Introduction to Mathematical Sociology

Path extension similarity link prediction method based on matrix algebra in directed networks

This article addresses the pervasive issue of fraud in financial transactions by introducing the Graph Attention Network (GAN) into graph neural networks. The article integrates Node Attention Networks and Semantic Attention Networks to construct a Dual-Head Attention Network module, enabling a comprehensive analysis of complex relationships in user transaction data. This approach adeptly handles non-linear features and intricate data interaction relationships. The article incorporates a Gradient-Boosting Decision Tree (GBDT) to enhance fraud identification to create the GBDT–Dual-channel Graph Attention Network (GBDT-DGAN). In a bid to ensure user privacy, this article introduces blockchain technology, culminating in the development of a financial anti-fraud model that fuses blockchain with the GBDT-DGAN algorithm. Experimental verification demonstrates the model’s accuracy, reaching 93.82%, a notable improvement of at least 5.76% compared to baseline algorithms such as Convolutional Neural Networks. The recall and F1 values stand at 89.5% and 81.66%, respectively. Additionally, the model exhibits superior network data transmission security, maintaining a packet loss rate below 7%. Consequently, the proposed model significantly outperforms traditional approaches in financial fraud detection accuracy and ensures excellent network data transmission security, offering an efficient and secure solution for fraud detection in the financial domain.

Financial Anti-Fraud Based on Dual-Channel Graph Attention Network

In recent years, the increasing prevalence of mobile social network fraud has led to significant distress and depletion of personal and social wealth, resulting in considerable economic harm. Graph neural networks (GNNs) have emerged as a popular approach to tackle this issue. However, the challenge of graph imbalance, which can greatly impede the effectiveness of GNN-based fraud detection methods, has received little attention in prior research. Thus, we are going to present a novel cost-sensitive graph neural network (CSGNN) in this article. Initially, reinforcement learning is utilized to train a suitable sampling threshold, followed by neighbor sampling based on node similarity, which helps to alleviate the graph imbalance issue preliminarily. Subsequently, message aggregation is executed on the sampled graph using GNN to obtain node embeddings. Concurrently, the optimization objective for the cost matrix is formulated using the sample histogram matrix, scatter matrix, and confusion matrix. The cost matrix and GNN are collaboratively optimized through the backpropagation algorithm. Ultimately, the derived cost-sensitive node embedding is employed for fraudulent node detection. Furthermore, this study provides a theoretical demonstration of the effectiveness of adaptive cost-sensitive learning in GNN. Extensive experiments are carried out on two publicly accessible real-world mobile network fraud datasets, revealing that the proposed CSGNN effectively addresses the graph imbalance issue while outperforming state-of-the-art algorithms in detection performance. The CSGNN code and datasets can be accessed at https://github.com/xxhu94/CSGNN. 

pdf/cost-sensitive-gnn-based-imbalanced-learning-for-mobile-l7y11f7fa4.pdf

Cost-Sensitive GNN-Based Imbalanced Learning for Mobile Social Network Fraud Detection

With the rapid development of mobile networks, the people's social contacts have been considerably facilitated. However, the rise of mobile social network fraud upon those networks, has caused a great deal of distress, in case of depleting personal and social wealth, then potentially doing significant economic harm. To detect fraudulent users, call detail record (CDR) data, which portrays the social behavior of users in mobile networks, has been widely utilized. But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph neural networks(GNN), has hardly been addressed in previous work. In this paper, we are going to present a novel Cost-Sensitive Graph Neural Network (CSGNN) by creatively combining cost-sensitive learning and graph neural networks. We conduct extensive experiments on two open-source realworld mobile network fraud datasets. The results show that CSGNN can effectively solve the graph imbalance problem and then achieve better detection performance than the state-of-the-art algorithms. We believe that our research can be applied to solve the graph imbalance problems in other fields. The CSGNN code and datasets are publicly available at https://github.com/xxhu94/CSGNN.

pdf/cost-sensitive-gnn-based-imbalanced-learning-for-mobile-36ga6vuz.pdf

Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection

Link prediction in directed networks has always been a hot topic in many fields including network science, information system and data mining. Intuitively, once links are endowed with certain orientations, their reciprocate nature can potentially provide extra information for guiding link prediction. However, the role of reciprocal links in the formation of directed closure triads and their ability to enhance link prediction accuracy are not thoroughly investigated yet in existing works. In this paper, we first design an empirical test to investigate the role of reciprocal links in different types of directed networks by taking advantage of null models. Subsequently, based on solid evidence of the empirical test, two novel weighting mechanisms for link prediction are proposed utilizing reciprocity as extra information. The performance of proposed methods is comprehensively studied on eight realistic networks compared with several groups of benchmarks. Experimental results indicate that the proposed methods are more effective and robust than two state-of-the-art weighting methods and eight well-performing similarity indices.

/pdf/link-prediction-in-directed-networks-utilizing-the-role-of-3bgv91y6dy.pdf

Link Prediction in Directed Networks Utilizing the Role of Reciprocal Links

Incomplete mixed data-driven outlier detection based on local-global neighborhood information

With the rapid evolution of mobile communication networks, the number of subscribers and their communication practices is increasing dramatically worldwide. However, fraudsters are also sniffing out the benefits. Detecting fraudsters from the massive volume of call detail records (CDR) in mobile communication networks has become an important yet challenging topic. Fortunately, Graph neural network (GNN) brings new possibilities for telecom fraud detection. However, the presence of the graph imbalance and GNN oversmoothing problems makes fraudster detection unsatisfactory. To address these problems, we propose a new fraud detector. First, we transform the user features with the help of a multilayer perceptron. Then, a reinforcement learning-based neighbor sampling strategy is designed to balance the number of neighbors of different classes of users. Next, we perform user feature aggregation using GNN. Finally, we innovatively treat the above augmented GNN as weak classifier and integrate multiple weak classifiers using the AdaBoost algorithm. A balanced focal loss function is also used to monitor the model training error. Extensive experiments are conducted on two open real-world telecom fraud datasets, and the results show that the proposed method is significantly effective for the graph imbalance problem and the oversmoothing problem in telecom fraud detection.

/pdf/mining-mobile-network-fraudsters-with-augmented-graph-neural-34js64q5.pdf

Mining Mobile Network Fraudsters with Augmented Graph Neural Networks

Along with the rapid evolution of mobile communication technologies, such as 5G, there has been a drastically increase in telecom fraud, which significantly dissipates individual fortune and social wealth. In recent years, graph mining techniques are gradually becoming a mainstream solution for detecting telecom fraud. However, the graph imbalance problem, caused by the Pareto principle, brings severe challenges to graph data mining. This is a new and challenging problem, but little previous work has been noticed. In this paper, we propose a Graph ATtention network with COst-sensitive BOosting (GAT-COBO) for the graph imbalance problem. First, we design a GAT-based base classifier to learn the embeddings of all nodes in the graph. Then, we feed the embeddings into a well-designed cost-sensitive learner for imbalanced learning. Next, we update the weights according to the misclassification cost to make the model focus more on the minority class. Finally, we sum the node embeddings obtained by multiple cost-sensitive learners to obtain a comprehensive node representation, which is used for the downstream anomaly detection task. Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors. In addition, our model is also helpful for solving the widespread over-smoothing problem in GNNs. The GAT-COBO code and datasets are available at https://github.com/xxhu94/GAT-COBO.

pdf/gat-cobo-cost-sensitive-graph-neural-network-for-telecom-oya97qi5.pdf

GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection

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