Structural Graph Clustering is a well-known problem that aims to identify clusters and distinguish between special roles, such as hub and outlier. However, SCAN, the fundamental structural clustering model, is designed for pairwise graphs and fails to capture the unique structural information inherent in hypergraphs when clustering hypergraphs. Motivated by this, we propose a new structural clustering model, HSCAN, specifically for hypergraphs. We further design an Order-Index to accelerate fetching the key information of the HSCAN and a Lightweight Similarity Bucket Index to reduce the index cost. Next, we present an index-based sequential query algorithm with high performance and a parallel query algorithm to process large hypergraphs faster. Additionally, we provide the algorithms for constructing Order-Index and Lightweight Similarity Bucket Index. Extensive experiments on both real-world and synthetic datasets show that HSCAN performs better than existing models, and the two index-based query algorithms are up to three orders of magnitude faster than the existing algorithm.

A criminal community is an interior group where individuals commit criminal activities with high intention. Therefore, the detection is of great importance to prevent potential crimes early in the stage. Prior studies focused on methods in modularity or network analysis based on topology. These approaches, however, do not work well for detecting minority communities, which is also a key issue in criminal detection. The main reasons are: 1) modularity-based approach cannot identify the inside community structure due to the resolution limit, 2) topology-based network analysis cannot fully leverage personal feature information, such as the amount and frequency of criminal transactions. To address these problems, this paper proposes a novel framework named DccGraph (Detect criminal communities using a Graph neural network) to enhance the overall performance of detecting criminal communities, especially minority ones. First, we extract the feature information of criminals and balance the distribution of criminal community members to construct an Augmented Criminal Network(ACN), which alleviates representation collapse and distinguish the feature of criminals in minority communities. In that case, it is capable to go beyond the resolution limit and locate minority communities effectively. Then, we design a criminal-oriented siamese graph encoder to capture both structural and feature information of criminals in the ACN. Specifically, feature interference and connection disturbance of criminals are employed to enrich the feature representation. To the best of our knowledge, DccGraph is the first framework to apply a graph neural network on criminal community detection. Experiments on several real-life dataset and benchmark datasets show that: DccGraph successfully outperforms eight baselines by 29.25%, 48.04%, 35.37%, and 40.98% on ACC, NMI, ARI, and F1, respectively. The dataset and the code for this framework are publicly available.

Method names are crucial to program comprehension and maintenance. Recently, many approaches have been proposed to automatically recommend method names and detect inconsistent names. Despite promising, their results are still suboptimal considering the three following drawbacks: 1) These models are mostly trained from scratch, learning two different objectives simultaneously. The misalignment between two objectives will negatively affect training efficiency and model performance. 2) The enclosing class context is not fully exploited, making it difficult to learn the abstract functionality of the method. 3) Current method name consistency checking methods follow a generate-then-compare process, which restricts the accuracy as they highly rely on the quality of generated names and face difficulty measuring the semantic consistency.In this paper, we propose an approach named AUMENA to AUtomate MEthod NAming tasks with context-aware prompt-tuning. Unlike existing deep learning based approaches, our model first learns the contextualized representation(i.e., class attributes) of programming language and natural language through the pre-training model, then fully exploits the capacity and knowledge of large language model with prompt-tuning to precisely detect inconsistent method names and recommend more accurate names. To better identify semantically consistent names, we model the method name consistency checking task as a two-class classification problem, avoiding the limitation of previous generate-then-compare consistency checking approaches. Experiment results reflect that AUMENA scores 68.6%, 72.0%, 73.6%, 84.7% on four datasets of method name recommendation, surpassing the state-of-the-art baseline by 8.5%, 18.4%, 11.0%, 12.0%, respectively. And our approach scores 80.8% accuracy on method name consistency checking, reaching an 5.5% outperformance. All data and trained models are publicly available.

Code review is one of the best practices as a powerful safeguard for software quality. In practice, senior or highly skilled reviewers inspect source code and provide constructive comments, consider- ing what authors may ignore, for example, some special cases. The collaborative validation between contributors results in code being highly qualified and less chance of bugs. However, since personal knowledge is limited and varies, the efficiency and effectiveness of code review practice are worthy of further improvement. In fact, it still takes a colossal and time-consuming effort to deliver useful review comments. This paper explores a synergy of multiple practical review comments to enhance code review and proposes AUGER (AUtomatically GEnerating Review comments): a review comments generator with pre-training models. We first collect empirical review data from 11 notable Java projects and construct a dataset of 10,882 code changes. By leveraging Text-to-Text Transfer Transformer (T5) models, the framework synthesizes valuable knowledge in the training stage and effectively outperforms baselines by 37.38% in ROUGE-L. 29% of our automatic review comments are considered useful according to prior studies. The inference generates just in 20 seconds and is also open to training further. Moreover, the performance also gets improved when thoroughly analyzed in case study.

Recent low-rank based tensor completion (LRTC) algorithms have been successfully applied into color image inpainting. However, most of existing LRTC algorithms treat each dimension of tensors equally, which ignores the differences of the intrinsic structure correlations among dimensions. In this paper, we make a detailed analysis about the rank properties of each dimension and design a simple yet effective reweighted low-rank tensor completion model that truthfully capture the intrinsic structure correlations with reduced computational burden. Moreover, to capture the local smooth and piecewise priors of tensors, we integrate total variation into our model. Considering two formulations of LRTC, tensor unfolding and tensor decomposition, we propose corresponding two algorithms for color image recovery. Extensive experimental results on color image recovery show the efficiency and effectiveness of the proposed two algorithms against state-of-the-art competitors.