With the explosive adoption of web-based technology, the amount of graph-structured data has increased dramatically, resulting in a higher demand to find anomalous patterns in different types of online services, such as fraudulent transactions, fake accounts, and coordinated malicious campaigns. The performance of anomaly detection in graphs of web systems is a challenge due to the sparse and camouflaged nature of such anomalies, multi-granular irregularity features, and the instability of the generative models in a real-world web application. To address these constraints, we introduce a new unified generative framework GRAND (Graph Anomaly Detection via Diffusion) suitable for graph data of the web domain. GRAND applies a novel dual-diffusion strategy: continuous Gaussian diffusion for node features and discrete diffusion for edges, combined with a structural-prior-conditioned graph transformer denoiser. Besides this, the framework adds strong anomaly scoring mechanisms with adaptive pooling and normalization schemes to detect the subtle anomaly signals typical of web data. It provides degeneracy detection for inference stability. GRAND has been demonstrated to obtain better results than state-of-the-art methods by extensive evaluations of different benchmark datasets. GRAND demonstrates strong performance in the detection of money laundering, particularly on the Elliptic dataset, a Bitcoin transaction graph typically for financial web applications. © 2026 Owner/Author.

In the rapidly evolving landscape of e-commerce, visual search has become a cornerstone of user experience, enabling customers to find products using images rather than traditional text queries. However, a comprehensive analysis reveals a persistent challenge: nearly half of retrieval failures stem from systems that prioritize superficial visual similarity over semantic relevance, resulting in frustrating user experiences where searches return visually similar but functionally different products. This limitation becomes particularly acute in Business-to-Business environments, where incorrect product recommendations can have significant operational and safety implications. In this paper, we propose a novel solution, Adaptive Retrieval with Category-aware Hierarchical sEmantic Refinement (ARCHER), which presents a novel multimodal retrieval framework that addresses these challenges through progressive semantic alignment. Unlike existing approaches that treat all visual similarities equally, ARCHER employs a sophisticated three-stage learning strategy that systematically builds from coarse-grained category understanding to fine-grained product discrimination. The framework begins with Proto-Align Enhancement to establish foundational visual-textual correspondences, progresses through Cross-View Learning to develop robust viewpoint-invariant representations, and culminates with Margin-based Representation Enhancement that learns to distinguish between visually similar but functionally distinct products. Most significantly, the framework has been successfully deployed on Alibaba.com's B2B platform since December 2024, where it serves millions of daily queries and has achieved a measurable 2.1% improvement in click-through rates.

Recent image-based rendering (IBR) methods usually adopt plenty of views to reconstruct dense scene geometry. However, the number of available views is limited in prac-tice. When only few views are provided, the performance of these methods drops off significantly, as the scene geometry becomes sparse as well. Therefore, in this paper, we propose Sparse-IBRNet (SIBRNet) to perform robust IBR on sparse scene geometry by depth completion. The SIBR-Net has two stages, geometry recovery (GR) stage and light blending (LB) stage. Specifically, GR stage takes sparse depth map and RGB as input to predict dense depth map by exploiting the correlation between two modals. As in-accuracy of the complete depth map may cause projection biases in the warping process, LB stage first uses a bias-corrected module (BCM) to rectify deviations, and then ag-gregates modified features from different views to render a novel view. Extensive experimental results demonstrate that our method performs best on sparse scene geometry than re-cent IBR methods, and it can generate better or comparable results as well when the geometric information is dense.1