Recent studies have highlighted the limitations of message-passing based graph neural networks (GNNs), e.g., limited model expressiveness, over-smoothing, over-squashing, etc. To alleviate these issues, Graph Transformers (GTs) have been proposed which work in the paradigm that allows message passing to a larger coverage even across the whole graph. Hinging on the global range attention mechanism, GTs have shown a superpower for representation learning on homogeneous graphs. However, the investigation of GTs on heterogeneous information networks (HINs) is still under-exploited. In particular, on account of the existence of heterogeneity, HINs show distinct data characteristics and thus require different treatment. To bridge this gap, in this paper we investigate the representation learning on HINs with Graph Transformer, and propose a novel model named HINormer, which capitalizes on a larger-range aggregation mechanism for node representation learning. In particular, assisted by two major modules, i.e., a local structure encoder and a heterogeneous relation encoder, HINormer can capture both the structural and heterogeneous information of nodes on HINs for comprehensive node representations. We conduct extensive experiments on four HIN benchmark datasets, which demonstrate that our proposed model can outperform the state-of-the-art.

How to effectively explore inter-frame information is critical for video denoising. Existing methods often rely on complex architectures, such as optical flow estimation and cross-frame self-attention, which introduce high computational costs and limit their practicality in real-world scenarios. To address this limitation, we propose a simple yet efficient deep Frequency-Separable Temporal Network (FSTN) for video denoising. FSTN utilizes the multi-scale analysis capability of wavelet transform to extract high-frequency and low-frequency information at the feature level, enabling faster processing while maintaining high-quality reconstruction. To further reduce computational complexity and enhance detail preservation, we develop a learnable high-frequency processing module that adaptively filters noise and recovers edge details. Additionally, to effectively utilize information from long-range frames, we propose a low-frequency propagation method equipped with a temporal feature alignment module. This method enables the efficient transfer of structural information from distant frames, ensuring temporal consistency and enhancing denoising performance. Extensive experiments demonstrate that our method has 1.28× fewer network parameters than state-of-the-art efficient video denoising methods, such as BasicVSR++, and requires less computational cost while achieving comparable performance.

The code review comment (CRC) is pivotal in the process of modern code review. It provides reviewers with the opportunity to identify potential bugs, offer constructive feedback, and suggest improvements. Clear and concise code review comments (CRCs) facilitate the communication between developers and are crucial to the correct understanding of the identified issues and proposed solutions. Despite the importance of CRCs’ clarity, there is still a lack of guidelines on what constitutes a good clarity and how to evaluate it. In this paper, we conduct a comprehensive study on understanding and evaluating the clarity of CRCs. We first derive a set of attributes related to the clarity of CRCs, namely RIE attributes (i.e., Relevance , Informativeness , and Expression ), as well as their corresponding evaluation criteria based on our literature review and survey with practitioners. We then investigate the clarity of CRCs in open-source projects written in nine programming languages and find that a large portion (i.e., 28.8%) of the CRCs lack the clarity in at least one of the attributes. Finally, we explore the potential of automatically evaluating the clarity of CRCs by proposing ClearCRC. Experimental results show that ClearCRC with pre-trained language models is promising for effective evaluation of the clarity of CRCs, achieving a balanced accuracy up to 73.04% and a F-1 score up to 94.61%.

The integration of Large Language Models (LLMs) with Graph Representation Learning (GRL) marks a significant evolution in analyzing complex data structures. This collaboration harnesses the sophisticated linguistic capabilities of LLMs to improve the contextual understanding and adaptability of graph models, thereby broadening the scope and potential of GRL. Despite a growing body of research dedicated to integrating LLMs into the graph domain, a comprehensive review that deeply analyzes the core components and operations within these models is notably lacking. Our survey fills this gap by proposing a novel taxonomy that breaks down these models into primary components and operation techniques from a novel technical perspective. We further dissect recent literature into two primary components including knowledge extractors and organizers, and two operation techniques including integration and training stratigies, shedding light on effective model design and training strategies. Additionally, we identify and explore potential future research avenues in this nascent yet underexplored field, proposing paths for continued progress.