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- research-article
FLOOD: A Flexible Invariant Learning Framework for Out-of-Distribution Generalization on Graphs
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2023, pp 1548-1558• https://doi.org/10.1145/3580305.3599355Graph Neural Networks (GNNs) have achieved remarkable success in various domains but most of them are developed under the in-distribution assumption. Under out-of-distribution (OOD) settings, they suffer from the distribution shift between the training ...
- short-paper
Explainable Graph-based Fraud Detection via Neural Meta-graph Search
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management•October 2022, pp 4414-4418• https://doi.org/10.1145/3511808.3557598Though graph neural networks (GNNs)-based fraud detectors have received remarkable success in identifying fraudulent activities, few of them pay equal attention to models' performance and explainability. In this paper, we attempt to achieve high ...
- research-article
Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2022, pp 925-935• https://doi.org/10.1145/3534678.3539484Benefiting from the message passing mechanism, Graph Neural Networks (GNNs) have been successful on flourish tasks over graph data. However, recent studies have shown that attackers can catastrophically degrade the performance of GNNs by maliciously ...
- research-article
UD-GNN: Uncertainty-aware Debiased Training on Semi-Homophilous Graphs
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2022, pp 1131-1140• https://doi.org/10.1145/3534678.3539483Recent studies on Graph Neural Networks (GNNs) point out that most GNNs depend on the homophily assumption but fail to generalize to graphs with heterophily where dissimilar nodes connect. The concept of homophily or heterophily defined previously is a ...
- research-article
AUC-oriented Graph Neural Network for Fraud Detection
WWW '22: Proceedings of the ACM Web Conference 2022•April 2022, pp 1311-1321• https://doi.org/10.1145/3485447.3512178Though Graph Neural Networks (GNNs) have been successful for fraud detection tasks, they suffer from imbalanced labels due to limited fraud compared to the overall userbase. This paper attempts to resolve this label-imbalance problem for GNNs by ...
- Article
Bi-Level Selection via Meta Gradient for Graph-Based Fraud Detection
Database Systems for Advanced Applications•April 2022, pp 387-394• https://doi.org/10.1007/978-3-031-00123-9_31AbstractGraph Neural Networks (GNNs) have achieved remarkable successes by utilizing rich interactions in network data. When applied to fraud detection tasks, the scarcity and concealment of fraudsters bring two challenges: class imbalance and label ...
- research-article
Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding
IEEE Transactions on Knowledge and Data Engineering, Volume 34, Issue 1•Jan. 2022, pp 462-474 • https://doi.org/10.1109/TKDE.2020.2983892With the ever-increasing urbanization process, modeling people's spatiotemporal activities from their online traces has become a crucial task. State-of-the-art methods for this task rely on cross-modal embedding, which maps items from different ...
- research-article
Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection
WWW '21: Proceedings of the Web Conference 2021•April 2021, pp 3168-3177• https://doi.org/10.1145/3442381.3449989Graph-based fraud detection approaches have escalated lots of attention recently due to the abundant relational information of graph-structured data, which may be beneficial for the detection of fraudsters. However, the GNN-based algorithms could fare ...
- short-paper
Alike and Unlike: Resolving Class Imbalance Problem in Financial Credit Risk Assessment
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management•October 2020, pp 2125-2128• https://doi.org/10.1145/3340531.3412111Financial credit risk assessment serves as the impetus to evaluate the credit admission or potential business failure of customers in order to make early actions prior to the actual financial crisis. It aims to predict the probability that a customer ...
- research-article
Financial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network
WWW '20: Proceedings of The Web Conference 2020•April 2020, pp 785-795• https://doi.org/10.1145/3366423.3380159Default user detection plays one of the backbones in credit risk forecasting and management. It aims at, given a set of corresponding features, e.g., patterns extracted from trading behaviors, predicting the polarity indicating whether a user will fail ...
- research-article
Grid-based DBSCAN: Indexing and inference
Pattern Recognition, Volume 90, Issue C•Jun 2019, pp 271-284 • https://doi.org/10.1016/j.patcog.2019.01.034Highlights- The proposed method extends grid-based DBSCAN scalable to higher-dimensional dataset.
AbstractDBSCAN is one of clustering algorithms which can report arbitrarily-shaped clusters and noises without requiring the number of clusters as a parameter (unlike the other clustering algorithms, k-means, for example). Because the running ...
- Article
Free-Rider Episode Screening via Dual Partition Model
Database Systems for Advanced Applications•May 2018, pp 665-683• https://doi.org/10.1007/978-3-319-91452-7_43AbstractOne of the drawbacks of frequent episode mining is that overwhelmingly many of the discovered patterns are redundant. Free-rider episode, as a typical example, consists of a real pattern doped with some additional noise events. Because of the ...