Deep graph clustering is a highly significant tool for community detection, enabling the identification of strongly connected groups of nodes within a graph. This technology is crucial in various fields such as education and E-learning. However, deep graph clustering can be more misled by the graph topology, disregarding node information. For example, an excessive number of intercommunity edges or insufficient intracommunity edges can lead to inaccurate community distinction by the model. In this article, we propose a novel model, deep Graph Clustering with Triple Fusion Autoencoder (GC-TriFA) for community detection, which utilizes a triple encoding fusion mechanism to balance the incorporation of node and topological information, thereby mitigating this issue. Specifically, GC-TriFA employs a shallow linear coding fusion and a deep coding fusion method within an autoencoder structure. This approach enables the model to simultaneously learn and capture the embedding of cross-modality information and later utilizes weight fusion to equalize the two modalities. Furthermore, GC-TriFA also reconstructs the graph structure, learns relaxed$k$-means, and undergoes self-supervised training to enhance the quality of the graph embedding. The experimental results of GC-TriFA, when evaluated as an end-to-end model on publicly available datasets, demonstrate its superiority compared to the baseline models.

The collusive fraudulent behaviors on e-commerce platforms lead to proliferation of fraudulent reviews, which disrupt fair competition among merchants and mislead consumers’ shopping decisions. Detection of spammer groups helps purify the e-commerce environment and enhances consumers’ shopping experience. However, existing graph-based methods for detecting spammer groups first learn user node vector representations from the graph, and then use clustering methods to obtain candidate groups. Such separate two-stage detection methods are difficult to obtain high-quality candidate groups, resulting in suboptimal detection performance. Additionally, current graph construction methods used in spammer group detection do not fully consider the characteristics of spammer groups, which limits the detection performance. Aiming these concerns, we integrate heterogeneous graph attention network (HGAN) with label propagation (LP) for detecting spammer groups. First, we build a heterogeneous weighted directed (HWD) graph by analyzing the dataset and assign an initial label to each node. Then, we integrate a HGAN-module with an LP-module to obtain the HWD graph’s node embeddings and simultaneously generate candidate groups. We enhance the quality of embeddings and groups through the collaborative optimization between the predicted labels obtained from the HGAN-module and the pseudo-labels obtained from the LP-module. Finally, we calculate the suspiciousness values of groups using the reconstruction loss of the autoencoder for spammer group identification. Experiments conducted on real-world review datasets, including Amazon, Yelp, and YelpChi, demonstrate that our method achieves significant improvements in average Precision@k and Recall@k metrics compared with state-of-the-art baseline approaches.

Abstract Group shilling attacks are more threatening than individual shilling attacks due to the collusive behaviours among group members, which pose a great challenge to the credibility of recommender systems. Detection of group shilling attacks can reduce the risk caused by such attacks and ensure the credibility of recommendations. The existing methods for detecting group shilling attacks mainly extract features from the rating patterns of users at group level to measure the shilling behaviours of groups. However, they may become ineffective with the change of attack strategy, resulting in a decrease in detection performance. Aiming at this problem, a new solution based on user multi-dimensional features and collusive behaviour analysis is presented for detecting group shilling attacks. First, we employ the information entropy and latent semantic analysis to analyse the user behavioural patterns from dimensions of item, rating, time and interest, and propose a suite of indicators to measure the anomaly behaviours of users. Second, we propose a measure based on the multi-dimensional features of users to capture the collusion of group members from the perspective of their synchronized behaviours and abnormal behaviours, and treat the groups with high collusion as candidate groups. Finally, based on the multi-dimensional features of users, we construct the user behaviour similarity matrix using Gaussian radial basis function (Gaussian-RBF) and adopt the spectral clustering algorithm to spot group shilling attackers in the candidate groups. Experiments show that the detection performance (F1-measure) of the proposed method can achieve 0.965, 0.964, 0.991 and 0.868 on the Netflix, CiaoDVD, Epinions and Amazon datasets, respectively, which is better than that of state-of-the-art methods.

Abstract As an extension of conventional top-K item recommendation solution, bundle recommendation has aroused increasingly attention. However, because of the extreme sparsity of user-bundle (UB) interactions, the existing top-K item recommendation methods suffer from poor performance when applied to bundle recommendation. While some graph-based approaches have been proposed for bundle recommendation, these approaches primarily leverage the bipartite graph to model the UB interactions, resulting in suboptimal performance. In this paper, a dual hypergraph contrastive learning model is proposed for bundle recommendation. First, we model the direct and indirect UB interactions as hypergraphs to represent the higher-order UB relations. Second, we utilize the hypergraph convolution networks to learn the user and bundle embeddings from the hypergraphs, and improve the learned embeddings through a bidirectional contrastive learning strategy. Finally, we adopt a joint loss that combines the InfoBPR loss supporting multiple negative samples and the contrastive losses to optimize model parameters for prediction. Experiments on the real-world datasets indicate that our model performs better than the state-of-the-art baseline methods.