52,015 research outputs found

    Signed Network Modeling Based on Structural Balance Theory

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    The modeling of networks, specifically generative models, have been shown to provide a plethora of information about the underlying network structures, as well as many other benefits behind their construction. Recently there has been a considerable increase in interest for the better understanding and modeling of networks, but the vast majority of this work has been for unsigned networks. However, many networks can have positive and negative links(or signed networks), especially in online social media, and they inherently have properties not found in unsigned networks due to the added complexity. Specifically, the positive to negative link ratio and the distribution of signed triangles in the networks are properties that are unique to signed networks and would need to be explicitly modeled. This is because their underlying dynamics are not random, but controlled by social theories, such as Structural Balance Theory, which loosely states that users in social networks will prefer triadic relations that involve less tension. Therefore, we propose a model based on Structural Balance Theory and the unsigned Transitive Chung-Lu model for the modeling of signed networks. Our model introduces two parameters that are able to help maintain the positive link ratio and proportion of balanced triangles. Empirical experiments on three real-world signed networks demonstrate the importance of designing models specific to signed networks based on social theories to obtain better performance in maintaining signed network properties while generating synthetic networks.Comment: CIKM 2018: https://dl.acm.org/citation.cfm?id=327174

    Labour Administration Reforms in China

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    [Excerpt] This publication provides an explanation of the comprehensive labour administration system in China, including its recent advances, with emphasis on its public services functions, such as public employment, labour inspection and social insurance services. With the recent improvements to both the legal framework and the institutions of labour administration, it is believed that these public services will play bigger and more active roles in ensuring compliance with legislation and protecting the legitimate rights and interests of employers and workers alike

    Deep learning for time series classification: a review

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    Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover

    Clustering and Community Detection in Directed Networks: A Survey

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    Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed - in the sense that there is directionality on the edges, making the semantics of the edges non symmetric. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of applications. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs - with clustering being the primary method and tool for community detection and evaluation. The goal of this paper is to offer an in-depth review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms capitalize on. Then we present the relevant work along two orthogonal classifications. The first one is mostly concerned with the methodological principles of the clustering algorithms, while the second one approaches the methods from the viewpoint regarding the properties of a good cluster in a directed network. Further, we present methods and metrics for evaluating graph clustering results, demonstrate interesting application domains and provide promising future research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear

    CloudJet4BigData: Streamlining Big Data via an Accelerated Socket Interface

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    Big data needs to feed users with fresh processing results and cloud platforms can be used to speed up big data applications. This paper describes a new data communication protocol (CloudJet) for long distance and large volume big data accessing operations to alleviate the large latencies encountered in sharing big data resources in the clouds. It encapsulates a dynamic multi-stream/multi-path engine at the socket level, which conforms to Portable Operating System Interface (POSIX) and thereby can accelerate any POSIX-compatible applications across IP based networks. It was demonstrated that CloudJet accelerates typical big data applications such as very large database (VLDB), data mining, media streaming and office applications by up to tenfold in real-world tests
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