TY - JOUR
T1 - MINT
T2 - 49th International Conference on Very Large Data Bases, VLDB 2023
AU - Xiao, Fei
AU - Wu, Yuncheng
AU - Zhang, Meihui
AU - Chen, Gang
AU - Ooi, Beng Chin
N1 - Publisher Copyright:
© 2023, VLDB Endowment. All rights reserved.
PY - 2023
Y1 - 2023
N2 - The e-commerce platforms, such as Shopee, have accumulated a huge volume of time-series relational data, which contains useful information on differentiating fraud users from benign users. Existing fraud behavior detection approaches typically model the time-series data with a vanilla Recurrent Neural Network (RNN) or combine the whole sequence as a single intention without considering the temporal behavioral patterns, row-level interactions, and different view intentions. In this paper, we present MINT, a Multiview row-INteractive Time-aware framework to detect fraudulent behaviors from time-series structured data. The key idea of MINT is to build a time-aware behavior graph for each user’s time-series relational data with each row represented as an action node. We utilize the user’s temporal information to construct three different graph convolutional matrices for hierarchically learning the user’s intentions from different views, that is, short-term, medium-term, and long-term intentions. To capture more meaningful row-level interactions and alleviate the over-smoothing issue in a vanilla time-aware behavior graph, we propose a novel gated neighbor interaction mechanism to calibrate the aggregated information by each action node. Since the receptive fields of the three graph convolutional layers are designed to grow nearly exponentially, our MINT requires many fewer layers than traditional deep graph neural networks (GNNs) to capture multi-hop neighboring information, and avoids recurrent feedforward propagation, thus leading to higher training efficiency and scalability. Our extensive experiments on the large-scale e-commerce datasets from Shopee with up to 4.6 billion records and a public dataset from Amazon show that MINT achieves superior performance over 10 state-of-the-art models and provides better interpretability and scalability.
AB - The e-commerce platforms, such as Shopee, have accumulated a huge volume of time-series relational data, which contains useful information on differentiating fraud users from benign users. Existing fraud behavior detection approaches typically model the time-series data with a vanilla Recurrent Neural Network (RNN) or combine the whole sequence as a single intention without considering the temporal behavioral patterns, row-level interactions, and different view intentions. In this paper, we present MINT, a Multiview row-INteractive Time-aware framework to detect fraudulent behaviors from time-series structured data. The key idea of MINT is to build a time-aware behavior graph for each user’s time-series relational data with each row represented as an action node. We utilize the user’s temporal information to construct three different graph convolutional matrices for hierarchically learning the user’s intentions from different views, that is, short-term, medium-term, and long-term intentions. To capture more meaningful row-level interactions and alleviate the over-smoothing issue in a vanilla time-aware behavior graph, we propose a novel gated neighbor interaction mechanism to calibrate the aggregated information by each action node. Since the receptive fields of the three graph convolutional layers are designed to grow nearly exponentially, our MINT requires many fewer layers than traditional deep graph neural networks (GNNs) to capture multi-hop neighboring information, and avoids recurrent feedforward propagation, thus leading to higher training efficiency and scalability. Our extensive experiments on the large-scale e-commerce datasets from Shopee with up to 4.6 billion records and a public dataset from Amazon show that MINT achieves superior performance over 10 state-of-the-art models and provides better interpretability and scalability.
UR - http://www.scopus.com/inward/record.url?scp=85174515306&partnerID=8YFLogxK
U2 - 10.14778/3611540.3611551
DO - 10.14778/3611540.3611551
M3 - Conference article
AN - SCOPUS:85174515306
SN - 2150-8097
VL - 16
SP - 3710
EP - 3723
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 12
Y2 - 28 August 2023 through 1 September 2023
ER -