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In this paper, a model-based scheme for recognizing hand-drawn symbols in schematic diagrams using attributed graph (AG) matching in the absence of any information concerning their pose (translation, rotation and scale) is described. The process of AG matching proceeds as follows. First, an observed AG (AG_O) is constructed from single-pixel-width line-representation of an observed symbol. Second, the pose of the AG_O is estimated in terms of translation, rotation and scale with respect to the model AGs (AG_Ms). The search space is effectively pruned by introducing the concept of control vertex and applying geometrical constraints in an early stage. In this step, a small number of candidate AG_Ms are selected. Third, correspondences between components of the observed AG after normalization (AG_O^N) and those of the AG_Ms are found for the given poses. Fourth, distance measures between the AG_O^N and the AG_Ms are calculated, based upon the correspondences. Finally, the AG_O^N is classified as the AG_M with the minimum distance. Experimental results for hand-drawn symbols with and without templates show that using AG matching is very efficient and successful for translation-, rotation- and scale-invariant recognition of hand-drawn symbols in schematic diagrams.

The stroke analysis method is an effective approach for handwritten Chinese character recognition. But as we know, it is very difficult to accurately extract the strokes. In this paper, a robust stroke extraction method is proposed. First, smoothing and thinning processes are applied to smooth the shape and to obtain the skeleton of the observed character. Then the end point, internal point and fork point are detected by calculating their own crossing numbers while the corner points are determined by a knowledge-based iterative method. Virtual-end-points are introduced for separating a stroke into a certain number of line segments without losing the connection relations among them. By representing each line segment as a vertex and the connection relation of two segments as an edge, the observed character can be represented by an attributed graph. Finally, a stroke extraction procedure is proposed to extract the strokes from the global structures of the character. After each stroke of a character is extracted, the cross points can also be determined. Experimental results have shown that the proposed method is more effective than the other methods.^3,5−6

The goal of community search across attributed graphs is to locate the community that takes both attribute cohensiveness and constrained structure into account. The keyword closeness of subgraph is usually measured by similarity distance. However, existing works focus on how to find a community that has most relevant to the keywords of the query vertex through the similarity score, whereas we pay more attention to a community that can jointly cover keywords and find subgraph with the maximum core. To address this problem, we propose a novel query keyword-covered group enlargement community search $(KGEC)$. Given an initial subgraph and a set of query keywords, the $KGEC$ search aims to find the community which satisfies the following conditions: (1) it jointly covers all query keywords; (2) it is a subgraph with the maximum core; (3) it is added the minimum vertex set that meets the conditions (1) and (2). We design a baseline enumerateKGEC algorithm ($EGA$), which enumerates all the vertex combinations that cover the remaining query keywords. To further accelerate the search speed, we propose two heuristic algorithms candidate set-based algorithm $(CSBA)$ and candidate set and keyword combination algorithm $(CSKCA)$, which can effectively speed up the search and find a feasible solution. Finally, we evaluate the performance of our algorithms on two real datasets and show effectiveness and efficiency of our algorithms for $KGEC$ problem.

Service anomalies are difficult to locate accurately due to their propagation through service dependencies in microservice systems. Besides, the protection mechanisms are introduced into the microservice systems to ensure the stable operation of services. However, the existing approaches ignore the impact of protection mechanisms on the root cause localization of abnormal services. Specifically, the circuit breaking and rate limiting mechanisms can refuse service requests and thus change the way of anomaly propagation. Moreover, the different service request frequencies and latency make service dependencies change dynamically, resulting in the different probabilities of anomaly propagation among services. In this paper, we propose a novel framework named MicroGBPM to locate the root cause of abnormal services. We model the anomaly propagation among services as a dynamically constructed service attributed graph with metrics and traces when a failure occurs. To eliminate the impact of the protection mechanisms, we design a two-stage dynamic calibration strategy to adjust the probability of anomaly propagation among services. Then, we propose a random walking approach to calculate the root cause results by using the PageRank algorithm. The experimental results show that MicroGBPM improves the accuracy of root cause localization compared to other approaches in the microservice systems with protection mechanisms.

In recent years, community detection in social network analysis has been a hot research topic that has attracted much more attention in the scientific community and a large number of articles have already been published in the literature. Community detection problems focus on determining the subgraphs of the network that have dense edges and whose nodes are expected to have similar features. Community detection has significant applications in different domains of social network analysis such as sociology, business, criminal detection and recommendation systems. The computational complexity is hampered by the huge size and the dynamic nature of social networks. This paper introduces a comprehensive survey of community detection methods in social networks based on mining attributes and interactions of users. We divide the community detection approaches into three basic categories, namely (1) node content-based approaches, (2) edge content-based approaches, and (3) node- and edge content-based approaches. We also discuss in detail the main idea of each method that is suitable for social network data. Furthermore, to promote the study of community detection, we introduce the benchmark datasets from available sources. Last, we present further research directions as well as some open challenges for the community detection problem in social networks.

The stroke analysis method is an effective approach for handwritten Chinese character recognition. But as we know, it is very difficult to accurately extract the strokes. In this paper, a robust stroke extraction method is proposed. First, smoothing and thinning processes are applied to smooth the shape and to obtain the skeleton of the observed character. Then the end point, internal point and fork point are detected by calculating their own crossing numbers while the corner points are determined by a knowledge-based iterative method. Virtual-end-points are introduced for separating a stroke into a certain number of line segments without losing the connection relations among them. By representing each line segment as a vertex and the connection relation of two segments as an edge, the observed character can be represented by an attributed graph. Finally, a stroke extraction procedure is proposed to extract the strokes from the global structures of the character. After each stroke of a character is extracted, the cross points can also be determined. Experimental results have shown that the proposed method is more effective than the other methods.^3,5–6