Graph-based fraud detection (GFD) aims to identify fraud nodes within graph-structured data that significantly deviate from the majority of benign nodes. However, existing graph neural networks (GNNs) often struggle in GFD scenarios due to their reliance on homophily assumption, which is frequently violated by the inherent homophily-heterophily mixture of fraud graphs. Moreover, most methods focus primarily on local topology, overlooking mesoscopic community structures, making them less efficient in detecting suspicious patterns like densely connected subgraphs. To address the aforementioned issues, we present NeCo, a novel approach that integrates mixture of neighborhood and community experts for graph-based fraud detection. Specifically, we first introduce a fraud-discriminative representation preservation mechanism from a neighborhood perspective, leveraging the empirical finding that fraud nodes tend to exhibit larger feature propagation discrepancies compared to benign nodes. We then design a community-oriented node representation module that models structural compactness among nodes, enabling the detection of suspicious topological patterns associated with fraud behaviors. By integrating these two complementary perspectives, NeCo can effectively captures both local inconsistency and global structural irregularity. Extensive experiments across five real-world datasets demonstrate the effectiveness of our proposed NeCo over state-of-the-art baselines.

Medical image segmentation is a critical task in the healthcare field. While deep learning techniques have shown promise in this area, they often require a large number of accurately labeled images. To address this issue, semisupervised learning has emerged as a potential solution by reducing the reliance on precise annotations. Among these approaches, the student-teacher framework has garnered attention, but it is limited in its reliance solely on the teacher model for information. To overcome this limitation, we propose a prototype-based mutual consistency learning (PMCL) framework. This framework utilizes two branches that learn from each other, incorporating supervision loss and consistency loss to adapt to minor data perturbations and structural differences. By employing prototype consistency learning, we are able to achieve reliable consistency loss. Our experiments on three public medical image datasets demonstrate that PMCL outperforms other state-of-the-art methods, indicating its potential in semisupervised medical image segmentation. Our framework has the potential to assist medical professionals in enhancing their diagnoses and delivering improved patient care.

Multimodal learning using audio and visual information has improved Violence Detection tasks. However, previous studies overlook the gap between pre-trained networks and the final violence detection task, as well as the semantic inconsistency between audio and visual features. We consider task-irrelevant information caused by the former situation and semantic noise due to the latter as redundancy, negatively affecting overall detection performance. Besides, the prevailing visual modality-centric approach with audio features as guidance may be biased. We contend that both modalities are crucial in violence detection. To address these issues, we propose a Cross-modal and Redundancy-reduced Network for Weakly-Supervised Audio-Visual Violence Detection. Our framework integrates a relation-ware module with a bi-directional cross-modal attention mechanism to explore interactions between modalities. Then, we introduce a feature filter gate to reduce redundancy. Finally, a multi-branch classification module is proposed for better utilization of both modalities. Extensive experiments demonstrate the effectiveness of our approach, surpassing previous methods with state-of-the-art performance in violence detection.