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Glove: Global Vectors for Word Representation

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Efficient Estimation of Word Representations in Vector Space

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Nonlinear Dimensionality Reduction by Locally Linear Embedding

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A Global Geometric Framework for Nonlinear Dimensionality Reduction

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Representation Learning: A Review and New Perspectives

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Community detection in graphs

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DeepWalk: online learning of social representations

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Molecular signatures database (MSigDB) 3.0

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Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

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Graph Few-Shot Learning via Knowledge Transfer

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Node Embedding over Temporal Graphs

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Improving bug detection via context-based code representation learning and attention-based neural networks

Li, Y., Wang, S., Nguyen, T. N., & Nguyen, S. (2019) · Proceedings of the ACM on Programming Languages · doi:10.1145/3360588 · Cited by 164

Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding

Bandyopadhyay, S., Lokesh, N., Vivek, S. V., & Murty, M. N. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371788 · Cited by 165

Multi-hop Attention Graph Neural Networks

Wang, G., Ying, R., Huang, J., & Leskovec, J. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/425 · Cited by 195

NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization

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APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding

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Provenance-based Intrusion Detection Systems: A Survey

Zipperle, M., Gottwalt, F., Chang, E. J., & Dillon, T. S. (2022) · ACM Computing Surveys · doi:10.1145/3539605 · Cited by 160

Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications

Bing, R., Yuan, G., Zhu, M., Meng, F., Ma, H., & Qiao, S. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10375-2 · Cited by 169

To Embed or Not: Network Embedding as a Paradigm in Computational Biology

Nelson, W., Žitnik, M., Wang, B., Leskovec, J., Goldenberg, A., & Sharan, R. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00381 · Cited by 164

Fault Localization with Code Coverage Representation Learning

Li, Y., Wang, S., & Nguyen, T. D. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00067 · Cited by 176

A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation

Zhu, F., Wang, Y., Chen, C., Liu, G., & Zheng, X. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/415 · Cited by 158

A Deep Reinforcement Learning Framework Based on an Attention Mechanism and Disjunctive Graph Embedding for the Job-Shop Scheduling Problem

Chen, R., Li, W., & Yang, H. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3167380 · Cited by 158

When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks

Fang, Y., Qin, Y., Luo, H., Zhao, F., Xu, B., Zeng, L., & Wang, C. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00046 · Cited by 172

Compositional Fairness Constraints for Graph EmbeddingsPreprint

Bose, A. J., & Hamilton, W. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10674 · Cited by 160

GRAM: Graph-based Attention Model for Healthcare Representation LearningPreprint

Choi, E., Bahadori, M. T., Song, L., Stewart, W. F., & Sun, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07012 · Cited by 163

Sub2Vec: Feature Learning for Subgraphs

Adhikari, B., Zhang, Y., Ramakrishnan, N., & Prakash, B. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93037-4_14 · Cited by 157

Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance

Cheng, Z., Lv, Q., Liang, J., Wang, Y., Sun, D., Pasquier, T., & Han, X. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00005 · Cited by 190

Deep Graph Matching ConsensusPreprint

Fey, M., Lenssen, J. E., Morris, C., Masci, J., & Kriege, N. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.09621 · Cited by 171

Personalized Graph Neural Networks With Attention Mechanism for Session-Aware Recommendation

Zhang, M., Wu, S., Gao, M., Jiang, X., Xu, K., & Wang, L. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3031329 · Cited by 156

Graphite: Iterative Generative Modeling of GraphsPreprint

Grover, A., Zweig, A., & Ermon, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10459 · Cited by 156

Representation Bias in Data: A Survey on Identification and Resolution Techniques

Shahbazi, N., Yin, L., Asudeh, A., & Jagadish, H. V. (2023) · ACM Computing Surveys · doi:10.1145/3588433 · Cited by 178

Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link PredictionPreprint

Zhu, Z., Zhang, Z., Xhonneux, L.-P., & Tang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06935 · Cited by 174

Large-Scale Visual Relationship Understanding

Zhang, J., Kalantidis, Y., Rohrbach, M., Paluri, M., Elgammal, A., & Elhoseiny, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019185 · Cited by 159

TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection

Li, S., Gou, G., Liu, C., Hou, C., Li, Z., & Xiong, G. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512226 · Cited by 154

Fast Gradient Attack on Network EmbeddingPreprint

Chen, J., Wu, Y., Xu, X., Chen, Y., Zheng, H., & Xuan, Q. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.02797 · Cited by 182

Fast Network Embedding Enhancement via High Order Proximity Approximation

Yang, C. H., Sun, M., Liu, Z., & Tu, C. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/544 · Cited by 154

Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism

Hu, B.-B., Zhang, Z.-Q., Shi, C., Zhou, J., Li, X.-L., & Qi, Y. (2019) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301946 · Cited by 159

Classifying Malware Represented as Control Flow Graphs using Deep Graph Convolutional Neural Network

Yan, J., Yan, G., & Jin, D. (2019) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2019.00020 · Cited by 152

Evaluating Overfit and Underfit in Models of Network Community Structure

Ghasemian, A., Hosseinmardi, H., & Clauset, A. J. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2911585 · Cited by 161

Learning Steady-States of Iterative Algorithms over Graphs

Dai, H., Kozareva, Z., Dai, B., Smola, A. J., & Song, L. (2018) · International Conference on Machine Learning · Cited by 151

DeepCrime

Huang, C., Zhang, J., Zheng, Y., & Chawla, N. V. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3271793 · Cited by 151

Motif-based Graph Self-Supervised Learning for Molecular Property PredictionPreprint

Zhang, Z., Liu, Q., Wang, H., Lu, C., & Lee, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.00987 · Cited by 156

Semi-Supervised Graph Classification: A Hierarchical Graph Perspective

Li, J., Rong, Y., Cheng, H., Meng, H. M. L., Huang, W., & Huang, J. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313461 · Cited by 150

Multi-dimensional Graph Convolutional Networks

Ma, Y., Wang, S., Aggarwal, C. C., Yin, D., & Tang, J. (2019) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611975673.74 · Cited by 152

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective

Bonner, S., Barrett, I. P., Ye, C., Swiers, R., Engkvist, O., Bender, A., Hoyt, C. T., & Hamilton, W. L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac404 · Cited by 170

Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks

Qiu, R., Huang, Z. H., Li, J., & Yin, H. (2020) · ACM Transactions on Information Systems · doi:10.1145/3382764 · Cited by 151

SNE: Signed Network Embedding

Yuan, S., Wu, X., & Xiang, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-57529-2_15 · Cited by 151

CommunityGAN: Community Detection with Generative Adversarial Nets

Jia, Y., Zhang, Q., Zhang, W., & Wang, X. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313564 · Cited by 147

Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media

Li, C., & Goldwasser, D. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1247 · Cited by 147

OWL2Vec*: embedding of OWL ontologies

Chen, J., Hu, P., Jiménez-Ruiz, E., Holter, O. M., Antonyrajah, D., & Horrocks, I. R. (2021) · Machine Learning · doi:10.1007/s10994-021-05997-6 · Cited by 168

Knowledge Graph Reasoning with Relational Digraph

Zhang, Y., & Yao, Q. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512008 · Cited by 163

Generative AI for visualization: State of the art and future directions

Ye, Y., Hao, J., Hou, Y., Wang, Z., Xiao, S., Luo, Y., & Zeng, W. (2024) · Visual Informatics · doi:10.1016/j.visinf.2024.04.003 · Cited by 167

Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

Cai, L., Chen, Z., Luo, C., Gui, J., Ni, J., Li, D., & Chen, H. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management · doi:10.1145/3459637.3481955 · Cited by 146

Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks

Cui, Z., Li, Z., Wu, S., Zhang, X.-Y., & Wang, L. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313444 · Cited by 149

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

Thafar, M. A., Raies, A. B., Albaradei, S., Essack, M., & Bajić, V. B. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00782 · Cited by 149

Modeling and Understanding Ethereum Transaction Records via a Complex Network Approach

Lin, D., Wu, J., Yuan, Q., & Zheng, Z. (2020) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2020.2968376 · Cited by 146

Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

Chen, H., Yin, H., Sun, X., Chen, T., Gabryś, B., & Musiał, K. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403201 · Cited by 147

Attention-based graph neural networks: a survey

Sun, C., Li, C., Lin, X., Zheng, T., Meng, F., Rui, X., & Wang, Z. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10577-2 · Cited by 156

Overlapping Community Detection with Graph Neural NetworksPreprint

Shchur, O., & Günnemann, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.12201 · Cited by 177

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

Zhang, Y., Yao, Q., Shao, Y., & Chen, L. (2019) · 2019 IEEE 35th International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2019.00061 · Cited by 108

Fairness in Graph Mining: A Survey

Dong, Y., Ma, J., Wang, S., Chen, C., & Li, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3265598 · Cited by 161

Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay

Zhou, F., & Cao, C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16602 · Cited by 156

subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large GraphsPreprint

Narayanan, A. S., Chandramohan, M., Chen, L., Liu, Y., & Saminathan, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.08928 · Cited by 144

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Thafar, M. A., Olayan, R. S., Ashoor, H., Albaradei, S., Bajic, V. B., Gao, X., Gojobori, T., & Essack, M. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00447-2 · Cited by 143

Temporal Network Embedding with Micro- and Macro-dynamics

Lu, Y., Wang, X., Shi, C., Yu, P. S., & Ye, Y. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357943 · Cited by 143

Biological applications of knowledge graph embedding models

Mohamed, S. K., Nounu, A., & Nováček, V. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa012 · Cited by 151

Stacking models for nearly optimal link prediction in complex networks

Ghasemian, A., Hosseinmardi, H., Galstyan, A., Airoldi, E. M., & Clauset, A. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914950117 · Cited by 150

Hyper-SAGNN: a self-attention based graph neural network for hypergraphsPreprint

Zhang, R., Zou, Y., & Ma, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02613 · Cited by 162

SIDE

Kim, J. H., Park, H., Lee, J., & Kang, U. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186117 · Cited by 142

A survey on deep learning for patent analysis

Krestel, R., Chikkamath, R., Hewel, C., & Risch, J. (2021) · World Patent Information · doi:10.1016/j.wpi.2021.102035 · Cited by 157

Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningPreprint

Li, P., Wang, Y., Wang, H., & Leskovec, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.00142 · Cited by 142

Anomaly Detection in Dynamic Graphs via Transformer

Liu, Y., Pan, S., Wang, Y. G., Xiong, F., Wang, L., Chen, Q., & Lee, V. C. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124061 · Cited by 169

MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network

Wang, H., Xu, T., Liu, Q., Lian, D., Chen, E., Du, D., Wu, H., & Su, W. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330931 · Cited by 141

Deep Dynamic Network Embedding for Link Prediction

Li, T., Zhang, J., Yu, P. S., Zhang, Y., & Yan, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2839770 · Cited by 141

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity

Liu, X., Luo, Y., Li, P., Song, S., & Peng, J. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009284 · Cited by 140

Attacking Graph-based Classification via Manipulating the Graph Structure

Wang, B., & Gong, N. Z. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3354206 · Cited by 140

Gromov-Wasserstein Learning for Graph Matching and Node EmbeddingPreprint

Xu, H., Luo, D., Hongyuan Zha, B., & Carin, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.06003 · Cited by 142

Signed Graph Attention Networks

Huang, J., Shen, H., Hou, L., & Cheng, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30493-5_53 · Cited by 151

Dynamic Heterogeneous Information Network Embedding With Meta-Path Based Proximity

Wang, X., Lu, Y., Shi, C., Wang, R., Cui, P., & Mou, S. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2993870 · Cited by 139

STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation

Lim, N., Hooi, B., Ng, S.-K., Wang, X., Goh, Y. L., Weng, R., & Varadarajan, J. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411876 · Cited by 147

Structure Based User Identification across Social Networks

Zhou, X., Liang, X., Du, X., & Zhao, J. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2784430 · Cited by 139

High-order Proximity Preserved Embedding For Dynamic Networks

Zhu, D., Cui, P., Zhang, Z., Pei, J., & Zhu, W. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2822283 · Cited by 138

GA-Based Q-Attack on Community Detection

Chen, J., Chen, L., Chen, Y.-X., Zhao, M., Yu, S., Xuan, Q., & Yang, X. (2019) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2019.2912801 · Cited by 140

Principled Multilayer Network Embedding

Liu, W., Chen, P., Yeung, S., Suzumura, T., & Chen, L. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.23 · Cited by 144

GraphACT

Zeng, H., & Prasanna, V. (2020) · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · doi:10.1145/3373087.3375312 · Cited by 143

Variational Graph Recurrent Neural NetworksPreprint

Hajiramezanali, E., Hasanzadeh, A., Duffield, N., Narayanan, K. R., Zhou, M., & Qian, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.09710 · Cited by 138

entity2rec

Palumbo, E., Rizzo, G. F., & Raphaël Troncy, A. (2017) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3109859.3109889 · Cited by 137

A general path-based representation for predicting program properties

Alon, U., Zilberstein, M., Levy, O., & Yahav, E. (2018) · Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/3192366.3192412 · Cited by 79

Heterogeneous Network Representation Learning Approach for Ethereum Identity Identification

Wang, Y., Liu, Z., Xu, J., & Yan, W. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3164719 · Cited by 136

MGAT: Multi-view Graph Attention Networks

Xie, Y., Zhang, Y., Gong, M., Tang, Z., & Han, C. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.08.021 · Cited by 138

Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview

Castellano, G., & Vessio, G. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05893-z · Cited by 143

A Graph-based Approach for Trajectory Similarity Computation in Spatial Networks

Han, P., Wang, J., Yao, D., Shang, S., & Zhang, X. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467337 · Cited by 134

Graph Barlow Twins: A self-supervised representation learning framework for graphs

Bielak, P., Kajdanowicz, T. J., & Chawla, N. V. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109631 · Cited by 134

Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study

Jiang, J., Ren, X., & Ferrara, E. (2021) · JMIRx Med · doi:10.2196/29570 · Cited by 162

The Art of Feature Engineering

Duboue, P. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671682 · Cited by 140

SkipGNN: predicting molecular interactions with skip-graph networks

Huang, K., Xiao, C., Glass, L. M., Zitnik, M., & Sun, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-77766-9 · Cited by 133

Graph Unlearning

Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., & Zhang, Y. (2022) · Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3548606.3559352 · Cited by 133

AI Meets AI

Ding, B., Das, S., Marcus, R., Wu, W., Chaudhuri, S., & Narasayya, V. R. (2019) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3299869.3324957 · Cited by 138

Deep reinforcement learning for transportation network combinatorial optimization: A survey

Wang, Q., & Tang, C. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107526 · Cited by 137

A review of recommender systems based on knowledge graph embedding

Zhang, J., Zain, A. M., Zhou, K.-Q., Chen, X., & Zhang, R. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123876 · Cited by 146

Deep Variational Network Embedding in Wasserstein Space

Zhu, D., Cui, P., Wang, D., & Zhu, W. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220052 · Cited by 132

Functional code clone detection with syntax and semantics fusion learning

Fang, C., Liu, Z., Shi, Y., Huang, J., & Shi, Q. (2020) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3395363.3397362 · Cited by 132

Learning on Attribute-Missing Graphs

Chen, X., Chen, S., Yao, J., Zheng, H., Zhang, Y., & Tsang, I. W.-H. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3032189 · Cited by 137

A Survey of Community Detection in Complex Networks Using Nonnegative Matrix Factorization

He, C., Fei, X., Cheng, Q., Li, H., Hu, Z., & Tang, Y. Z. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3114419 · Cited by 134

What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders

Li, J., Wu, R., Sun, W., Chen, L., Tian, S. F., Zhu, L., Meng, C., Zheng, Z., & Wang, W. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599546 · Cited by 132

Relation Structure-Aware Heterogeneous Information Network Embedding

Lu, Y., Shi, C., Hu, L., & Liu, Z. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014456 · Cited by 132

Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers

Bang, D., Lim, S., Lee, S., & Kim, S. (2023) · Nature Communications · doi:10.1038/s41467-023-39301-y · Cited by 132

Bipartite Graph Embedding via Mutual Information Maximization

Cao, J., Lin, X., Guo, S., Liu, L., Liu, T., & Wang, B. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441783 · Cited by 132

Graph Representation Learning

Jin, W., Ma, Y., Wang, Y., Liu, X., Tang, J., Cen, Y., Qiu, J., Tang, J., Shi, C., Ye, Y., et al. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3470824 · Cited by 130

BalNode2Vec: Balanced Random Walk based Versatile Feature Learning for Networks

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BiasedWalk: Biased Sampling for Representation Learning on Graphs

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motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks

Dareddy, M. R., Das, M., & Yang, H. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005670 · Cited by 25

Core2Vec: A Core-Preserving Feature Learning Framework for Networks

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Network2Vec: Learning Node Representation Based on Space Mapping in Networks

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