Graph anomaly detection is crucial for identifying anomalous nodes within graphs and addressing applications like financial fraud detection and social spam detection. Recent spectral graph neural network methods advance graph anomaly detection by focusing on anomalies that notably affect the distribution of graph spectral energy. Such spectrum-based methods rely on two steps: graph wavelet extraction and feature fusion. However, both steps are hand-designed, capturing incomprehensive anomaly information of wavelet-specific features and resulting in their inconsistent feature fusion. To address these problems, we propose a dynamic spectral graph anomaly detection framework DSGAD to adaptively capture comprehensive anomaly information and perform consistent feature fusion. DSGAD introduces dynamic wavelets, consisting of trainable wavelets to adaptively learn anomalous patterns and capture wavelet-specific features with comprehensive anomaly information. Furthermore, the consistent fusion of wavelet-specific features achieves dynamic fusion by combining wavelet-specific feature extraction with energy difference and channel convolution fusion using location correlation. Experimental results on four datasets substantiate the efficacy of our DSGAD method, surpassing state-of-the-art methods in both homogeneous and heterogeneous graphs.

Recommending hashtags for micro-videos is a challenging task due to the following two reasons: 1) micro-video is a unity of multi-modalities, including the visual, acoustic, and textual modalities. Therefore, how to effectively extract features from multi-modalities and utilize them to express the micro-video is of great significance; 2) micro-videos usually include moods and feelings, which may provide crucial cues for recommending proper hashtags. However, most of the existing works have not considered the sentiment of media data for hashtag recommendation. In this paper, the senTiment enhanced multi-mOdal Attentive haShtag recommendaTion model (TOAST) is proposed for micro-video hashtag recommendation. Different from previous hashtag recommendation models, which merely consider content features, sentiment features of modalities are further incorporated in TOAST to improve the recommendation performance of the sentiment hashtags (e.g., #funny, #sad). Specifically, the multi-modal content features and the multi-modal sentiment features are modeled by a content common space learning branch based on self-attention and a sentiment common space learning branch, respectively. Furthermore, the varying importance of the multi-modal sentiment and content features are dynamically captured via an attention neural network according to their consistency with the hashtag semantic embedding by an attention neural network. Extensive experiments on a real-world dataset have demonstrated the effectiveness of the proposed method compared with the baseline methods. Meanwhile, the findings from the experiments may provide new insight for future developments of micro-video hashtag recommendation.

Semantic segmentation for lightweight object parsing is a very challenging task, because both accuracy and efficiency (e.g., execution speed, memory footprint or computational complexity) should all be taken into account. However, most previous works pay too much attention to one-sided perspective, either accuracy or speed, and ignore others, which poses a great limitation to actual demands of intelligent devices. To tackle this dilemma, we propose a novel lightweight architecture named Context-Integrated and Feature-Refined Network (CIFReNet). The core components of CIFReNet are the Long-skip Refinement Module (LRM) and the Multi-scale Context Integration Module (MCIM). The LRM is designed to ease the propagation of spatial information between low-level and high-level stages. Furthermore, channel attention mechanism is introduced into the process of long-skip learning to boost the quality of low-level feature refinement. Meanwhile, the MCIM consists of three cascaded Dense Semantic Pyramid (DSP) blocks with image-level features, which is presented to encode multiple context information and enlarge the field of view. Specifically, the proposed DSP block exploits a dense feature sampling strategy to enhance the information representations without significantly increasing the computation cost. Comprehensive experiments are conducted on three benchmark datasets for object parsing including Cityscapes, CamVid, and Helen. As indicated, the proposed method reaches a better trade-off between accuracy and efficiency compared with the other state-of-the-art methods.