BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

Hooi, Bryan; Shah, Neil; Beutel, Alex; Günnemann, Stephan; Akoglu, Leman; Kumar, Mohit; Makhija, Disha; Faloutsos, Christos

doi:10.1137/1.9781611974348.56

Cited by 74 publications

(67 citation statements)

References 29 publications

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“…Similar trends were observed in other datasets. These results suggest that NetConf is ideal for precision-critical applications, e.g., fraud detection [11,13].…”

Section: Q2 E↵ectiveness: How Accurate Is Netconf ?mentioning

confidence: 99%

The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation

Eswaran

Günnemann

Faloutsos

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Given a friendship network, how certain are we that Smith is a progressive (vs. conservative)? How can we propagate these certainties through the network? While Belief propagation marked the beginning of principled label-propagation to classify nodes in a graph, its numerous variants proposed in the literature fail to take into account uncertainty during the propagation process. As we show, this limitation leads to counter-intuitive results for even simple graphs. Motivated by these observations, we formalize axioms that any node classification algorithm should obey and propose NetConf which satisfies these axioms and handles arbitrary network e↵ects (homophily/heterophily) at scale. Our contributions are: (1) Axioms: We state axioms that any node classification algorithm should satisfy; (2) Theory: NetConf is grounded in a Bayesian-theoretic framework to model uncertainties, has a closed-form solution and comes with precise convergence guarantees; (3) Practice: Our method is easy to implement and scales linearly with the number of edges in the graph. On experiments using real world data, we always match or outperform BP while taking less processing time.

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“…Similar trends were observed in other datasets. These results suggest that NetConf is ideal for precision-critical applications, e.g., fraud detection [11,13].…”

Section: Q2 E↵ectiveness: How Accurate Is Netconf ?mentioning

confidence: 99%

The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation

Eswaran

Günnemann

Faloutsos

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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“…This is critical, since especially in domains where graph-based learning is used (e.g. the Web) adversaries are omnipresent, e.g., manipulating online reviews and product websites [11]. One of the core challenges is that in a GNN a node's prediction is also affected when perturbing other nodes in the graph -making the space of possible perturbations large.…”

Section: Introductionmentioning

confidence: 99%

Certifiable Robustness and Robust Training for Graph Convolutional Networks

Zügner

Günnemann

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Recent works show that Graph Neural Networks (GNNs) are highly non-robust with respect to adversarial attacks on both the graph structure and the node attributes, making their outcomes unreliable. We propose the first method for certifiable (non-)robustness of graph convolutional networks with respect to perturbations of the node attributes 1 . We consider the case of binary node attributes (e.g. bag-of-words) and perturbations that are L 0 -bounded. If a node has been certified with our method, it is guaranteed to be robust under any possible perturbation given the attack model. Likewise, we can certify non-robustness. Finally, we propose a robust semisupervised training procedure that treats the labeled and unlabeled nodes jointly. As shown in our experimental evaluation, our method significantly improves the robustness of the GNN with only minimal effect on the predictive accuracy.

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“…Panigrahi et al [17] combined a Dempster-Schaefer adder with a Bayesian learner to solve credit card fraud with their own synthesised data. Hooi et al [18] developed BIRD, a Bayesian inference approach for ratings fraud detection. The method provides a principled way to combine rating and temporal information to detect rating fraud, and to find a trade-off between users with extreme rating distributions vs. users with larger number of ratings.…”

Section: Naive Bayesmentioning

confidence: 99%

A state of the art survey of data mining-based fraud detection and credit scoring

Zhou¹,

Cheng²,

Zhu³

et al. 2018

MATEC Web Conf.

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Abstract.Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.

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BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

Cited by 74 publications

References 29 publications

The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation

The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation

Certifiable Robustness and Robust Training for Graph Convolutional Networks

A state of the art survey of data mining-based fraud detection and credit scoring

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