A survey of imbalanced learning on graphs: Problems, techniques, and future directions

Z Liu, Y Li, N Chen, Q Wang, B Hooi, B He - arXiv preprint arXiv …, 2023 - arxiv.org
Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …

BTG: A Bridge to Graph machine learning in telecommunications fraud detection

X Hu, H Chen, S Liu, H Jiang, G Chu, R Li - Future Generation Computer …, 2022 - Elsevier
Telecommunications fraud runs rampant recently around the world. Therefore, how to
effectively detect fraudsters has become an increasingly challenging problem. However …

Detecting malicious reviews and users affecting social reviewing systems: A survey

C Esposito, V Moscato, G Sperlì - Computers & Security, 2023 - Elsevier
The proliferation of attacks on On-line Social Networks (OSNs) has imposed particular
attention by providers and users. This has an even higher importance for Social Reviewing …

Uncovering insights from big data: change point detection of classroom engagement

K Nakamura, M Ishihara, I Horikoshi… - Smart Learning …, 2024 - Springer
Expectations of big data across various fields, including education, are increasing. However,
uncovering valuable insights from big data is like locating a needle in a haystack, and it is …

Dynamic graph neural network-based fraud detectors against collaborative fraudsters

L Ren, R Hu, D Li, Y Liu, J Wu, Y Zang, W Hu - Knowledge-Based Systems, 2023 - Elsevier
Telecom fraud detection is a challenging task since the fact that fraudulent behaviors are
hidden in the vast amount of telecom records. More concerning, the ongoing coronavirus …

Telecom fraud detection via hawkes-enhanced sequence model

Y Jiang, G Liu, J Wu, H Lin - IEEE Transactions on Knowledge …, 2022 - ieeexplore.ieee.org
Detecting frauds from a massive amount of user behavioral data is often regarded as finding
a needle in a haystack. While tremendous efforts have been devoted to fraud detection from …

Nowhere to H2IDE: Fraud Detection from Multi-relation Graphs via Disentangled Homophily and Heterophily Identification

C Fu, G Liu, K Yuan, J Wu - IEEE Transactions on Knowledge …, 2024 - ieeexplore.ieee.org
Fraud detection has always been one of the primary concerns in social and economic
activities and is becoming a decisive force in the booming digital economy. Graph structures …

Supervised anomaly detection via conditional generative adversarial network and ensemble active learning

Z Chen, J Duan, L Kang, G Qiu - IEEE Transactions on Pattern …, 2022 - ieeexplore.ieee.org
Anomaly detection has wide applications in machine intelligence but is still a difficult
unsolved problem. Major challenges include the rarity of labeled anomalies and it is a class …

Beyond the individual: An improved telecom fraud detection approach based on latent synergy graph learning

J Wu, R Hu, D Li, L Ren, Z Huang, Y Zang - Neural Networks, 2024 - Elsevier
The development of telecom technology not only facilitates social interactions but also
inevitably provides the breeding ground for telecom fraud crimes. However, telecom fraud …

Density-based discriminative nonnegative representation model for imbalanced classification

Y Li, S Wang, J Jin, H Tao, J Nan, H Wu… - Neural Processing …, 2024 - Springer
Abstract Representation-based methods have found widespread applications in various
classification tasks. However, these methods cannot deal effectively with imbalanced data …