A survey of imbalanced learning on graphs: Problems, techniques, and future directions
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 …
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 …
effectively detect fraudsters has become an increasingly challenging problem. However …
Detecting malicious reviews and users affecting social reviewing systems: A survey
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 …
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 …
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
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 …
hidden in the vast amount of telecom records. More concerning, the ongoing coronavirus …
Telecom fraud detection via hawkes-enhanced sequence model
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 …
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
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 …
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
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 …
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
The development of telecom technology not only facilitates social interactions but also
inevitably provides the breeding ground for telecom fraud crimes. However, telecom fraud …
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 …
classification tasks. However, these methods cannot deal effectively with imbalanced data …