过去,现实世界中社交网络的图表错过了两个重要元素:连接的多重性和表示时间。为此,在本文中,我们为社交网络提供了一个新的动态异质图表示,其中包括图形的每个组件中的时间,即节点和边缘,每种捕获异质性的不同类型。我们通过提出四个与时间有关的查询和深度学习问题来说明这种表示的力量,这些查询和深度学习问题无法轻易在常规的均匀图表中处理。作为概念的证明,我们介绍了新的社交媒体平台(Steemit)的详细表示,我们用它来说明动态查询功能以及使用图形神经网络(GNNS)的预测任务。结果说明了动态异质图表示对社交网络的模型的力量。鉴于这是一个相对研究的领域,我们还说明了在查询优化方面的未来工作以及异质图结构的新动态预测任务的机会。
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Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field's youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
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保持个人特征和复杂的关系,广泛利用和研究了图表数据。通过更新和聚合节点的表示,能够捕获结构信息,图形神经网络(GNN)模型正在获得普及。在财务背景下,该图是基于实际数据构建的,这导致复杂的图形结构,因此需要复杂的方法。在这项工作中,我们在最近的财务环境中对GNN模型进行了全面的审查。我们首先将普通使用的财务图分类并总结每个节点的功能处理步骤。然后,我们总结了每个地图类型的GNN方法,每个区域的应用,并提出一些潜在的研究领域。
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Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.
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异质图卷积网络在解决异质网络数据的各种网络分析任务方面已广受欢迎,从链接预测到节点分类。但是,大多数现有作品都忽略了多型节点之间的多重网络的关系异质性,而在元路径中,元素嵌入中关系的重要性不同,这几乎无法捕获不同关系跨不同关系的异质结构信号。为了应对这一挑战,这项工作提出了用于异质网络嵌入的多重异质图卷积网络(MHGCN)。我们的MHGCN可以通过多层卷积聚合自动学习多重异质网络中不同长度的有用的异质元路径相互作用。此外,我们有效地将多相关结构信号和属性语义集成到学习的节点嵌入中,并具有无监督和精选的学习范式。在具有各种网络分析任务的五个现实世界数据集上进行的广泛实验表明,根据所有评估指标,MHGCN与最先进的嵌入基线的优势。
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本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片(Sota)。它填补了空白,因为现有的调查文章在其范围内或被约会。我们包括两个重要方面,目前正在挖掘和建模社交媒体的重要性:动态和网络。社会动态对于了解影响影响或疾病的传播,友谊的形成,友谊的形成等,另一方面,可以捕获各种复杂关系,提供额外的洞察力和识别否则将不会被注意的重要模式。
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大多数人类活动都需要在正式或非正式团队内部和跨部队进行合作。我们对团队所花费的合作努力与他们的表现有何关系的理解仍然是一个辩论问题。团队合作导致了一个高度相互联系的生态系统,这些生态系统可能是重叠的组件,其中与团队成员和其他团队进行互动执行任务。为了解决这个问题,我们提出了一个图形神经网络模型,旨在预测团队的性能,同时确定确定这种结果的驱动程序。特别是,该模型基于三个架构渠道:拓扑,中心性和上下文,它们捕获了不同因素可能塑造了团队的成功。我们赋予该模型具有两种注意机制,以提高模型性能并允许解释性。第一种机制允许查明团队内部的关键成员。第二种机制使我们能够量化三个驱动程序在确定结果绩效方面的贡献。我们在广泛的域上测试模型性能,其表现优于所考虑的大多数经典和神经基准。此外,我们包括专门设计的合成数据集,以验证该模型如何删除我们的模型胜过基线的预期属性。
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Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks like, node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network (GCN), graph attention network (GAT), graph autoencoder (GAE), and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field.
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时间图代表实体之间的动态关系,并发生在许多现实生活中的应用中,例如社交网络,电子商务,通信,道路网络,生物系统等。他们需要根据其生成建模和表示学习的研究超出与静态图有关的研究。在这项调查中,我们全面回顾了近期针对处理时间图提出的神经时间依赖图表的学习和生成建模方法。最后,我们确定了现有方法的弱点,并讨论了我们最近发表的论文提格的研究建议[24]。
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Fake news detection has become a research area that goes way beyond a purely academic interest as it has direct implications on our society as a whole. Recent advances have primarily focused on textbased approaches. However, it has become clear that to be effective one needs to incorporate additional, contextual information such as spreading behaviour of news articles and user interaction patterns on social media. We propose to construct heterogeneous social context graphs around news articles and reformulate the problem as a graph classification task. Exploring the incorporation of different types of information (to get an idea as to what level of social context is most effective) and using different graph neural network architectures indicates that this approach is highly effective with robust results on a common benchmark dataset.
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最近,图神经网络显示了建模基于网络的推荐系统中复杂拓扑结构的优势。由于节点之间的各种相互作用以及来自各种类型的节点和边缘的大量语义,因此在多重异质网络中学习表达性节点表示的研究兴趣爆发。推荐系统中最重要的任务之一是预测特定边缘类型下两个节点之间的潜在连接(即关系)。尽管现有的研究利用明确的元数据来汇总邻居,但实际上,它们仅考虑了关系内部的元数据,因此无法通过相互关联信息来利用潜在的提升。此外,在各种关系下,尤其是在越来越多的节点和边缘类型的情况下,全面利用相互关系的元数据并不总是直接的。此外,两个节点之间不同关系的贡献很难衡量。为了应对挑战,我们提出了Hybridgnn,这是一种具有混合聚集流和分层的端到端GNN模型,以在多路复用方案中充分利用异质性。具体而言,Hybridgnn应用了一个随机的关系探索模块来利用不同关系之间的多重性属性。然后,我们的模型利用在关系内的元数据和随机探索下的混合聚集流以学习丰富的语义。为了探索不同聚合流的重要性并利用多重性属性,我们提出了一个新型的分层注意模块,该模块既利用了Metapath级别的注意力和关系级的关注。广泛的实验结果表明,与几个最先进的基线相比,Hybridgnn取得了最佳性能。
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Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.
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在社交媒体上传播谣言对社会构成了重要威胁,因此最近提出了各种谣言检测技术。然而,现有的工作重点是\ emph {what}实体构成谣言,但几乎没有支持理解\ emph {为什么}实体已被归类为这样。这样可以防止对检测的谣言以及对策设计的有效评估。在这项工作中,我们认为,可以通过过去检测到的相关谣言的例子来给出检测到的谣言的解释。一系列类似的谣言有助于用户概括,即了解控制谣言的探测的特性。由于通常使用特征声明的图表对社交媒体的谣言传播通常是建模的,因此我们提出了一种逐个示例的方法,鉴于谣言图,它从过去的谣言中提取了$ k $最相似和最多的子图。挑战是所有计算都需要快速评估图之间的相似性。为了在流式设置中实现该方法的有效和适应性实现,我们提出了一种新颖的图表学习技术,并报告了实施注意事项。我们的评估实验表明,我们的方法在为各种谣言传播行为提供有意义的解释方面优于基线技术。
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预期观众对某些文本的反应是社会的几个方面不可或缺的,包括政治,研究和商业行业。情感分析(SA)是一种有用的自然语言处理(NLP)技术,它利用词汇/统计和深度学习方法来确定不同尺寸的文本是否表现出正面,负面或中立的情绪。但是,目前缺乏工具来分析独立文本的组并从整体中提取主要情感。因此,当前的论文提出了一种新型算法,称为多层推文分析仪(MLTA),该算法使用多层网络(MLN)以图形方式对社交媒体文本进行了图形方式,以便更好地编码跨越独立的推文集的关系。与其他表示方法相比,图结构能够捕获复杂生态系统中有意义的关系。最先进的图形神经网络(GNN)用于从Tweet-MLN中提取信息,并根据提取的图形特征进行预测。结果表明,与标准的正面,负或中性相比,MLTA不仅可以从更大的可能情绪中预测,从而提供了更准确的情感,还允许对Twitter数据进行准确的组级预测。
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Graph learning is a popular approach for performing machine learning on graph-structured data. It has revolutionized the machine learning ability to model graph data to address downstream tasks. Its application is wide due to the availability of graph data ranging from all types of networks to information systems. Most graph learning methods assume that the graph is static and its complete structure is known during training. This limits their applicability since they cannot be applied to problems where the underlying graph grows over time and/or new tasks emerge incrementally. Such applications require a lifelong learning approach that can learn the graph continuously and accommodate new information whilst retaining previously learned knowledge. Lifelong learning methods that enable continuous learning in regular domains like images and text cannot be directly applied to continuously evolving graph data, due to its irregular structure. As a result, graph lifelong learning is gaining attention from the research community. This survey paper provides a comprehensive overview of recent advancements in graph lifelong learning, including the categorization of existing methods, and the discussions of potential applications and open research problems.
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Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types, making them infeasible to represent heterogeneous structures. In this paper, we present the Heterogeneous Graph Transformer (HGT) architecture for modeling Web-scale heterogeneous graphs. To model heterogeneity, we design node-and edge-type dependent parameters to characterize the heterogeneous attention over each edge, empowering HGT to maintain dedicated representations for different types of nodes and edges. To handle dynamic heterogeneous graphs, we introduce the relative temporal encoding technique into HGT, which is able to capture the dynamic structural dependency with arbitrary durations. To handle Web-scale graph data, we design the heterogeneous mini-batch graph sampling algorithm-HGSampling-for efficient and scalable training. Extensive experiments on the Open Academic Graph of 179 million nodes and 2 billion edges show that the proposed HGT model consistently outperforms all the state-of-the-art GNN baselines by 9%-21% on various downstream tasks. The dataset and source code of HGT are publicly available at https://github.com/acbull/pyHGT.
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社交机器人被称为社交网络上的自动帐户,这些帐户试图像人类一样行事。尽管图形神经网络(GNNS)已大量应用于社会机器人检测领域,但大量的领域专业知识和先验知识大量参与了最先进的方法,以设计专门的神经网络体系结构,以设计特定的神经网络体系结构。分类任务。但是,在模型设计中涉及超大的节点和网络层,通常会导致过度平滑的问题和缺乏嵌入歧视。在本文中,我们提出了罗斯加斯(Rosgas),这是一种新颖的加强和自我监督的GNN Architecture搜索框架,以适应性地指出了最合适的多跳跃社区和GNN体系结构中的层数。更具体地说,我们将社交机器人检测问题视为以用户为中心的子图嵌入和分类任务。我们利用异构信息网络来通过利用帐户元数据,关系,行为特征和内容功能来展示用户连接。 Rosgas使用多代理的深钢筋学习(RL)机制来导航最佳邻域和网络层的搜索,以分别学习每个目标用户的子图嵌入。开发了一种用于加速RL训练过程的最接近的邻居机制,Rosgas可以借助自我监督的学习来学习更多的判别子图。 5个Twitter数据集的实验表明,Rosgas在准确性,训练效率和稳定性方面优于最先进的方法,并且在处理看不见的样本时具有更好的概括。
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尽管与以太坊这样的加密货币交易变得越来越普遍,但欺诈和其他犯罪交易并不少见。图分析算法和机器学习技术检测到导致大型交易网络网络钓鱼的可疑交易。已经提出了许多图形神经网络(GNN)模型将深度学习技术应用于图形结构。尽管在以太坊交易网络中使用GNN模型进行了网络钓鱼检测的研究,但尚未研究针对顶点和边缘数量的规模以及标签不平衡的模型。在本文中,我们比较了GNN模型在实际以太坊交易网络数据集和网络钓鱼报告的标签数据上的模型性能,以详尽地比较和验证哪些GNN模型和超参数产生最佳精度。具体而言,我们评估了代表性同质GNN模型的模型性能,该模型考虑了单型节点和边缘以及支持不同类型的节点和边缘的异质GNN模型。我们表明,异质模型比同质模型具有更好的模型性能。特别是,RGCN模型在整体指标中取得了最佳性能。
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为了解决疫苗犹豫不决,这会损害COVID-19疫苗接种运动的努力,必须了解公共疫苗接种态度并及时掌握其变化。尽管具有可靠性和可信赖性,但基于调查的传统态度收集是耗时且昂贵的,无法遵循疫苗接种态度的快速发展。我们利用社交媒体上的文本帖子通过提出深入学习框架来实时提取和跟踪用户的疫苗接种立场。为了解决与疫苗相关话语中常用的讽刺和讽刺性的语言特征的影响,我们将用户社交网络邻居的最新帖子集成到框架中,以帮助检测用户的真实态度。根据我们从Twitter的注释数据集,与最新的仅文本模型相比,从我们框架实例化的模型可以提高态度提取的性能高达23%。使用此框架,我们成功地验证了使用社交媒体跟踪现实生活中疫苗接种态度的演变的可行性。我们进一步显示了对我们的框架的一种实际用途,它可以通过从社交媒体中感知到的信息来预测用户疫苗犹豫的变化的可能性。
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检测假新闻对于确保信息的真实性和维持新闻生态系统的可靠性至关重要。最近,由于最近的社交媒体和伪造的内容生成技术(例如Deep Fake)的扩散,假新闻内容的增加了。假新闻检测的大多数现有方式都集中在基于内容的方法上。但是,这些技术中的大多数无法处理生成模型生产的超现实合成媒体。我们最近的研究发现,真实和虚假新闻的传播特征是可以区分的,无论其方式如何。在这方面,我们已经根据社会环境调查了辅助信息,以检测假新闻。本文通过基于混合图神经网络的方法分析了假新闻检测的社会背景。该混合模型基于将图形神经网络集成到新闻内容上的新闻和BI定向编码器表示的传播中,以了解文本功能。因此,这种提出的方​​法可以学习内容以及上下文特征,因此能够在Politifact上以F1分别为0.91和0.93的基线模型和八西八角数据集的基线模型,分别超过了基线模型,分别在八西八学数据集中胜过0.93
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