Spatio-Temporal Graph Representation Learning for Fraudster Group Detection

• URL: http://arxiv.org/abs/2201.02621v1
• Date: Fri, 7 Jan 2022 08:01:38 GMT
• Title: Spatio-Temporal Graph Representation Learning for Fraudster Group Detection
• Authors: Saeedreza Shehnepoor, Roberto Togneri, Wei Liu, Mohammed Bennamoun
• Abstract summary: Companies may hire fraudster groups to write fake reviews to either demote competitors or promote their own businesses. To detect such groups, a common model is to represent fraudster groups' static networks. We propose to first capitalize on the effectiveness of the HIN-RNN in both reviewers' representation learning.
• Score: 50.779498955162644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivated by potential financial gain, companies may hire fraudster groups to write fake reviews to either demote competitors or promote their own businesses. Such groups are considerably more successful in misleading customers, as people are more likely to be influenced by the opinion of a large group. To detect such groups, a common model is to represent fraudster groups' static networks, consequently overlooking the longitudinal behavior of a reviewer thus the dynamics of co-review relations among reviewers in a group. Hence, these approaches are incapable of excluding outlier reviewers, which are fraudsters intentionally camouflaging themselves in a group and genuine reviewers happen to co-review in fraudster groups. To address this issue, in this work, we propose to first capitalize on the effectiveness of the HIN-RNN in both reviewers' representation learning while capturing the collaboration between reviewers, we first utilize the HIN-RNN to model the co-review relations of reviewers in a group in a fixed time window of 28 days. We refer to this as spatial relation learning representation to signify the generalisability of this work to other networked scenarios. Then we use an RNN on the spatial relations to predict the spatio-temporal relations of reviewers in the group. In the third step, a Graph Convolution Network (GCN) refines the reviewers' vector representations using these predicted relations. These refined representations are then used to remove outlier reviewers. The average of the remaining reviewers' representation is then fed to a simple fully connected layer to predict if the group is a fraudster group or not. Exhaustive experiments of the proposed approach showed a 5% (4%), 12% (5%), 12% (5%) improvement over three of the most recent approaches on precision, recall, and F1-value over the Yelp (Amazon) dataset, respectively.

Related papers

• VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection [26.35056300387743]
  Existing fraud detection methods rely on graph-based or sequence-based approaches. VecAug is a novel cohort-augmented learning framework. We deploy our framework on e-commerce platforms and evaluate it on three fraud detection datasets.
  arXiv  Detail & Related papers  (2024-08-01T12:39:27Z)
• Cluster-guided Contrastive Graph Clustering Network [53.16233290797777]
  We propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) We construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. To construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples.
  arXiv  Detail & Related papers  (2023-01-03T13:42:38Z)
• AGRO: Adversarial Discovery of Error-prone groups for Robust Optimization [109.91265884632239]
  Group distributionally robust optimization (G-DRO) can minimize the worst-case loss over a set of pre-defined groups over training data. We propose AGRO -- Adversarial Group discovery for Distributionally Robust Optimization. AGRO results in 8% higher model performance on average on known worst-groups, compared to prior group discovery approaches.
  arXiv  Detail & Related papers  (2022-12-02T00:57:03Z)
• Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detection [13.26226951002133]
  This paper addresses the challenges of defining, approximating, and utilizing a new subgroup structure for fair spam detection. We first identify subgroup structures in the review graph that lead to discrepant accuracy in the groups. Comprehensive comparisons against baselines on three large Yelp review datasets demonstrate that the subgroup membership can be identified and exploited for group fairness.
  arXiv  Detail & Related papers  (2022-04-24T02:19:22Z)
• Group-disentangled Representation Learning with Weakly-Supervised Regularization [13.311886256230814]
  GroupVAE is a simple yet effective Kullback-Leibler divergence-based regularization to enforce consistent and disentangled representations. We demonstrate that learning group-disentangled representations improve upon downstream tasks, including fair classification and 3D shape-related tasks such as reconstruction, classification, and transfer learning.
  arXiv  Detail & Related papers  (2021-10-23T10:01:05Z)
• Unsupervised Learning of Debiased Representations with Pseudo-Attributes [85.5691102676175]
  We propose a simple but effective debiasing technique in an unsupervised manner. We perform clustering on the feature embedding space and identify pseudoattributes by taking advantage of the clustering results. We then employ a novel cluster-based reweighting scheme for learning debiased representation.
  arXiv  Detail & Related papers  (2021-08-06T05:20:46Z)
• HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features [42.30892608083864]
  We propose the first neural approach, HIN-RNN, a Heterogeneous Information Network (HIN) compatible RNN for fraudster group detection. HIN-RNN provides a unifying architecture for learning text representation of each reviewer, with the initial vector as the sum of word embeddings of all review written by the same reviewer. The proposed approach is confirmed to be effective with marked improvement over state-of-the-art approaches on both the Yelp (22% and 12% in terms of recall and F1-value, respectively) and Amazon (4% and 2% in terms of recall and F1-value, respectively
  arXiv  Detail & Related papers  (2021-05-25T01:48:28Z)
• ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation [50.779498955162644]
  We propose ScoreGAN for fraud review detection that makes use of both review text and review rating scores in the generation and detection process. Results show that the proposed framework outperformed the existing state-of-the-art framework, namely FakeGAN, in terms of AP by 7%, and 5% on the Yelp and TripAdvisor datasets.
  arXiv  Detail & Related papers  (2020-06-11T16:15:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.