Graph Neural Networks (GNNs) are vulnerable to backdoor attacks, where adversaries implant malicious triggers to manipulate model predictions. Existing graph backdoor attacks are susceptible to defense mechanisms or robust classifiers because they rely on subgraph injection or structural perturbations, e.g., creating additional edges to attach backdoor triggers to the original graph. To enhance the stealthiness of graph backdoors, we propose SPEAR, a novel structure-preserving graph backdoor attack that avoids modifying the graph's topology. SPEAR operates within a limited attack budget by selectively perturbing node attributes while ensuring the triggers exert significant influence through a global importance-driven feature selection strategy. Additionally, a neighborhood-aware trigger generator is employed to underpin a high attack success rate by utilizing semantic information from the neighborhood. SPEAR amplifies effectiveness and stealthiness by combining subtle yet impactful attribute manipulation with a refined trigger generation mechanism. Extensive experiments demonstrate that SPEAR achieves state-of-the-art effectiveness in bypassing defenses on real-world datasets, establishing it as a potent and stealthy backdoor attack for graph-based tasks. Code is available at https://github.com/yhDing/SPEAR.

Multi-behavior sequential recommendation (MBSR) aims to learn dynamic user preference from historical heterogeneous user interactions for identifying the next item under target behavior (i.e., purchase). Although significant efforts have been devoted to modeling users over observed multi-behavior interaction sequences, user modeling with dynamic behavior-aware multiple interests and elimination of inherent noises within these interactions are still underexplored. This limits user representations' awareness of true preference evolution and further constrains recommendation performance. To address the aforementioned issues, we propose a Multi-Interest Network with Simple Diffusion (MISD) via a combination of multi-interest learning and diffusion generative process for MBSR. Concretely, the dynamic multi-interest network is proposed to generate time-evolving personalized interests from the encoded dual-granularity user sequential patterns, leading to more accurate user preference learning. Additionally, simple diffusion is proposed to model the complex latent preference generation procedures in an iterative denoising manner, thereby alleviating the effect of noisy interactions. Extensive experiments on three real-world datasets demonstrate that MISD consistently outperforms various state-of-the-art recommendation methods under multiple settings (e.g., clean and noisy training).

In recent years, Heterogeneous Graph Neural Networks (HGNNs) have gained increasing attention due to their excellent performance in applications. However, the lack of high-quality benchmarks in new fields has become a critical limitation for developing and applying HGNNs. To accommodate the urgent need for emerging fields and the advancement of HGNNs, we present two large-scale, real-world, and challenging heterogeneous graph datasets from real scenarios: risk commodity detection and takeout recommendation. Meanwhile, we establish standard benchmark interfaces that provide over 40 heterogeneous graph datasets. We provide initial data split, unified evaluation metrics, and baseline results for future work, making it fair and handy to explore state-of-the-art HGNNs. Our interfaces also offer a comprehensive toolkit to research the characteristics of graph datasets. The above new datasets are publicly available on https://zenodo.org/communities/hgd, and the interface codes are available at https://github.com/BUPT-GAMMA/hgbi.

With the popularity of online shopping in recent years, various prohibited items are continuously attacking e-commerce portals. Searching and deleting such risk items online has played a fundamental role in protecting the health of e-commerce trades. To mitigate negative impact of limited supervision and adversarial behaviors of malicious sellers, current state-of-the-art work mainly introduces heterogeneous graph neural network with further improvements such as graph structure learning, pairwise training mechanism, etc. However, performance of these models is highly limited since domain knowledge is indispensable for identifying prohibited items but ignored by these methods. In this paper, we propose a novel Knowledge Based Prohibited item Detection system (named KBPD) to break through this limitation. To make full use of rich risk knowledge, the proposed method introduces the Risk-Domain Knowledge Graph (named RDKG), which is encoded by a path-based graph neural network method. Furthermore, to utilize information from both the RDKG and the Heterogeneous Risk Graph (named HRG), an interactive fusion framework is proposed and further improves the detection performance. We collect real-world datasets from the largest Chinese second-hand commodity trading platform, Xianyu. Both offline and online experimental results consistently demonstrate that KBPD outperforms the state-of-the-art baselines. The improvement over the second-best method is up to 22.67% in the AP metric.

Prohibited item detection is an important problem in e-commerce, where the goal is to detect illegal items online for evading risks and stemming crimes. Traditional solutions usually mine evidence from individual instances, while current efforts try employing advanced Graph Neural Networks (GNN) to utilize multiple risk-relevant structures of items. However, it still remains two essential challenges, including weak structure and weak supervision. This work proposes the Risk Graph Structure Learning model (RGSL) for prohibited item detection. RGSL first introduces structure learning into large-scale risk graphs, to reduce noisy connections and add similar pairs. It then designs the pairwise training mechanism, which transforms the detection process as a metric learning from candidates to their similar prohibited items. Furthermore, RGSL generates risk-aware item representations and searches risk-relevant pairs for structure learning iteratively. We test RGSL on three real-world scenarios, and the improvements to baselines are up to 21.91% in AP and 18.28% in MAX-F1. Meanwhile, RGSL has been deployed on an e-commerce platform, and the improvements to traditional solutions are up to 23.59% in [email protected] and 6.52% in [email protected]

Prohibited item detection, which aims to detect illegal items hidden on e-commerce platforms, plays a significant role in evading risks and preventing crimes for online shopping. While traditional solutions usually focus on mining evidence from independent items, they cannot effectively utilize the rich structural relevance among different items. A naive idea is to directly deploy existing supervised graph neural networks to learn node representations for item classification. However, the very few manually labeled items with various risk patterns introduce two essential challenges: (1) How to enhance the representations of enormous unlabeled items? (2) How to enrich the supervised information in this few-labeled but multiple-pattern business scenario? In this paper, we construct item logs as a Heterogeneous Risk Graph (HRG), and propose the novel Heterogeneous Self-supervised Prohibited item Detection model (HSPD) to overcome these challenges. HSPD first designs the heterogeneous self-supervised learning model, which treats multiple semantics as the supervision to enhance item representations. Then, it presents the directed pairwise labeling to learn the distance from candidates to their most relevant prohibited seeds, which tackles the binary-labeled multi-patterned risks. Finally, HSPD integrates with self-training mechanisms to iteratively expand confident pseudo labels for enriching supervision. The extensive offline and online experimental results on three real-world HRGs demonstrate that HSPD consistently outperforms the state-of-the-art alternatives.

In real-world problems, heterogeneous entities are often related to each other through multiple interactions, forming a Heterogeneous Interaction Graph (HIG). While modeling HIGs to deal with fundamental tasks, graph neural networks present an attractive opportunity that can make full use of the heterogeneity and rich semantic information by aggregating and propagating information from different types of neighborhoods. However, learning on such complex graphs, often with millions or billions of nodes, edges, and various attributes, could suffer from expensive time cost and high memory consumption. In this article, we attempt to accelerate representation learning on large-scale HIGs by adopting the importance sampling of heterogeneous neighborhoods in a batch-wise manner, which naturally fits with most batch-based optimizations. Distinct from traditional homogeneous strategies neglecting semantic types of nodes and edges, to handle the rich heterogeneous semantics within HIGs, we devise both type-dependent and type-fusion samplers where the former respectively samples neighborhoods of each type and the latter jointly samples from candidates of all types. Furthermore, to overcome the imbalance between the down-sampled and the original information, we respectively propose heterogeneous estimators including the self-normalized and the adaptive estimators to improve the robustness of our sampling strategies. Finally, we evaluate the performance of our models for node classification and link prediction on five real-world datasets, respectively. The empirical results demonstrate that our approach performs significantly better than other state-of-the-art alternatives, and is able to reduce the number of edges in computation by up to 93%, the memory cost by up to 92% and the time cost by up to 86%.