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Y.-N. Zhu, X. Luo, Y.-F. Li, B. Bu, K. Zhou, W. Zhang, and M. Lu, “Heterogeneous mini-graph neural network and its application to fraud invitation detection,” in ICDM , 2020, pp. 891–899
2020
Cited alongside, same era.
2022
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L. Wu, H. Lin, Z. Gao, C. Tan, S. Li et al. , “Graphmixup: Improving class-imbalanced node classification by reinforcement mixup and self-supervised context prediction,” ECML-PKDD , pp. 519–535, 2022
2022
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J. Park, J. Song, and E. Yang, “Graphens: Neighbor-aware ego network synthesis for class-imbalanced node classification,” in ICLR , 2022
2022
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L. Cui, X. Tang, S. Katariya, N. Rao, P. Agrawal, K. Subbian, and D. Lee, “Allie: Active learning on large-scale imbalanced graphs,” in WWW , 2022, pp. 690–698
2022
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in CVPR , 2022, pp. 10 684–10 695
2022
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C. Qiu, M. Kloft, S. Mandt, and M. Rudolph, “Raising the bar in graph-level anomaly detection,” IJCAI , pp. 2196–2203, 2022
2022
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R. Ma, G. Pang, L. Chen, and A. van den Hengel, “Deep graph-level anomaly detection by glocal knowledge distillation,” in WSDM , 2022, pp. 704–714
2022
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W. Chen, L. Tian, B. Chen, L. Dai, Z. Duan, and M. Zhou, “Deep variational graph convolutional recurrent network for multivariate time series anomaly detection,” in ICML , 2022, pp. 3621–3633
2022
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P. Qi, D. Li, and S.-K. Ng, “Mad-sgcn: Multivariate anomaly detection with self-learning graph convolutional networks,” in ICDE , 2022, pp. 1232–1244
2022
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X. Chen, Q. Qiu, C. Li, and K. Xie, “Graphad: A graph neural network for entity-wise multivariate time-series anomaly detection,” in SIGIR , 2022, pp. 2297–2302
2022
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J. Zhang, S. Wang, and S. Chen, “Reconstruction enhanced multi-view contrastive learning for anomaly detection on attributed networks,” IJCAI , pp. 2376–2382, 2022
2022
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X. Luo, J. Wu, A. Beheshti, J. Yang, X. Zhang, Y. Wang, and S. Xue, “Comga: Community-aware attributed graph anomaly detection,” in WSDM , 2022, pp. 657–665
2022
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A. Goodge, B. Hooi, S.-K. Ng, and W. S. Ng, “Lunar: Unifying local outlier detection methods via graph neural networks,” in AAAI , vol. 36, no. 6, 2022, pp. 6737–6745
2022
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S. Zhou, X. Huang, N. Liu, Q. Tan, and F.-L. Chung, “Unseen anomaly detection on networks via multi-hypersphere learning,” in SDM , 2022, pp. 262–270
2022
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F. Liu, X. Ma, J. Wu, J. Yang, S. Xue, A. Beheshti, C. Zhou, H. Peng, Q. Z. Sheng, and C. C. Aggarwal, “Dagad: Data augmentation for graph anomaly detection,” ICDM , pp. 259–268, 2022
2022
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T. Huang, Y. Pei, V. Menkovski, and M. Pechenizkiy, “Hop-count based self-supervised anomaly detection on attributed networks,” in ECML-PKDD , 2022, pp. 225–241
2022
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L. Dong, Y. Liu, X. Ao, J. Chi, J. Feng, H. Yang, and Q. He, “Bi-level selection via meta gradient for graph-based fraud detection,” in DASFAA , 2022, pp. 387–394
2022
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M. Huang, Y. Liu, X. Ao, K. Li, J. Chi, J. Feng, H. Yang, and Q. He, “Auc-oriented graph neural network for fraud detection,” in WWW , 2022, pp. 1311–1321
2022
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Z. Li, D. Chen, Q. Liu, and S. Wu, “The devil is in the conflict: Disentangled information graph neural networks for fraud detection,” ICDM , pp. 1059–1064, 2022
2022
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Z. Qin, Y. Liu, Q. He, and X. Ao, “Explainable graph-based fraud detection via neural meta-graph search,” in CIKM , 2022, pp. 4414–4418
2022
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J. Tang, J. Li, Z. Gao, and J. Li, “Rethinking graph neural networks for anomaly detection,” in ICML , 2022, pp. 21 076–21 089
2022
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F. Shi, Y. Cao, Y. Shang, Y. Zhou, C. Zhou, and J. Wu, “H2-fdetector: a gnn-based fraud detector with homophilic and heterophilic connections,” in WWW , 2022, pp. 1486–1494
2022
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X. Guo, B. Zhou, and S. Skiena, “Subset node anomaly tracking over large dynamic graphs,” in KDD , 2022, pp. 475–485
2022
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L. Deng, D. Lian, Z. Huang, and E. Chen, “Graph convolutional adversarial networks for spatiotemporal anomaly detection,” TNNLS , vol. 33, no. 6, pp. 2416–2428, 2022
2022
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M. Lu, Z. Han, S. X. Rao, Z. Zhang, Y. Zhao, Y. Shan, R. Raghunathan, C. Zhang, and J. Jiang, “Bright-graph neural networks in real-time fraud detection,” in CIKM , 2022, pp. 3342–3351
2022
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J. Cui, K. Kim, S. H. Na, and S. Shin, “Meta-path-based fake news detection leveraging multi-level social context information,” in CIKM , 2022, pp. 325–334
2022
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J. Liu, F. Xia, X. Feng, J. Ren, and H. Liu, “Deep graph learning for anomalous citation detection,” TNNLS , vol. 33, no. 6, pp. 2543–2557, 2022
2022
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C. Wang, D. B. Neill, and F. Chen, “Calibrated nonparametric scan statistics for anomalous pattern detection in graphs,” in AAAI , vol. 36, no. 4, 2022, pp. 4201–4209
2022
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T. Chen and C. Tsourakakis, “Antibenford subgraphs: Unsupervised anomaly detection in financial networks,” in KDD , 2022, pp. 2762–2770
2022
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Z. Zhang and L. Zhao, “Unsupervised deep subgraph anomaly detection,” in ICDM , 2022, pp. 753–762
2022
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K. Liu, Y. Dou, Y. Zhao, X. Ding, X. Hu, R. Zhang, K. Ding, C. Chen, H. Peng, K. Shu et al. , “Bond: Benchmarking unsupervised outlier node detection on static attributed graphs,” NeurIPS , vol. 35, pp. 27 021–27 035, 2022
2022
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Y. Zhou, Y. Cao, Y. Shang, C. Zhou, C. Song, F. Shi, and Q. Li, “Task-level relations modelling for graph meta-learning,” in ICDM , 2022, pp. 813–822
2022
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Y. Liu, M. Li, X. Li, F. Giunchiglia, X. Feng, and R. Guan, “Few-shot node classification on attributed networks with graph meta-learning,” in SIGIR , 2022, pp. 471–481
2022
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L. Zhang, S. Wang, J. Liu, Q. Lin, X. Chang, Y. Wu, and Q. Zheng, “Mul-grn: Multi-level graph relation network for few-shot node classification,” TKDE , vol. 35, no. 6, pp. 6085–6098, 2022
2022
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Z. Wu, P. Zhou, G. Wen, Y. Wan, J. Ma, D. Cheng, and X. Zhu, “Information augmentation for few-shot node classification,” in IJCAI , 2022, pp. 3601–3607
2022
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Z. Tan, K. Ding, R. Guo, and H. Liu, “Supervised graph contrastive learning for few-shot node classification,” in ECML-PKDD , 2022, pp. 394–411
2022
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Z. Tan, S. Wang, K. Ding, J. Li, and huan liu, “Transductive linear probing: A novel framework for few-shot node classification,” in LoG , 2022
2022
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Z. Tan, K. Ding, R. Guo, and H. Liu, “Graph few-shot class-incremental learning,” in WSDM , 2022, pp. 987–996
2022
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B. Lu, X. Gan, L. Yang, W. Zhang, L. Fu, and X. Wang, “Geometer: Graph few-shot class-incremental learning via prototype representation,” KDD , pp. 1152–1161, 2022
2022
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Z. Xu, K. Ding, Y.-X. Wang, H. Liu, and H. Tong, “Generalized few-shot node classification,” in ICDM , 2022, pp. 608–617
2022
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Q. Zhang, X. Wu, Q. Yang, C. Zhang, and X. Zhang, “Hg-meta: Graph meta-learning over heterogeneous graphs,” in SDM , 2022, pp. 397–405
2022
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B. Lu, X. Gan, W. Zhang, H. Yao, L. Fu, and X. Wang, “Spatio-temporal graph few-shot learning with cross-city knowledge transfer,” in KDD , 2022, pp. 1162–1172
2022
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Q. Zhang, X. Wu, Q. Yang, C. Zhang, and X. Zhang, “Few-shot heterogeneous graph learning via cross-domain knowledge transfer,” in KDD , 2022, pp. 2450–2460
2022
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Q. Yue, J. Liang, J. Cui, and L. Bai, “Dual bidirectional graph convolutional networks for zero-shot node classification,” in KDD , 2022, pp. 2408–2417
2022
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S. Wang, Y. Dong, X. Huang, C. Chen, and J. Li, “Faith: Few-shot graph classification with hierarchical task graphs,” in IJCAI , 2022, pp. 1–7
2022
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D. Crisostomi, S. Antonelli, V. Maiorca, L. Moschella, R. Marin, and E. Rodolà, “Metric based few-shot graph classification,” in LoG , 2022
2022
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K. Hassani, “Cross-domain few-shot graph classification,” in AAAI , vol. 36, no. 6, 2022, pp. 6856–6864
2022
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D. Fu, L. Fang, R. Maciejewski, V. I. Torvik, and J. He, “Meta-learned metrics over multi-evolution temporal graphs,” in KDD , 2022, pp. 367–377
2022
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H. S. de Ocáriz Borde and F. Barbero, “Graph neural network expressivity and meta-learning for molecular property regression,” in LoG , 2022
2022
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C. Yang, C. Wang, Y. Lu, X. Gong, C. Shi, W. Wang, and X. Zhang, “Few-shot link prediction in dynamic networks,” in WSDM , 2022, pp. 1245–1255
2022
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F. Xia, L. Wang, T. Tang, X. Chen, X. Kong, G. Oatley, and I. King, “Cengcn: centralized convolutional networks with vertex imbalance for scale-free graphs,” TKDE , pp. 4555–4569, 2022
2022
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Q. Sun, J. Li, H. Yuan, X. Fu, H. Peng, C. Ji, Q. Li, and P. S. Yu, “Position-aware structure learning for graph topology-imbalance by relieving under-reaching and over-squashing,” in CIKM , 2022, pp. 1848–1857
2022
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M. Chen, W. Zhang, Y. Zhu, H. Zhou, Z. Yuan, C. Xu, and H. Chen, “Meta-knowledge transfer for inductive knowledge graph embedding,” in SIGIR , 2022, pp. 927–937
2022
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L. Zhenzhen, Y. Zhang, J.-Y. Nie, and D. Li, “Improving few-shot relation classification by prototypical representation learning with definition text,” in NAACL , 2022, pp. 454–464
2022
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S. Zheng, S. Mai, Y. Sun, H. Hu, and Y. Yang, “Subgraph-aware few-shot inductive link prediction via meta-learning,” TKDE , vol. 35, no. 6, pp. 6512–6517, 2022
2022
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C. Dou, S. Wu, X. Zhang, Z. Feng, and K. Wang, “Function-words adaptively enhanced attention networks for few-shot inverse relation classification,” in IJCAI , 2022, pp. 2937–2943
2022
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X. Zhang, X. Liang, X. Zheng, B. Wu, and Y. Guo, “Multiform: Few-shot knowledge graph completion via multi-modal contexts,” in ECML-PKDD , 2022, pp. 172–187
2022
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H. Ren, Y. Cai, R. Y. Lau, H.-f. Leung, and Q. Li, “Granularity-aware area prototypical network with bimargin loss for few shot relation classification,” TKDE , vol. 35, no. 5, pp. 4852–4866, 2022
2022
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Y. Li, K. Yu, X. Huang, and Y. Zhang, “Learning inter-entity-interaction for few-shot knowledge graph completion,” in EMNLP , 2022, pp. 7691–7700
2022
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Y. Zhang, Y. Qian, Y. Ye, and C. Zhang, “Adapting distilled knowledge for few-shot relation reasoning over knowledge graphs,” in SDM , 2022, pp. 666–674
2022
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T. Zhao, D. Luo, X. Zhang, and S. Wang, “Topoimb: Toward topology-level imbalance in learning from graphs,” in LoG , 2022
2022
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Y. Zhu, Y. Lai, K. Zhao, X. Luo, M. Yuan, J. Ren, and K. Zhou, “Binarizedattack: Structural poisoning attacks to graph-based anomaly detection,” in ICDE , 2022, pp. 14–26
2022
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D. Guo, Z. Chu, and S. Li, “Fair attribute completion on graph with missing attributes,” in ICLR , 2022
2022
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Z. Chen, T. Xiao, and K. Kuang, “Ba-gnn: On learning bias-aware graph neural network,” in ICDE , 2022, pp. 3012–3024
2022
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A. Abouzeid, O.-C. Granmo, C. Webersik, and M. Goodwin, “Socially fair mitigation of misinformation on social networks via constraint stochastic optimization,” in AAAI , vol. 36, no. 11, 2022, pp. 11 801–11 809
2022
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A. Khajehnejad, M. Khajehnejad, M. Babaei, K. P. Gummadi, A. Weller, and B. Mirzasoleiman, “Crosswalk: Fairness-enhanced node representation learning,” in AAAI , vol. 36, no. 11, 2022, pp. 11 963–11 970
2022
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H. Hussain, M. Cao, S. Sikdar, D. Helic, E. Lex, M. Strohmaier, and R. Kern, “Adversarial inter-group link injection degrades the fairness of graph neural networks,” in ICDM , 2022, pp. 975–980
2022
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Y. Dong, N. Liu, B. Jalaian, and J. Li, “Edits: Modeling and mitigating data bias for graph neural networks,” in WWW , 2022, pp. 1259–1269
2022
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J. Ma, R. Guo, M. Wan, L. Yang, A. Zhang, and J. Li, “Learning fair node representations with graph counterfactual fairness,” in WSDM , 2022, pp. 695–703
2022
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N. Wang, L. Lin, J. Li, and H. Wang, “Unbiased graph embedding with biased graph observations,” in WWW , 2022, pp. 1423–1433
2022
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W. Song, Y. Dong, N. Liu, and J. Li, “Guide: Group equality informed individual fairness in graph neural networks,” in KDD , 2022, pp. 1625–1634
2022
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Y. Liu, X. Ao, F. Feng, and Q. He, “Ud-gnn: Uncertainty-aware debiased training on semi-homophilous graphs,” in KDD , 2022, pp. 1131–1140
2022
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Y. Dong, S. Wang, Y. Wang, T. Derr, and J. Li, “On structural explanation of bias in graph neural networks,” in KDD , 2022, pp. 316–326
2022
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C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” NeurIPS , vol. 35, pp. 36 479–36 494, 2022
2022
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Y. Zhang, B. Kang, B. Hooi, S. Yan, and J. Feng, “Deep long-tailed learning: A survey,” TPAMI , 2023
2023
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X. Zhu, P. Luo, Z. Zhao, T. Xu, A. Lizhiyu, Y. Yu, X. Li, and E. Chen, “Few-shot link prediction for event-based social networks via meta-learning,” in DASFAA , 2023, pp. 31–41
2023
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Y. Song, T. Wang, S. K. Mondal, and J. P. Sahoo, “A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities,” CSUR , 2023
2023
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2023
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Y. Dong, J. Ma, S. Wang, C. Chen, and J. Li, “Fairness in graph mining: A survey,” TKDE , no. 01, pp. 1–22, 2023
2023
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X. Yu, Z. Liu, Y. Fang, and X. Zhang, “Learning to count isomorphisms with graph neural networks,” in AAAI , 2023
2023
Closest in time.
J. Liu, M. He, G. Wang, N. Q. V. Hung, X. Shang, and H. Yin, “Imbalanced node classification beyond homophilic assumption,” in IJCAI , 2023
2023
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H. T. Nguyen, P. J. Liang, and L. Akoglu, “Detecting anomalous graphs in labeled multi-graph databases,” TKDD , vol. 17, no. 2, pp. 1–25, 2023
2023
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Z. Zhuang, K. M. Ting, G. Pang, and S. Song, “Subgraph centralization: A necessary step for graph anomaly detection,” SDM , pp. 703–711, 2023
2023
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Y. Gao, X. Wang, X. He, Z. Liu, H. Feng, and Y. Zhang, “Addressing heterophily in graph anomaly detection: A perspective of graph spectrum,” in WWW , 2023, pp. 1528–1538
2023
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Y. Huang, L. Wang, F. Zhang, and X. Lin, “Unsupervised graph outlier detection: Problem revisit, new insight, and superior method,” ICDE , pp. 2556–2569, 2023
2023
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Y. Gao, X. Wang, X. He, Z. Liu, H. Feng, and Y. Zhang, “Alleviating structural distribution shift in graph anomaly detection,” in WSDM , 2023, pp. 357–365
2023
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S. Tian, J. Dong, J. Li, W. Zhao, X. Xu, B. Song, C. Meng, T. Zhang, L. Chen et al. , “Sad: Semi-supervised anomaly detection on dynamic graphs,” in IJCAI , 2023
2023
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J. Hou, Y. Lei, Z. Peng, W. Lu, F. Zhang, and X. Du, “Efficient anomaly detection in property graphs,” in DASFFA , 2023, pp. 120–136
2023
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Z. Liu, X. Yu, Y. Fang, and X. Zhang, “Graphprompt: Unifying pre-training and downstream tasks for graph neural networks,” in WWW , 2023, pp. 417–428
2023
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S. Wang, Y. Dong, K. Ding, C. Chen, and J. Li, “Few-shot node classification with extremely weak supervision,” in WSDM , 2023, pp. 276–284
2023
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Z. Meng, Y. Li, P. Zhao, Y. Yu, and I. King, “Meta-learning with motif-based task augmentation for few-shot molecular property prediction,” in SDM , 2023, pp. 811–819
2023
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W. Chen, A. Tripp, and J. M. Hernández-Lobato, “Meta-learning adaptive deep kernel gaussian processes for molecular property prediction,” in ICLR , 2023
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S. Virinchi and A. Saladi, “Blade: Biased neighborhood sampling based graph neural network for directed graphs,” in WSDM , 2023, pp. 42–50
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