Unsupervised graph anomaly detection aims to identify irregular patterns in graph-structured data without relying on labeled anomalies. Graph neural networks (GNNs) have advanced GAD by learning effective graph representations through neighborhood aggregation. However, challenges such as anomalous information diffusion impede GNNs to accurately distinguish anomalies from normal ones. To bridge this gap, we propose a novel framework named Selective Anomalous information Filtering for Enhance unsupervised graph anomaly detection (SAFE). SAFE allows nodes to selectively filter anomalous information, preventing the spread of anomalous noise to normal nodes while allowing anomalies to assimilate features from their neighbors. This strategy enhances the reconstruction error disparity between normal and anomalous nodes, thereby improving the accuracy of anomaly detection. Extensive experiments on both synthetic and real-world datasets demonstrate the significant performance gains of SAFE over existing methods.

Multi-modal knowledge graph (MMKG) completion aims to infer missing links in knowledge graphs by leveraging both structural information and multi-modal data, such as images and text. While existing methods focus on multi-modal feature fusion, they often fail to disentangle modality-specific and shared features, leading to redundant and suboptimal embeddings. Furthermore, implicit semantic correlations between entities are frequently overlooked. To address these issues, we propose INCADE, an Implicit Entity Community-Aware Disentangled Expert framework that jointly captures implicit high-order correlations among entities and disentangles modality-specific and shared representations. INCADE introduces an entity community-aware encoder to model implicit entity interactions and auxiliary tasks (e.g., modality memorization and unlearning) to enforce disentanglement. This advantage enables the model to easily utilizes both explicit and implicit multi-modal information. Extensive experiments on standard MMKG benchmarks demonstrate that INCADE achieves state-of-the-art performance, outperforming 14 baseline models. Code is available at https://github.com/shchli/INCADE.

Micro-video popularity prediction (MVPP) plays a crucial role in diverse real-world applications. While recent popularity prediction methods leveraging multimodal content have shown impressive performance, they face two unresolved challenges: (1) neglecting the presence of noise in micro-videos and (2) failing to address incomplete modalities. To tackle these issues, we propose FMOE, a novel Flexible Mixture of Modality Experts framework designed for robust MVPP. FMOE employs a two-stage learning strategy, consisting of pre-training and post-training phases. Specifically, during pre-training, FMOE extracts discriminative unimodal representations by training modality-specific experts. In the post-training phase, FMOE dynamically captures cross-modal correlations by mixing experts. Subsequently, FMOE quantifies uncertainty within micro-videos using an uncertainty-aware router, which maps modal representations into a Gaussian distribution space. Extensive experiments on three real-world datasets demonstrate that FMOE significantly outperforms all competitive baselines by a large margin.

Temporal knowledge graph reasoning is an essential task that holds immense value in diverse real-world applications. Existing studies mainly focus on leveraging structural and sequential dependencies, excelling in tasks like entity and link prediction. However, they confront a notable interpretability gap in their predictions, a pivotal facet for comprehending model behavior. In this study, we propose an innovative method, LSGAT, which not only exhibits remarkable precision in entity predictions but also enhances interpretability by identifying pivotal historical events influencing event predictions. LSGAT enables concise explanations for prediction outcomes, offering valuable insights into the otherwise enigmatic "black box" reasoning process. Through an exploration of the implications of the most influential events, it facilitates a deeper understanding of the underlying mechanisms governing predictions.

Recently, the field of Speech Enhancement has witnessed the success of diffusion-based generative models. However, these diffusion-based methods used to take multiple iterations to generate high-quality samples, leading to high computational costs and inefficiency. In this paper, we propose SDFEN (Shallow Diffusion for Fast spEech eNhancement), a novel approach for addressing the inefficiency problem while enhancing the quality of generated samples by reducing the iterative steps in the reverse process of diffusion method. Specifically, we introduce the shallow diffusion strategy initiating the reverse process with an adaptive time step to accelerate inference. In addition, a dedicated noisy predictor is further proposed to guide the adaptive selection of time step. Experiment results demonstrate the superiority of the proposed SDFEN in effectiveness and efficiency.

The rising popularity of online social network services has attracted a lot of research focusing on mining various user patterns. Among them, accurate IP geolocation is essential for a plethora of location-aware applications. However, despite extensive research efforts and significant advances, the "accurate and reliable'' desideratum is yet to be achieved at a higher quality level. This work presents a graph neural network (GNN)-based model, called TrustGeo, for trustworthy street-level IP geolocation. A distinct and important aspect of TrustGeo is the incorporation of sources of uncertainty in the learning process. The results of our extensive experimental evaluations on three real-world datasets demonstrate the superiority of our framework in significantly improving the accuracy and trustworthiness of street-level IP geolocation. Our code and datasets are available at https://github.com/ICDM-UESTC/TrustGeo.

IP geolocation refers to the process of determining the geographic locations of Internet Protocol (IP) addresses, which is important for mobile computing and spatial data management. Despite extensive research efforts, a client-independent geolocation service with high accuracy and reliability has not yet been developed. This paper presents a graph neural network (GNN) model, dubbed RIPGeo, for robust street-level IP geolocation. Three factors that affect data quality are identified, and the importance of considering data quality in algorithm development is emphasized. Two novel self-supervised perturbational training strategies are proposed to enhance the generalization and robustness of the model. A multi-task learning framework is introduced to solve the homogenized representation problem caused by perturbational training, demonstrating much more efficiency than prevailing solutions. Theoretical analysis and experimental results demonstrate the superiority of our framework in significantly improving the accuracy and stability of street-level IP geolocation.