{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:51:21Z","timestamp":1740160281774,"version":"3.37.3"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Nos. 62262003","U21A20474"],"award-info":[{"award-number":["Nos. 62262003","U21A20474"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangxi Science and technology project","award":["Guike AA22068070"],"award-info":[{"award-number":["Guike AA22068070"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s13042-024-02209-0","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T15:02:32Z","timestamp":1716822152000},"page":"5053-5070","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CausalFD: causal invariance-based fraud detection against camouflaged preference"],"prefix":"10.1007","volume":"15","author":[{"given":"Yudan","family":"Song","sequence":"first","affiliation":[]},{"given":"Yuecen","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Haonan","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Qingyun","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Xingcheng","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Li-e","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xianxian","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,27]]},"reference":[{"key":"2209_CR1","unstructured":"Velampalli S, Eberle W (2017) Novel graph based anomaly detection using background knowledge. In: FLAIRS, pp 538\u2013543"},{"issue":"1","key":"2209_CR2","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1109\/TPAMI.2022.3144993","volume":"45","author":"H Peng","year":"2023","unstructured":"Peng H, Zhang R, Li S, Cao Y, Pan S, Yu PS (2023) Reinforced, incremental and cross-lingual event detection from social messages. IEEE Trans Pattern Anal Mach Intell 45(1):980\u2013998","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2209_CR3","doi-asserted-by":"crossref","unstructured":"Liang X, Yang Z, Wang B, Hu S, Yang Z, Yuan D, Gong NZ, Li Q, He F (2021) Unveiling fake accounts at the time of registration: an unsupervised approach. In: KDD, pp 3240\u20133250","DOI":"10.1145\/3447548.3467094"},{"key":"2209_CR4","doi-asserted-by":"crossref","unstructured":"Lin H, Yi P, Ma J, Jiang H, Luo Z, Shi S, Liu R (2023) Zero-shot rumor detection with propagation structure via prompt learning. In: AAAI, pp 5213\u20135221","DOI":"10.1609\/aaai.v37i4.25651"},{"key":"2209_CR5","doi-asserted-by":"crossref","unstructured":"Zhang K, Yu J, Shi H, Liang J, Zhang X (2023) Rumor detection with diverse counterfactual evidence. In: KDD, pp 3321\u20133331","DOI":"10.1145\/3580305.3599494"},{"issue":"5","key":"2209_CR6","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1145\/3447585","volume":"15","author":"H Peng","year":"2021","unstructured":"Peng H, Li J, Song Y, Yang R, Ranjan R, Yu PS, He L (2021) Streaming social event detection and evolution discovery in heterogeneous information networks. ACM Trans Knowl Discov Data 15(5):89\u201318933","journal-title":"ACM Trans Knowl Discov Data"},{"key":"2209_CR7","doi-asserted-by":"crossref","unstructured":"Yang Y, Yang R, Peng H, Li Y, Li T, Liao Y, Zhou P (2023) Fedack: federated adversarial contrastive knowledge distillation for cross-lingual and cross-model social bot detection. In: WWW, pp 1314\u20131323","DOI":"10.1145\/3543507.3583500"},{"issue":"6","key":"2209_CR8","doi-asserted-by":"publisher","first-page":"3120","DOI":"10.1109\/TCSS.2022.3207479","volume":"10","author":"Q Meng","year":"2023","unstructured":"Meng Q, Liu B, Sun X, Yan H, Liang C, Cao J, Lee RK, Bao X (2023) Attention-fused deep relevancy matching network for clickbait detection. IEEE Trans Comput Soc Syst 10(6):3120\u20133131","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"2209_CR9","doi-asserted-by":"crossref","unstructured":"Peng H, Zhang J, Huang X, Hao Z, Li A, Yu Z, Yu PS (2024) Unsupervised social bot detection via structural information theory. arXiv:2404.13595","DOI":"10.1145\/3660522"},{"issue":"2","key":"2209_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11280-024-01243-w","volume":"27","author":"X Hao","year":"2024","unstructured":"Hao X, Liu B, Yang X, Sun X, Meng Q, Cao J (2024) Multi-stage dynamic disinformation detection with graph entropy guidance. World Wide Web 27(2):1\u201321","journal-title":"World Wide Web"},{"key":"2209_CR11","doi-asserted-by":"crossref","unstructured":"Zhu Y, Liu H, Du Y, Wu Z (2021) Ifspard: an information fusion-based framework for spam review detection. In: WWW, pp 507\u2013517","DOI":"10.1145\/3442381.3449920"},{"key":"2209_CR12","doi-asserted-by":"crossref","unstructured":"Li A, Qin Z, Liu R, Yang Y, Li D (2019) Spam review detection with graph convolutional networks. In: CIKM, pp 2703\u20132711","DOI":"10.1145\/3357384.3357820"},{"key":"2209_CR13","doi-asserted-by":"crossref","unstructured":"Dhawan S, Gangireddy SCR, Kumar S, Chakraborty T (2019) Spotting collective behaviour of online frauds in customer reviews. In: IJCAI, pp 245\u2013251","DOI":"10.24963\/ijcai.2019\/35"},{"key":"2209_CR14","doi-asserted-by":"crossref","unstructured":"Kaghazgaran P, Caverlee J, Squicciarini AC (2018) Combating crowdsourced review manipulators: a neighborhood-based approach. In: WSDM, pp 306\u2013314","DOI":"10.1145\/3159652.3159726"},{"key":"2209_CR15","doi-asserted-by":"crossref","unstructured":"Rayana S, Akoglu L (2015) Collective opinion spam detection: bridging review networks and metadata. In: KDD, pp 985\u2013994","DOI":"10.1145\/2783258.2783370"},{"key":"2209_CR16","doi-asserted-by":"crossref","unstructured":"Zhang Y, Fan Y, Ye Y, Zhao L, Shi C (2019) Key player identification in underground forums over attributed heterogeneous information network embedding framework. In: CIKM, pp 549\u2013558","DOI":"10.1145\/3357384.3357876"},{"issue":"11","key":"2209_CR17","doi-asserted-by":"publisher","first-page":"11004","DOI":"10.1109\/TKDE.2023.3235944","volume":"35","author":"J Li","year":"2023","unstructured":"Li J, Fu X, Zhu S, Peng H, Wang S, Sun Q, Yu PS, He L (2023) A robust and generalized framework for adversarial graph embedding. IEEE Trans Knowl Data Eng 35(11):11004\u201311018","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2209_CR18","doi-asserted-by":"crossref","unstructured":"Yang S, Zhang Z, Zhou J, Wang Y, Sun W, Zhong X, Fang Y, Yu Q, Qi Y (2020) Financial risk analysis for smes with graph-based supply chain mining. In: IJCAI, pp 4661\u20134667","DOI":"10.24963\/ijcai.2020\/643"},{"issue":"12","key":"2209_CR19","doi-asserted-by":"publisher","first-page":"2082","DOI":"10.14778\/3352063.3352126","volume":"12","author":"S Cao","year":"2019","unstructured":"Cao S, Yang X, Chen C, Zhou J, Li X, Qi Y (2019) Titant: online real-time transaction fraud detection in ant financial. Proc VLDB Endow 12(12):2082\u20132093","journal-title":"Proc VLDB Endow"},{"key":"2209_CR20","doi-asserted-by":"crossref","unstructured":"Wang D, Qi Y, Lin J, Cui P, Jia Q, Wang Z, Fang Y, Yu Q, Zhou J, Yang S (2019) A semi-supervised graph attentive network for financial fraud detection. In: ICDM, pp 598\u2013607","DOI":"10.1109\/ICDM.2019.00070"},{"key":"2209_CR21","doi-asserted-by":"crossref","unstructured":"Xu B, Shen H, Sun B, An R, Cao Q, Cheng X (2021) Towards consumer loan fraud detection: Graph neural networks with role-constrained conditional random field. In: AAAI, pp 4537\u20134545","DOI":"10.1609\/aaai.v35i5.16582"},{"key":"2209_CR22","doi-asserted-by":"crossref","unstructured":"Liu Y, Ao X, Zhong Q, Feng J, Tang J, He Q (2020) Alike and unlike: resolving class imbalance problem in financial credit risk assessment. In: CIKM, pp 2125\u20132128","DOI":"10.1145\/3340531.3412111"},{"key":"2209_CR23","doi-asserted-by":"crossref","unstructured":"Fu X, Wei Y, Sun Q, Yuan H, Wu J, Peng H, Li J (2023) Hyperbolic geometric graph representation learning for hierarchy-imbalance node classification. In: WWW, pp 460\u2013468","DOI":"10.1145\/3543507.3583403"},{"key":"2209_CR24","doi-asserted-by":"crossref","unstructured":"Sun Q, Li J, Peng H, Wu J, Fu X, Ji C, Yu PS (2022) Graph structure learning with variational information bottleneck. In: AAAI, pp 4165\u20134174","DOI":"10.1609\/aaai.v36i4.20335"},{"key":"2209_CR25","doi-asserted-by":"crossref","unstructured":"Wei Y, Fu X, Sun Q, Peng H, Wu J, Wang J, Li X (2022) Heterogeneous graph neural network for privacy-preserving recommendation. In: ICDM, pp 528\u2013537","DOI":"10.1109\/ICDM54844.2022.00063"},{"key":"2209_CR26","doi-asserted-by":"crossref","unstructured":"Bian T, Xiao X, Xu T, Zhao P, Huang W, Rong Y, Huang J (2020) Rumor detection on social media with bi-directional graph convolutional networks. In: AAAI, pp 549\u2013556","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"2209_CR27","doi-asserted-by":"crossref","unstructured":"Kurshan E, Shen H, Yu H (2021) Financial crime & fraud detection using graph computing: Application considerations & outlook. arXiv:2103.01854 [CoRR abs]","DOI":"10.1109\/TransAI49837.2020.00029"},{"key":"2209_CR28","doi-asserted-by":"crossref","unstructured":"Nan Q, Cao J, Zhu Y, Wang Y, Li J (2021) MDFEND: multi-domain fake news detection. In: CIKM, pp 3343\u20133347","DOI":"10.1145\/3459637.3482139"},{"key":"2209_CR29","doi-asserted-by":"crossref","unstructured":"Wen R, Wang J, Wu C, Xiong J (2020) ASA: adversary situation awareness via heterogeneous graph convolutional networks. In: WWW (Companion Volume), pp 674\u2013678","DOI":"10.1145\/3366424.3391266"},{"issue":"4","key":"2209_CR30","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1145\/3490181","volume":"40","author":"H Peng","year":"2022","unstructured":"Peng H, Zhang R, Dou Y, Yang R, Zhang J, Yu PS (2022) Reinforced neighborhood selection guided multi-relational graph neural networks. ACM Trans Inf Syst 40(4):69\u201316946","journal-title":"ACM Trans Inf Syst"},{"key":"2209_CR31","doi-asserted-by":"crossref","unstructured":"Dou Y, Liu Z, Sun L, Deng Y, Peng H, Yu PS (2020) Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In: CIKM, pp 315\u2013324","DOI":"10.1145\/3340531.3411903"},{"key":"2209_CR32","doi-asserted-by":"crossref","unstructured":"Liu Z, Dou Y, Yu PS, Deng Y, Peng H (2020) Alleviating the inconsistency problem of applying graph neural network to fraud detection. In: SIGIR, pp 1569\u20131572","DOI":"10.1145\/3397271.3401253"},{"key":"2209_CR33","doi-asserted-by":"crossref","unstructured":"Liu Y, Ao X, Qin Z, Chi J, Feng J, Yang H, He Q (2021) Pick and choose: a gnn-based imbalanced learning approach for fraud detection. In: WWW, pp 3168\u20133177","DOI":"10.1145\/3442381.3449989"},{"key":"2209_CR34","doi-asserted-by":"crossref","unstructured":"Shi F, Cao Y, Shang Y, Zhou Y, Zhou C, Wu J (2022) H2-fdetector: a gnn-based fraud detector with homophilic and heterophilic connections. In: WWW, pp 1486\u20131494","DOI":"10.1145\/3485447.3512195"},{"key":"2209_CR35","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhang J, Huang Z, Li W, Feng S, Ma Z, Sun Y, Yu D, Dong F, Jin J, Wang B, Luo J (2023) Label information enhanced fraud detection against low homophily in graphs. In: WWW, pp 406\u2013416","DOI":"10.1145\/3543507.3583373"},{"key":"2209_CR36","doi-asserted-by":"crossref","unstructured":"Zhang G, Wu J, Yang J, Beheshti A, Xue S, Zhou C, Sheng QZ (2021) FRAUDRE: fraud detection dual-resistant to graph inconsistency and imbalance. In: ICDM, pp 867\u2013876","DOI":"10.1109\/ICDM51629.2021.00098"},{"key":"2209_CR37","doi-asserted-by":"crossref","unstructured":"Li Z, Chen D, Liu Q, Wu S (2022) The devil is in the conflict: disentangled information graph neural networks for fraud detection. In: ICDM, pp 1059\u20131064","DOI":"10.1109\/ICDM54844.2022.00131"},{"key":"2209_CR38","doi-asserted-by":"crossref","unstructured":"Wei L, Hu D, Zhou W, Yue Z, Hu S (2021) Towards propagation uncertainty: edge-enhanced bayesian graph convolutional networks for rumor detection. In: ACL\/IJCNLP (1), pp 3845\u20133854","DOI":"10.18653\/v1\/2021.acl-long.297"},{"key":"2209_CR39","doi-asserted-by":"crossref","unstructured":"Yang X, Lyu Y, Tian T, Liu Y, Liu Y, Zhang X (2020) Rumor detection on social media with graph structured adversarial learning. In: IJCAI, pp 1417\u20131423","DOI":"10.24963\/ijcai.2020\/197"},{"key":"2209_CR40","doi-asserted-by":"crossref","unstructured":"Khoo LMS, Chieu HL, Qian Z, Jiang J (2020) Interpretable rumor detection in microblogs by attending to user interactions. In: AAAI, pp 8783\u20138790","DOI":"10.1609\/aaai.v34i05.6405"},{"issue":"6","key":"2209_CR41","doi-asserted-by":"publisher","first-page":"2530","DOI":"10.1109\/TNNLS.2021.3114027","volume":"33","author":"C Li","year":"2022","unstructured":"Li C, Peng H, Li J, Sun L, Lyu L, Wang L, Yu PS, He L (2022) Joint stance and rumor detection in hierarchical heterogeneous graph. IEEE Trans Neural Netw Learn Syst 33(6):2530\u20132542","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2209_CR42","doi-asserted-by":"crossref","unstructured":"Sheng Q, Zhang X, Cao J, Zhong L (2021) Integrating pattern- and fact-based fake news detection via model preference learning. In: CIKM, pp 1640\u20131650","DOI":"10.1145\/3459637.3482440"},{"key":"2209_CR43","doi-asserted-by":"crossref","unstructured":"Wu J, Hooi B (2023) DECOR: degree-corrected social graph refinement for fake news detection. In: KDD, pp 2582\u20132593","DOI":"10.1145\/3580305.3599298"},{"key":"2209_CR44","doi-asserted-by":"crossref","unstructured":"Wang H, Dou Y, Chen C, Sun L, Yu PS, Shu K (2023) Attacking fake news detectors via manipulating news social engagement. In: WWW, pp 3978\u20133986","DOI":"10.1145\/3543507.3583868"},{"key":"2209_CR45","doi-asserted-by":"crossref","unstructured":"Su X, Yang J, Wu J, Zhang Y (2023) Mining user-aware multi-relations for fake news detection in large scale online social networks. In: WSDM, pp 51\u201359","DOI":"10.1145\/3539597.3570478"},{"key":"2209_CR46","doi-asserted-by":"crossref","unstructured":"Yang R, Wang X, Jin Y, Li C, Lian J, Xie X (2022) Reinforcement subgraph reasoning for fake news detection. In: KDD, pp 2253\u20132262","DOI":"10.1145\/3534678.3539277"},{"key":"2209_CR47","doi-asserted-by":"crossref","unstructured":"Jin Y, Wang X, Yang R, Sun Y, Wang W, Liao H, Xie X (2022) Towards fine-grained reasoning for fake news detection. In: AAAI, pp 5746\u20135754","DOI":"10.1609\/aaai.v36i5.20517"},{"key":"2209_CR48","doi-asserted-by":"crossref","unstructured":"Cao Y, Peng H, Yu Z, Yu PS (2023) Hierarchical and incremental structural entropy minimization for unsupervised social event detection. arXiv:2312.11891 [CoRR abs]","DOI":"10.1609\/aaai.v38i8.28666"},{"key":"2209_CR49","doi-asserted-by":"crossref","unstructured":"Ren J, Peng H, Jiang L, Liu Z, Wu J, Yu Z, Yu PS (2023) Uncertainty-guided boundary learning for imbalanced social event detection. arXiv:2310.19247 [CoRR abs]","DOI":"10.1109\/TKDE.2023.3324510"},{"issue":"11","key":"2209_CR50","doi-asserted-by":"publisher","first-page":"11860","DOI":"10.1109\/TKDE.2023.3235312","volume":"35","author":"X Sun","year":"2023","unstructured":"Sun X, Cheng H, Liu B, Li J, Chen H, Xu G, Yin H (2023) Self-supervised hypergraph representation learning for sociological analysis. IEEE Trans Knowl Data Eng 35(11):11860\u201311871","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"9","key":"2209_CR51","doi-asserted-by":"publisher","first-page":"9128","DOI":"10.1109\/TKDE.2022.3221438","volume":"35","author":"X Sun","year":"2023","unstructured":"Sun X, Yin H, Liu B, Meng Q, Cao J, Zhou A, Chen H (2023) Structure learning via meta-hyperedge for dynamic rumor detection. IEEE Trans Knowl Data Eng 35(9):9128\u20139139","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2209_CR52","doi-asserted-by":"crossref","unstructured":"Sun X, Yin H, Liu B, Chen H, Cao J, Shao Y, Hung NQV (2021) Heterogeneous hypergraph embedding for graph classification. In: WSDM, pp 725\u2013733","DOI":"10.1145\/3437963.3441835"},{"issue":"5","key":"2209_CR53","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1145\/3444944","volume":"15","author":"L Yao","year":"2021","unstructured":"Yao L, Chu Z, Li S, Li Y, Gao J, Zhang A (2021) A survey on causal inference. ACM Trans Knowl Discov Data 15(5):74\u201317446","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"1","key":"2209_CR54","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1145\/3533725","volume":"17","author":"Q Li","year":"2023","unstructured":"Li Q, Wang X, Wang Z, Xu G (2023) Be causal: de-biasing social network confounding in recommendation. ACM Trans Knowl Discov Data 17(1):14\u201311423","journal-title":"ACM Trans Knowl Discov Data"},{"key":"2209_CR55","unstructured":"Yuan H, Sun Q, Fu X, Zhang Z, Ji C, Peng H, Li J (2023) Environment-aware dynamic graph learning for out-of-distribution generalization. In: NeurIPS"},{"key":"2209_CR56","doi-asserted-by":"crossref","unstructured":"Lin X, Wu Z, Chen G, Li G, Yu Y (2022) A causal debiasing framework for unsupervised salient object detection. In: AAAI, vol 36, pp 1610\u20131619","DOI":"10.1609\/aaai.v36i2.20052"},{"key":"2209_CR57","doi-asserted-by":"crossref","unstructured":"Wang T, Huang J, Zhang H, Sun Q (2020) Visual commonsense R-CNN. In: CVPR, pp 10757\u201310767","DOI":"10.1109\/CVPR42600.2020.01077"},{"key":"2209_CR58","doi-asserted-by":"crossref","unstructured":"Wang L, Adiga A, Chen J, Sadilek A, Venkatramanan S, Marathe MV (2022) Causalgnn: causal-based graph neural networks for spatio-temporal epidemic forecasting. In: AAAI, pp 12191\u201312199","DOI":"10.1609\/aaai.v36i11.21479"},{"issue":"7","key":"2209_CR59","doi-asserted-by":"publisher","first-page":"7328","DOI":"10.1109\/TKDE.2022.3193725","volume":"35","author":"H Li","year":"2023","unstructured":"Li H, Wang X, Zhang Z, Zhu W (2023) OOD-GNN: out-of-distribution generalized graph neural network. IEEE Trans Knowl Data Eng 35(7):7328\u20137340","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2209_CR60","unstructured":"Fan S, Wang X, Shi C, Kuang K, Liu N, Wang B (2022) Debiased graph neural networks with agnostic label selection bias. arXiv:2201.07708 [CoRR abs]"},{"key":"2209_CR61","doi-asserted-by":"crossref","unstructured":"Sui Y, Wang X, Wu J, Lin M, He X, Chua T (2022) Causal attention for interpretable and generalizable graph classification. In: KDD, pp 1696\u20131705","DOI":"10.1145\/3534678.3539366"},{"key":"2209_CR62","doi-asserted-by":"crossref","unstructured":"Mu S, Li Y, Zhao WX, Wang J, Ding B, Wen J (2022) Alleviating spurious correlations in knowledge-aware recommendations through counterfactual generator. In: SIGIR, pp 1401\u20131411","DOI":"10.1145\/3477495.3531934"},{"key":"2209_CR63","doi-asserted-by":"crossref","unstructured":"Li Y, Sun X, Chen H, Zhang S, Yang Y, Xu G (2024) Attention is not the only choice: counterfactual reasoning for path-based explainable recommendation. arXiv:2401.05744 [CoRR abs]","DOI":"10.1109\/TKDE.2024.3373608"},{"key":"2209_CR64","doi-asserted-by":"crossref","unstructured":"Sun X, Cheng H, Dong H, Qiao B, Qin S, Lin Q (2023) Counter-empirical attacking based on adversarial reinforcement learning for time-relevant scoring system. arXiv:2311.05144 [CoRR abs]","DOI":"10.1109\/TKDE.2023.3341430"},{"key":"2209_CR65","unstructured":"Zhu J, Yan Y, Zhao L, Heimann M, Akoglu L, Koutra D (2020) Beyond homophily in graph neural networks: current limitations and effective designs. In: NeurIPS"},{"key":"2209_CR66","unstructured":"Tolstikhin IO, Houlsby N, Kolesnikov A, Beyer L, Zhai X, Unterthiner T, Yung J, Steiner A, Keysers D, Uszkoreit J, Lucic M, Dosovitskiy A (2021) Mlp-mixer: an all-mlp architecture for vision. In: NeurIPS, pp 24261\u201324272"},{"key":"2209_CR67","doi-asserted-by":"crossref","unstructured":"He Y, Cui P, Shen Z, Xu R, Liu F, Jiang Y (2021) DARING: differentiable causal discovery with residual independence. In: KDD, pp 596\u2013605","DOI":"10.1145\/3447548.3467439"},{"key":"2209_CR68","unstructured":"Zheng X, Aragam B, Ravikumar P, Xing EP (2018) Dags with NO TEARS: continuous optimization for structure learning. In: NeurIPS, pp 9492\u20139503"},{"key":"2209_CR69","doi-asserted-by":"crossref","unstructured":"He Y, Wang Z, Cui P, Zou H, Zhang Y, Cui Q, Jiang Y (2022) Causpref: causal preference learning for out-of-distribution recommendation. In: WWW, pp 410\u2013421","DOI":"10.1145\/3485447.3511969"},{"key":"2209_CR70","doi-asserted-by":"crossref","unstructured":"McAuley JJ, Leskovec J (2013) From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In: WWW, pp 897\u2013908","DOI":"10.1145\/2488388.2488466"},{"key":"2209_CR71","doi-asserted-by":"crossref","unstructured":"Rayana S, Akoglu L (2015) Collective opinion spam detection: bridging review networks and metadata. In: KDD, pp 985\u2013994","DOI":"10.1145\/2783258.2783370"},{"key":"2209_CR72","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: KDD, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"2209_CR73","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks"},{"key":"2209_CR74","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2017) Graph attention networks. arXiv:1710.10903 [CoRR abs]"},{"key":"2209_CR75","unstructured":"Hamilton WL, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: NIPS, pp 1024\u20131034"},{"key":"2209_CR76","doi-asserted-by":"crossref","unstructured":"Chinchor N (1992) MUC-4 evaluation metrics. In: MUC, pp 22\u201329","DOI":"10.3115\/1072064.1072067"},{"key":"2209_CR77","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11)"},{"issue":"1","key":"2209_CR78","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/s00180-022-01230-7","volume":"38","author":"M Sundqvist","year":"2023","unstructured":"Sundqvist M, Chiquet J, Rigaill G (2023) Adjusting the adjusted rand index: a multinomial story. Comput Stat 38(1):327\u2013347","journal-title":"Comput Stat"},{"key":"2209_CR79","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) LINE: large-scale information network embedding. In: WWW, pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02209-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02209-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02209-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T05:21:39Z","timestamp":1728451299000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02209-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,27]]},"references-count":79,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["2209"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02209-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,5,27]]},"assertion":[{"value":"9 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}