16 research outputs found
How Fraudster Detection Contributes to Robust Recommendation
The adversarial robustness of recommendation systems under node injection
attacks has received considerable research attention. Recently, a robust
recommendation system GraphRfi was proposed, and it was shown that GraphRfi
could successfully mitigate the effects of injected fake users in the system.
Unfortunately, we demonstrate that GraphRfi is still vulnerable to attacks due
to the supervised nature of its fraudster detection component. Specifically, we
propose a new attack metaC against GraphRfi, and further analyze why GraphRfi
fails under such an attack. Based on the insights we obtained from the
vulnerability analysis, we build a new robust recommendation system PDR by
re-designing the fraudster detection component. Comprehensive experiments show
that our defense approach outperforms other benchmark methods under attacks.
Overall, our research demonstrates an effective framework of integrating
fraudster detection into recommendation to achieve adversarial robustness
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Graph Neural Networks (GNNs) have been widely applied to fraud detection
problems in recent years, revealing the suspiciousness of nodes by aggregating
their neighborhood information via different relations. However, few prior
works have noticed the camouflage behavior of fraudsters, which could hamper
the performance of GNN-based fraud detectors during the aggregation process. In
this paper, we introduce two types of camouflages based on recent empirical
studies, i.e., the feature camouflage and the relation camouflage. Existing
GNNs have not addressed these two camouflages, which results in their poor
performance in fraud detection problems. Alternatively, we propose a new model
named CAmouflage-REsistant GNN (CARE-GNN), to enhance the GNN aggregation
process with three unique modules against camouflages. Concretely, we first
devise a label-aware similarity measure to find informative neighboring nodes.
Then, we leverage reinforcement learning (RL) to find the optimal amounts of
neighbors to be selected. Finally, the selected neighbors across different
relations are aggregated together. Comprehensive experiments on two real-world
fraud datasets demonstrate the effectiveness of the RL algorithm. The proposed
CARE-GNN also outperforms state-of-the-art GNNs and GNN-based fraud detectors.
We integrate all GNN-based fraud detectors as an opensource toolbox:
https://github.com/safe-graph/DGFraud. The CARE-GNN code and datasets are
available at https://github.com/YingtongDou/CARE-GNN.Comment: Accepted by CIKM 202
Robust Recommender System: A Survey and Future Directions
With the rapid growth of information, recommender systems have become
integral for providing personalized suggestions and overcoming information
overload. However, their practical deployment often encounters "dirty" data,
where noise or malicious information can lead to abnormal recommendations.
Research on improving recommender systems' robustness against such dirty data
has thus gained significant attention. This survey provides a comprehensive
review of recent work on recommender systems' robustness. We first present a
taxonomy to organize current techniques for withstanding malicious attacks and
natural noise. We then explore state-of-the-art methods in each category,
including fraudster detection, adversarial training, certifiable robust
training against malicious attacks, and regularization, purification,
self-supervised learning against natural noise. Additionally, we summarize
evaluation metrics and common datasets used to assess robustness. We discuss
robustness across varying recommendation scenarios and its interplay with other
properties like accuracy, interpretability, privacy, and fairness. Finally, we
delve into open issues and future research directions in this emerging field.
Our goal is to equip readers with a holistic understanding of robust
recommender systems and spotlight pathways for future research and development
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges
Anomaly analytics is a popular and vital task in various research contexts,
which has been studied for several decades. At the same time, deep learning has
shown its capacity in solving many graph-based tasks like, node classification,
link prediction, and graph classification. Recently, many studies are extending
graph learning models for solving anomaly analytics problems, resulting in
beneficial advances in graph-based anomaly analytics techniques. In this
survey, we provide a comprehensive overview of graph learning methods for
anomaly analytics tasks. We classify them into four categories based on their
model architectures, namely graph convolutional network (GCN), graph attention
network (GAT), graph autoencoder (GAE), and other graph learning models. The
differences between these methods are also compared in a systematic manner.
Furthermore, we outline several graph-based anomaly analytics applications
across various domains in the real world. Finally, we discuss five potential
future research directions in this rapidly growing field
Heterogeneous Graph Neural Networks for Fraud Detection and Explanation in Supply Chain Finance
It is a critical mission for financial service providers to discover fraudulent borrowers in a supply chain. The borrowers’ transactions in anongoing business are inspected to support the providers’ decision on whether to lend the money. Considering multiple participants in a supply chain business, the borrowers may use sophisticated tricks to cheat, making fraud detection challenging. In this work, we propose a multitask learning framework, MultiFraud, for complex fraud detection with reasonable explanation. The heterogeneous information from multi-view around the entities is leveraged in the detection framework based on heterogeneous graph neural networks. MultiFraud enables multiple domains to share embeddings and enhance modeling capabilities for fraud detection. The developed explainer provides comprehensive explanations across multiple graphs. Experimental results on five datasets demonstrate the framework’s effectiveness in fraud detection and explanation across domains
Graph learning for anomaly analytics : algorithms, applications, and challenges
Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field. © 2023 Association for Computing Machinery
Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection
Customer lifetime value (LTV) prediction is essential for mobile game
publishers trying to optimize the advertising investment for each user
acquisition based on the estimated worth. In mobile games, deploying
microtransactions is a simple yet effective monetization strategy, which
attracts a tiny group of game whales who splurge on in-game purchases. The
presence of such game whales may impede the practicality of existing LTV
prediction models, since game whales' purchase behaviours always exhibit varied
distribution from general users. Consequently, identifying game whales can open
up new opportunities to improve the accuracy of LTV prediction models. However,
little attention has been paid to applying game whale detection in LTV
prediction, and existing works are mainly specialized for the long-term LTV
prediction with the assumption that the high-quality user features are
available, which is not applicable in the UA stage. In this paper, we propose
ExpLTV, a novel multi-task framework to perform LTV prediction and game whale
detection in a unified way. In ExpLTV, we first innovatively design a deep
neural network-based game whale detector that can not only infer the intrinsic
order in accordance with monetary value, but also precisely identify high
spenders (i.e., game whales) and low spenders. Then, by treating the game whale
detector as a gating network to decide the different mixture patterns of LTV
experts assembling, we can thoroughly leverage the shared information and
scenario-specific information (i.e., game whales modelling and low spenders
modelling). Finally, instead of separately designing a purchase rate estimator
for two tasks, we design a shared estimator that can preserve the inner task
relationships. The superiority of ExpLTV is further validated via extensive
experiments on three industrial datasets