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Most community detection methods focus on the similarities between detection nodes to achieve community partitioning. Traditional network representation learning methods are also limited to the local context of the central nodes, which results in less truly representative results. This article examines nodes’ influence information, nodes’ community affiliating information, and similarity of community topologies and proposes a more effective node representation strategy. According to the local node information and global topology in the social network graph, a method of combining local node embedding and global community embedding is also designed. The effectiveness of learning node representation and community representation is improved by our approach. The proposed model can also effectively detect overlapping communities.

Software reliability assessment has become an important means to evaluate software failure behaviors and ensure the software reliability. With the continuous development of automation technology, considerably number of reliability assessment tools have been developed for software reliability analysis, estimation and prediction. In this paper, most popular software reliability assessment tools are reviewed. Five of them, SFRAT, SRATS, RSRAT, PHSRM and SMERFS, are selected for comprehensively comparative analysis. We also construct the evaluation index system of software reliability assessment tools, and analyze the differences in software reliability estimation and prediction ability of these tools through experimental studies. In the end, we summarize the characteristics and potential application scenarios of each tool, and further discuss the development trends and future research directions.

The clustering algorithm plays an important role in data mining and image processing. The breakthrough of algorithm precision and method directly affects the direction and progress of the following research. At present, types of clustering algorithms are mainly divided into hierarchical, density-based, grid-based and model-based ones. This paper mainly studies the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm, which is a new clustering method based on density. The algorithm has the characteristics of no iterative process, few parameters and high precision. However, we found that the clustering algorithm did not consider the original topological characteristics of the data. We also found that the clustering data is similar to the social network nodes mentioned in DeepWalk, which satisfied power-law distribution. In this study, we tried to consider the topological characteristics of the graph in the clustering algorithm. Based on previous studies, we propose a clustering algorithm that adds the topological characteristics of original data on the basis of the CFSFDP algorithm. Our experimental results show that the clustering algorithm with topological features significantly improves the clustering effect and proves that the addition of topological features is effective and feasible.