationsPreprint
Eliasof, M., Haber, E., & Treister, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.01938 · Cited by 61
Rossi, E., Kenlay, H., Gorinova, M. I., Chamberlain, B. P., Dong, X., & Bronstein, M. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.12128 · Cited by 67
Unsupervised Feature Selection on Networks: A Generative View
Wei, X., Cao, B., & Yu, P. S. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10309 · Cited by 55
Toward Time-Evolving Feature Selection on Dynamic Networks
Li, J., Hu, X., Jian, L., & Liu, H. (2016) · Proceedings · doi:10.1109/icdm.2016.0127 · Cited by 55
Semi-supervised classification by graph p-Laplacian convolutional networks
Fu, S., Liu, W., Zhang, K., Zhou, Y., & Tao, D. (2021) · Information Sciences · doi:10.1016/j.ins.2021.01.075 · Cited by 55
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
Tan, Q., Zhang, X., Liu, N., Zha, D., Li, L., Chen, R., Choi, S.-H., & Hu, X. (2023) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3539597.3570407 · Cited by 57
GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification
Li, W.-Z., Wang, C., Xiong, H., & Lai, J. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599374 · Cited by 55
PAC-Bayes analysis of multi-view learning
Sun, S., Shawe-Taylor, J. S., & Mao, L. (2016) · Information Fusion · doi:10.1016/j.inffus.2016.09.008 · Cited by 55
Simplifying Architecture Search for Graph Neural NetworkPreprint
Zhao, H., Wei, L., & Yao, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.11652 · Cited by 63
Elastic Graph Neural NetworksPreprint
Liu, X., Jin, W., Ma, Y., Li, Y., Liu, H., Wang, Y., Yan, M., & Tang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.06996 · Cited by 55
Hierarchical Prototype Networks for Continual Graph Representation Learning
Zhang, X., Song, D., & Tao, D. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3186909 · Cited by 54
Scalable text and link analysis with mixed-topic link models
Zhu, Y., Yan, X., Getoor, L. C., & Moore, C. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487693 · Cited by 55
Self-citationTowards a piRNA prediction using multiple kernel fusion and support vector machine
Brayet, J., Zehraoui, F., Jeanson, L., Israeli, D., & Tahi, F. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu441 · Cited by 54
Heterogeneous source consensus learning via decision propagation and negotiation
Gao, J., Fan, W., Sun, Y., & Han, J. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557061 · Cited by 54
Yang, D., Wang, S., Li, C., Zhang, X., & Li, Z. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132975 · Cited by 54
On the Equivalence between Positional Node Embeddings and Structural Graph RepresentationsPreprint
Srinivasan, B., & Ribeiro, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00452 · Cited by 73
He, C., Xie, T., Rong, Y., Huang, W., Huang, J., Ren, X., & Shahabi, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.11994 · Cited by 54
Learning to Make Predictions on Graphs with Autoencoders
Tran, P. V. (2018) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00034 · Cited by 53
Hypergraph Collaborative Network on Vertices and Hyperedges
Wu, H., Yan, Y., & Ng, M. K. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3178156 · Cited by 53
Deep Dual Graph attention Auto-Encoder for community detection
Wu, X., Lu, W., Quan, Y., Miao, Q., & Sun, P. G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122182 · Cited by 53
Network regression with predictive clustering trees
Stojanova, D., Ceci, M., Appice, A., & Džeroski, S. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0278-6 · Cited by 53
A nonparametric classification method based on K-associated graphs
Bertini, J. R., Zhao, L., da Motta, R. C., & Lopes, A. D. A. (2011) · Information Sciences · doi:10.1016/j.ins.2011.07.043 · Cited by 53
Efficient Graph Deep Learning in TensorFlow with tf_geometric
Hu, J., Qian, S., Fang, Q., Wang, Y., Zhao, Q., Zhang, H., & Xu, C. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3478322 · Cited by 53
GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended AnimationPreprint
Zhang, J., & Meng, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05729 · Cited by 59
Graph Geometry Interaction LearningPreprint
Zhu, S., Pan, S., Zhou, C. D., Wu, J. X., Cao, Y., & Wang, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12135 · Cited by 54
Hypergraph Convolution on Nodes-Hyperedges Network for Semi-Supervised Node Classification
Wu, H., & Ng, M. K. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3494567 · Cited by 52
Learning Strong Graph Neural Networks with Weak Information
Liu, Y., Ding, K.-Z., Wang, J., Lee, V., Liu, H., & Pan, S. (2023) · Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599410 · Cited by 75
Prediction in Economic Networks
Dhar, V., Geva, T., Oestreicher-Singer, G., & Sundararajan, A. (2014) · Information Systems Research · doi:10.1287/isre.2013.0510 · Cited by 52
A United Approach to Learning Sparse Attributed Network Embedding
Wang, H., Chen, E., Liu, Q., Xu, T., Du, D., Su, W., & Zhang, X. (2018) · Proceedings · doi:10.1109/icdm.2018.00071 · Cited by 53
Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data
Lin, W., Gao, Z., & Li, B. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00423 · Cited by 52
Wang, C., Qiu, Y., Gao, D., & Scherer, S. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01335 · Cited by 57
Debiased Graph Neural Networks With Agnostic Label Selection Bias
Fan, S., Wang, X., Shi, C., Kuang, K., Liu, N., & Wang, B. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3141260 · Cited by 52
I’m Me, We’re Us, and I’m Us: Tri-directional Contrastive Learning on Hypergraphs
Lee, D., & Shin, K. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.26019 · Cited by 55
Huo, C., Jin, D., Li, Y., He, D., Yang, Y.-B., & Wu, L. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25553 · Cited by 51
Simple and deep graph attention networks
Su, G., Wang, H., Zhang, Y., Zhang, W., & Lin, X. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111649 · Cited by 51
Milli, L., Monreale, A., Rossetti, G., Giannotti, F., Pedreschi, D., & Sebastiani, F. (2013) · Proceedings · doi:10.1109/icdm.2013.122 · Cited by 51
Learning Graph Dynamics using Deep Neural Networks
Narayan, A., & Roe, P. H. O. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.03.074 · Cited by 51
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models
Zang, X., Xie, Y., Chen, J., & Yuan, B. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/458 · Cited by 51
Self-supervised Consensus Representation Learning for Attributed Graph
Liu, C., Wen, L., Kang, Z., Luo, G., & Tian, L. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475416 · Cited by 52
On Self-Distilling Graph Neural Network
Chen, Y., Bian, Y., Xiao, X., Rong, Y., Xu, T., & Huang, J. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/314 · Cited by 51
Latent Multi-group Membership Graph ModelPreprint
Kim, M., & Leskovec, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.4546 · Cited by 51
Deep Iterative and Adaptive Learning for Graph Neural NetworksPreprint
Chen, Y., Wu, L., & Zaki, M. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.07832 · Cited by 58
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges
Ren, J. F., Xia, F., Lee, I., Hoshyar, A. N., & Aggarwal, C. C. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3570906 · Cited by 55
Graph contrastive learning with implicit augmentations
Liang, H., Du, X., Zhu, B., Ma, Z., Chen, K., & Gao, J. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.04.001 · Cited by 51
Collaborative contrastive learning for hypergraph node classification
Wu, H., Li, N., Zhang, J., Chen, S., Ng, M. K., & Long, J. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109995 · Cited by 51
Jannesari, V., Keshvari, M., & Berahmand, K. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122799 · Cited by 50
Coloring large complex networks
Rossi, R. A., & Ahmed, N. K. (2014) · Social Network Analysis and Mining · doi:10.1007/s13278-014-0228-y · Cited by 50
Active learning for node classification in assortative and disassortative networks
Moore, C., Yan, X., Zhu, Y., Rouquier, J., & Lane, T. D. R. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020552 · Cited by 51
Conditional Random Field Enhanced Graph Convolutional Neural Networks
Gao, H., Pei, J., & Huang, H. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330888 · Cited by 51
Wang, B., Jia, J., & Gong, N. Z. (2019) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2019.23226 · Cited by 50
Bisimulation-based Approximate Lifted InferencePreprint
Sen, P., Deshpande, A., & Getoor, L. C. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2616 · Cited by 50
Self-citationA Flexible Generative Framework for Graph-based Semi-supervised LearningPreprint
Ma, J., Tang, W., Zhu, J., & Mei, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10769 · Cited by 50
Graph fairing convolutional networks for anomaly detection
Mesgaran, M., & Hamza, A. B. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109960 · Cited by 52
GraphHop: An Enhanced Label Propagation Method for Node Classification
Xie, T., Wang, B., & Kuo, C. ‐. J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3157746 · Cited by 51
Yun, S., Kim, K., Yoon, K., & Park, C. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557381 · Cited by 55
Towards Machine Learning on the Semantic Web
Volker Tresp, C., Bundschus, M., Rettinger, A., & Huang, Y. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89765-1_17 · Cited by 49
A Novel Method on Information Recommendation via Hybrid Similarity
Zhao, Q., Wang, C., Wang, P., Zhou, M., & Jiang, C. (2016) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2016.2633573 · Cited by 49
Zhu, Z., Fan, X., Chu, X., & Bi, J. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403169 · Cited by 49
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning
Ding, K., Wang, J., Caverlee, J., & Liu, H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i6.20605 · Cited by 49
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence CalibrationPreprint
Wang, X., Liu, H., Shi, C., & Yang, C. (2021) · Neural Information Processing Systems · doi:10.48550/arxiv.2109.14285 · Cited by 161
Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification
Xu, B., Huang, J., Hou, L., Shen, H., Gao, J., & Cheng, X. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401308 · Cited by 48
Khatua, A., Mailthody, V. S., Taleka, B., Ma, T., Song, X., & Hwu, W. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599843 · Cited by 61
Linearized and single-pass belief propagation
Gatterbauer, W., Günnemann, S., Koutra, D., & Faloutsos, C. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2735479.2735490 · Cited by 48
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples
Duan, W., Xuan, J., Qiao, M., & Lü, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i6.20608 · Cited by 50
Label-dependent node classification in the network
Kazienko, P., & Kajdanowicz, T. J. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.04.047 · Cited by 48
The Atlas for the Aspiring Network ScientistPreprint
Coscia, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.00863 · Cited by 66
Efficient Partial Order Preserving Unsupervised Feature Selection on Networks
Wei, X., Xie, S., & Yu, P. S. (2015) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611974010.10 · Cited by 47
Graph-Fraudster: Adversarial Attacks on Graph Neural Network-Based Vertical Federated Learning
Chen, J., Huang, G., Zheng, H., Yu, S., Jiang, W., & Cui, C. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3161016 · Cited by 47
Betty: Enabling Large-Scale GNN Training with Batch-Level Graph Partitioning
Yang, S., Zhang, M., Dong, W., & Li, D. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3575693.3575725 · Cited by 47
Wu, L., Xia, J., Gao, Z., Lin, H., Tan, C., & Li, S. Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26412-2_32 · Cited by 47
Exploiting Neighbor Effect: Conv-Agnostic GNN Framework for Graphs With Heterophily
Chen, J., Chen, S., Gao, J., Huang, Z., Zhang, J., & Pu, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3267902 · Cited by 51
Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One
Zhang, H., Zhu, Y., & Li, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3392782 · Cited by 47
Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions
Feng, F., He, X., Zhang, H., & Chua, T. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3077524 · Cited by 47
Negative sampling strategies for contrastive self-supervised learning of graph representations
Hafidi, H., Ghogho, M., Ciblat, P., & Swami, A. (2021) · Signal Processing · doi:10.1016/j.sigpro.2021.108310 · Cited by 47
Semi-supervised Anomaly Detection on Attributed Graphs
Kumagai, A., Iwata, T., & Fujiwara, Y. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533507 · Cited by 53
Path-based multi-sources localization in multiplex networks
Cheng, L., Li, X., Han, Z., Luo, T., Ma, L., & Zhu, P. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.112139 · Cited by 47
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training dataPreprint
Zhu, Q., Ponomareva, N., Han, J., & Perozzi, B. (2021) · Neural Information Processing Systems · doi:10.48550/arxiv.2108.01099 · Cited by 155
Regularizing deep networks with prior knowledge: A constraint-based approach
Roychowdhury, S., Diligenti, M., & Gori, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106989 · Cited by 46
RAW-GNN: RAndom Walk Aggregation based Graph Neural Network
Jin, D., Wang, R., Ge, M., He, D., Li, X., Lin, W. T., & Zhang, W. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/293 · Cited by 46
Graph coloring with physics-inspired graph neural networks
Schuetz, M. J. A., Brubaker, J. K., Zhu, Z., & Katzgraber, H. G. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.043131 · Cited by 46
Ju, M., Fan, Y., Zhang, C., & Ye, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25558 · Cited by 48
Multi-type clustering and classification from heterogeneous networks
Pio, G., Serafino, F., Malerba, D., & Ceci, M. (2017) · Information Sciences · doi:10.1016/j.ins.2017.10.021 · Cited by 46
Collective Classification of Spam Campaigners on Twitter
Gupta, S., Khattar, A., Gogia, A., Kumaraguru, P., & Chakraborty, T. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186119 · Cited by 46
Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs
Li, C., Qin, X., Xu, X., Yang, D., & Wei, G. (2020) · IEEE Access · doi:10.1109/access.2020.2999520 · Cited by 47
Huu, T., Nguyen, D. M., Bekoulis, G., Munteanu, A., & Deligiannis, N. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114711 · Cited by 46
Optimization of Graph Neural Networks with Natural Gradient Descent
Izadi, M. R., Fang, Y., Stevenson, R., & Lin, L. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378063 · Cited by 51
Dual-decoder graph autoencoder for unsupervised graph representation learning
Sun, D., Li, D., Ding, Z., Zhang, X., & Tang, J. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107564 · Cited by 46
GRAPHAF: A FLOW-BASED AUTOREGRESSIVE MODEL FOR MOLECULAR GRAPH GENERATIONPreprint
Shi, C. (2025) · arXiv (Cornell University) · doi:10.57702/zgp1231i · Cited by 46
Graph neural networks at the Large Hadron Collider
Dezoort, G., Battaglia, P., Biscarat, C., & Vlimant, J.-R. (2023) · Nature Reviews Physics · doi:10.1038/s42254-023-00569-0 · Cited by 47
Neural message passing on high order paths
Flam-Shepherd, D., Wu, T., Friederich, P., & Aspuru‐Guzik, A. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abf5b8 · Cited by 53
Distribution-Induced Bidirectional Generative Adversarial Network for Graph Representation Learning
Zheng, S., Zhu, Z., Zhang, X., Liu, Z., Cheng, J., & Zhao, Y. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00725 · Cited by 45
Edge-Featured Graph Attention NetworkPreprint
Chen, J., & Chen, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07671 · Cited by 54
Eigen-GNN: a Graph Structure Preserving Plug-in for GNNs
Zhang, Z., Cui, P., Pei, J., Wang, X., & Zhu, W. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3112746 · Cited by 44
Model Degradation Hinders Deep Graph Neural Networks
Zhang, W., Sheng, Z., Yin, Z., Jiang, Y., Xia, Y., Gao, J., Yang, Z. C., & Cui, B. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539374 · Cited by 44
Simplicial Complex Neural Networks
Wu, H., Yip, A. M., Long, J., Zhang, J., & Ng, M. K. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3323624 · Cited by 47
MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
Yu, X., Zhou, C., Fang, Y., & Zhang, X. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645423 · Cited by 48
Batch Mode Active Learning for Networked Data
Shi, L., Zhao, Y., & Tang, J. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2089094.2089109 · Cited by 44
MV-GCN: Multi-View Graph Convolutional Networks for Link Prediction
Li, Z., Liu, Z., Huang, J., Tang, G., Duan, Y., Zhang, Z., & Yang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2957306 · Cited by 44
DeepStochLog: Neural Stochastic Logic Programming
Winters, T., Marra, G., Manhaeve, R., & Raedt, L. D. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i9.21248 · Cited by 56
Metadata
- Authors
- Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., & Eliassi‐Rad, T.
- Affiliations
- University of Maryland, College Park (US)
- Venue
- AI Magazine
- Publisher
- Wiley
- Published
- 2008-09
- Volume
- 29
- Issue
- 3
- Pages
- 93-106
- ISSN
- 0738-4602 (ISSN-L)
- 2371-9621
- OpenAlex
- W2153959628
- Cited by
- 3,293
- References
- 73
- Citations by year
- 2026: 186 · 2025: 416 · 2024: 409 · 2023: 393 · 2022: 346 · 2021: 437 · 2020: 325 · 2019: 205 · 2018: 100 · 2017: 69 · 2016: 52 · 2015: 67 · 2014: 62 · 2013: 59 · 2012: 50
- FWCI
- 29.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- AI Magazine (publishedVersion)
- University Libraries (University of Maryland) (submittedVersion)
- Funders
- National Science Foundation (0308030)
- U.S. Department of Energy (DE‐AC52‐07NA27344)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- collective classification, network data, node classification, inference algorithms, social networks, biological networks, communication networks, protein-protein interaction network, algorithm comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
