Social network analysis faces profound difficulties in sharing data between researchers due to privacy and security concerns. A potential remedy to this issue are synthetic networks, that closely resemble their real counterparts, but can be freely distributed. generating synthetic networks requires the creation of network topologies that, in application, function as realistically as possible. Widely applied models are currently rule-based and can struggle to reproduce structural dynamics. Lead by recent developments in Graph Neural Network (GNN) models for network generation we evaluate the potential of GNNs for synthetic social networks. Our GNN use is specifically within a reasonable use-case and includes empirical evaluation using Maximum Mean Discrepancy (MMD). We include social network specific measurements which allow evaluation of how realistically synthetic networks behave in typical social network analysis applications. We find that the Gated Recurrent Attention Network (GRAN) extends well to social networks, and in comparison to a benchmark popular rule-based generation Recursive-MATrix (R-MAT) method, is better able to replicate realistic structural dynamics. We find that GRAN is more computationally costly than R-MAT, but is not excessively costly to employ, so would be effective for researchers seeking to create datasets of synthetic social networks.
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Pre-publication draft of a book to be published byMorgan & Claypool publishers. Unedited version released with permission. All relevant copyrights held by the author and publisher extend to this pre-publication draft.
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我们从光谱的角度解决图形生成问题,首先生成图形laplacian光谱的主要部分,然后构建与这些特征值和特征向量相匹配的图。光谱调节允许直接建模全局和局部图结构,并有助于克服单发图生成器的表达性和模式崩溃问题。我们的新颖的甘(Spectre)称为Spectre,可以使用一声模型来产生比以前可能更大的图。Spectre的表现优于最先进的深度自动回归发电机在建模忠诚方面,同时还避免了昂贵的顺序产生和对节点排序的依赖。一个很好的例子,在相当大的合成和现实图形中,Specter的幽灵比最佳竞争对手的最佳竞争对手的改进是4到170倍,该竞争对手不合适,比自回旋发电机快23至30倍。
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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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Graph classification is an important area in both modern research and industry. Multiple applications, especially in chemistry and novel drug discovery, encourage rapid development of machine learning models in this area. To keep up with the pace of new research, proper experimental design, fair evaluation, and independent benchmarks are essential. Design of strong baselines is an indispensable element of such works. In this thesis, we explore multiple approaches to graph classification. We focus on Graph Neural Networks (GNNs), which emerged as a de facto standard deep learning technique for graph representation learning. Classical approaches, such as graph descriptors and molecular fingerprints, are also addressed. We design fair evaluation experimental protocol and choose proper datasets collection. This allows us to perform numerous experiments and rigorously analyze modern approaches. We arrive to many conclusions, which shed new light on performance and quality of novel algorithms. We investigate application of Jumping Knowledge GNN architecture to graph classification, which proves to be an efficient tool for improving base graph neural network architectures. Multiple improvements to baseline models are also proposed and experimentally verified, which constitutes an important contribution to the field of fair model comparison.
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在过去十年中,图形内核引起了很多关注,并在结构化数据上发展成为一种快速发展的学习分支。在过去的20年中,该领域发生的相当大的研究活动导致开发数十个图形内核,每个图形内核都对焦于图形的特定结构性质。图形内核已成功地成功地在广泛的域中,从社交网络到生物信息学。本调查的目标是提供图形内核的文献的统一视图。特别是,我们概述了各种图形内核。此外,我们对公共数据集的几个内核进行了实验评估,并提供了比较研究。最后,我们讨论图形内核的关键应用,并概述了一些仍有待解决的挑战。
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图形嵌入是图形节点到一组向量的转换。良好的嵌入应捕获图形拓扑,节点与节点的关系以及有关图,其子图和节点的其他相关信息。如果实现了这些目标,则嵌入是网络中有意义的,可理解的,可理解的压缩表示形式,可用于其他机器学习工具,例如节点分类,社区检测或链接预测。主要的挑战是,需要确保嵌入很好地描述图形的属性。结果,选择最佳嵌入是一项具有挑战性的任务,并且通常需要领域专家。在本文中,我们在现实世界网络和人为生成的网络上进行了一系列广泛的实验,并使用选定的图嵌入算法进行了一系列的实验。根据这些实验,我们制定了两个一般结论。首先,如果需要在运行实验之前选择一种嵌入算法,则Node2Vec是最佳选择,因为它在我们的测试中表现最好。话虽如此,在所有测试中都没有单一的赢家,此外,大多数嵌入算法都具有应该调整并随机分配的超参数。因此,如果可能的话,我们对从业者的主要建议是生成几个问题的嵌入,然后使用一个通用框架,该框架为无监督的图形嵌入比较提供了工具。该框架(最近在文献中引入并在GitHub存储库中很容易获得)将分歧分数分配给嵌入,以帮助区分好的分数和不良的分数。
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Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due to the discrete and high-dimensional nature of graphs, as well as permutation invariance to node orderings in underlying graph distributions. Current leading autoregressive models fail to capture the permutation invariance nature of graphs for the reliance on generation ordering and have high time complexity. Here, we propose a continuous-time generative diffusion process for permutation invariant graph generation to mitigate these issues. Specifically, we first construct a forward diffusion process defined by a stochastic differential equation (SDE), which smoothly converts graphs within the complex distribution to random graphs that follow a known edge probability. Solving the corresponding reverse-time SDE, graphs can be generated from newly sampled random graphs. To facilitate the reverse-time SDE, we newly design a position-enhanced graph score network, capturing the evolving structure and position information from perturbed graphs for permutation equivariant score estimation. Under the evaluation of comprehensive metrics, our proposed generative diffusion process achieves competitive performance in graph distribution learning. Experimental results also show that GraphGDP can generate high-quality graphs in only 24 function evaluations, much faster than previous autoregressive models.
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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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已经为图形生成模型提出了广泛的模型,需要采用有效的方法来评估其质量。到目前为止,大多数技术都使用基于子图计数的传统指标或随机初始化的图形神经网络(GNN)的表示。我们建议使用对比训练的GNN而不是随机GNN的表示形式,并表明这给出了更可靠的评估指标。但是,传统方法和基于GNN的方法都没有主导另一方:我们举例说明每种方法无法区分的示例。我们证明了图形子结构网络(GSN),以一种结合两种方法的方式,可以更好地区分图形数据集之间的距离。
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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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图形神经网络(GNNS)通过考虑其内在的几何形状来扩展神经网络的成功到图形结构化数据。尽管根据图表学习基准的集合,已经对开发具有卓越性能的GNN模型进行了广泛的研究,但目前尚不清楚其探测给定模型的哪些方面。例如,他们在多大程度上测试模型利用图形结构与节点特征的能力?在这里,我们开发了一种原则性的方法来根据$ \ textit {敏感性配置文件} $进行基准测试数据集,该方法基于由于图形扰动的集合而导致的GNN性能变化了多少。我们的数据驱动分析提供了对GNN利用哪些基准测试数据特性的更深入的了解。因此,我们的分类法可以帮助选择和开发适当的图基准测试,并更好地评估未来的GNN方法。最后,我们在$ \ texttt {gtaxogym} $软件包中的方法和实现可扩展到多个图形预测任务类型和未来数据集。
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最近有一项激烈的活动在嵌入非常高维和非线性数据结构的嵌入中,其中大部分在数据科学和机器学习文献中。我们分四部分调查这项活动。在第一部分中,我们涵盖了非线性方法,例如主曲线,多维缩放,局部线性方法,ISOMAP,基于图形的方法和扩散映射,基于内核的方法和随机投影。第二部分与拓扑嵌入方法有关,特别是将拓扑特性映射到持久图和映射器算法中。具有巨大增长的另一种类型的数据集是非常高维网络数据。第三部分中考虑的任务是如何将此类数据嵌入中等维度的向量空间中,以使数据适合传统技术,例如群集和分类技术。可以说,这是算法机器学习方法与统计建模(所谓的随机块建模)之间的对比度。在论文中,我们讨论了两种方法的利弊。调查的最后一部分涉及嵌入$ \ mathbb {r}^ 2 $,即可视化中。提出了三种方法:基于第一部分,第二和第三部分中的方法,$ t $ -sne,UMAP和大节。在两个模拟数据集上进行了说明和比较。一个由嘈杂的ranunculoid曲线组成的三胞胎,另一个由随机块模型和两种类型的节点产生的复杂性的网络组成。
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在本文中,我们研究了具有内置社区结构的合成随机图模型的属性和性能。这样的模型对于评估和调整自然界无监督的社区检测算法很重要。我们提出了ABCDE,这是ABCD的多线程实现(社区检测的人工基准)图生成器。我们讨论了该算法的实现详细信息,并将其与ABCD模型的先前可用顺序版本以及标准和广泛使用的LFR(Lancichinetti-Fortunato-Radicchi)发电机进行了比较。我们表明,ABCDE的速度比NetworkIT中提供的LFR的并行实现要快十倍,并且比例比缩放更好。此外,该算法不仅更快,而且ABCD生成的随机图具有与原始LFR算法生成的属性相似的属性,而LFR的并行网络实现LFR会产生具有明显不同特征的图形。
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图表表示学习是一种快速增长的领域,其中一个主要目标是在低维空间中产生有意义的图形表示。已经成功地应用了学习的嵌入式来执行各种预测任务,例如链路预测,节点分类,群集和可视化。图表社区的集体努力提供了数百种方法,但在所有评估指标下没有单一方法擅长,例如预测准确性,运行时间,可扩展性等。该调查旨在通过考虑算法来评估嵌入方法的所有主要类别的图表变体,参数选择,可伸缩性,硬件和软件平台,下游ML任务和多样化数据集。我们使用包含手动特征工程,矩阵分解,浅神经网络和深图卷积网络的分类法组织了图形嵌入技术。我们使用广泛使用的基准图表评估了节点分类,链路预测,群集和可视化任务的这些类别算法。我们在Pytorch几何和DGL库上设计了我们的实验,并在不同的多核CPU和GPU平台上运行实验。我们严格地审查了各种性能指标下嵌入方法的性能,并总结了结果。因此,本文可以作为比较指南,以帮助用户选择最适合其任务的方法。
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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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社区检测是网络科学中最重要的方法领域之一,在过去的几十年里引起了大量关注的方法之一。该区域处理网络的自动部门到基础构建块中,目的是提供其大规模结构的概要。尽管它的重要性和广泛的采用普及,所谓的最先进和实际在各种领域实际使用的方法之间存在明显的差距。在这里,我们试图通过根据是否具有“描述性”或“推论”目标来划分现有方法来解决这种差异。虽然描述性方法在基于社区结构的直观概念的网络中找到模式的模式,但是推理方法阐述了精确的生成模型,并尝试将其符合数据。通过这种方式,他们能够为网络形成机制提供见解,并以统计证据支持的方式与随机性的单独结构。我们审查如何使用推论目标采用描述性方法被陷入困境和误导性答案,因此应该一般而言。我们认为推理方法更通常与更清晰的科学问题一致,产生更强大的结果,并且应该是一般的首选。我们试图消除一些神话和半真半假在实践中使用社区检测时,努力改善这些方法的使用以及对结果的解释。
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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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Denoising diffusion probabilistic models and score matching models have proven to be very powerful for generative tasks. While these approaches have also been applied to the generation of discrete graphs, they have, so far, relied on continuous Gaussian perturbations. Instead, in this work, we suggest using discrete noise for the forward Markov process. This ensures that in every intermediate step the graph remains discrete. Compared to the previous approach, our experimental results on four datasets and multiple architectures show that using a discrete noising process results in higher quality generated samples indicated with an average MMDs reduced by a factor of 1.5. Furthermore, the number of denoising steps is reduced from 1000 to 32 steps leading to a 30 times faster sampling procedure.
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尽管人们对叛乱团体结构对冲突结果的重要性越来越多,但那里的实证研究很少。尽管此问题源于激进群体结构的数据的无法获取性,但叛乱分子经常在Internet上发布大量图像数据。在本文中,我开发了一种新的方法,该方法通过自动化基于在照片中使用深度学习的照片中的共同出现的社交网络图来自动创建社交网络图,从而利用了这种丰富但未充分利用的数据来源。我使用库尔德人武装组织在土耳其的库尔德人激进组织在线发布的1915张ob告图像,我证明了个人在由此产生的共同出现网络中的中心地位与他们在叛乱组织中的排名密切相关。
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