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As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain – deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.

Service components for managing the life-cycle of service compositionsYang, J.; Papazoglou, M. AbstractWeb services are becoming the prominent paradigm for distributed computing and electronic business. This has raised the opportunity for service providers and application developers to develop value-added services by combining existing web services. However, current web service composition solutions do not address software engineering principles for raising the level of abstraction in web-services by providing facilities for packaging, re-using, specializing and customizing service compositions.In this paper we propose the concept of service component that packages together complex services and presents their interfaces and operations in a consistent and uniform manner in the form of an abstract class definition. Service components are internally synthesized out of reused, specialized, or extended complex web services and just like normal web services are published and can thus be invoked by any service-based application. In addition, we present an integrated framework and prototype system that manage the entire life-cycle of service components ranging from abstract service component definition, scheduling, and construction to execution.

A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in network analysis. Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages in handling high dimensional network data. Hence, a comprehensive overview of community detection's latest progress through deep learning is timely to both academics and practitioners. This survey devises and proposes a new taxonomy covering different categories of the state-of-theart methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep sparse filtering. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders. The survey also summarizes the popular benchmark data sets, model evaluation metrics, and open-source implementations to address experimentation settings. We then discuss the practical applications of community detection in various domains and point to implementation scenarios. Finally, we outline future directions by suggesting challenging topics in this fast-growing deep learning field.

Abstract-Trust and reputation for web services emerges as an important research issue in web service selection. Current web service trust models either do not integrate different important sources of trust (subjective and objective for example), or do not focus on satisfying different user's requirements about different quality of service (QoS) attributes such as performance, availability etc. In this paper, we propose a Bayesian network trust and reputation model for web services that can overcome such limitations by considering several factors when assessing web services' trust: direct opinion from the truster, user rating (subjective view) and QoS monitoring information (objective view). Our comprehensive approach also addresses the problems of users' preferences and multiple QoSbased trust by specifying different conditions for the Bayesian network and targets at building a reasonable credibility model for the raters of web services.