从社会或商业平台等工业生态系统连续发出的数据通常表示为由多种节点/边缘类型组成的异质图（HG）。使用称为异质图神经网络（HGNN）的HGS的最先进的图形学习方法用于学习深层上下文信息形式表示。但是，来自工业应用程序的许多HG数据集都遭受节点类型之间的标签失衡。由于没有直接学习使用扎根于不同节点类型的标签的直接方法，因此HGNN仅应用于具有丰富标签的几个节点类型。我们为HGNN提出了一个称为知识转移网络（KTN）的零射击传输学习模块，该模块通过HG中给出的丰富关系信息将知识从标签的源节点类型转移到零标记的节点类型。 KTN源自我们在这项工作中引入的理论关系，在HGNN模型中给出的每个节点类型的不同特征提取器之间。 KTN将6种不同类型的HGNN模型的性能提高了960％，以推断零标记的节点类型，并且在HGS上的18个不同的转移学习任务中，最高的最先进的转移学习基线胜过最高的最高转移学习基线。

本文研究了跨网络节点分类的问题，以克服单个网络中标记的数据的不足。它旨在利用部分标记的源网络中的标签信息来帮助完全未标记或部分标记的目标网络中的节点分类。由于跨网络的域转移，现有的单网络学习方法无法解决此问题。一些多网络学习方法在很大程度上依赖于跨网络连接的存在，因此对于此问题是不适用的。为了解决这个问题，我们提出了一种小说\ textColor {black} {graph}通过利用对抗域的适应和图形卷积的技术来传递学习框架。它由两个组成部分组成：半监督的学习组件和一个对抗域的适应性组件。前者的目标是通过源网络和目标网络的给定标签信息学习类别的歧视节点表示，而后者则有助于减轻源和目标域之间的分布差异以促进知识传递。对现实世界数据集的广泛经验评估表明，ADAGCN可以在源网络上以低标签速率成功传输类信息，并且源和目标域之间的差异很大。复制实验结果的源代码可在https://github.com/daiquanyu/adagcn上获得。

Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorithms focus on IID tasks, where the source/target samples are assumed to be independent and identically distributed. Very little effort is devoted to theoretically studying the knowledge transferability on non-IID tasks, e.g., cross-network mining. To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph. The crucial idea is to characterize the cross-network knowledge transferability from the perspective of the Weisfeiler-Lehman graph isomorphism test. To this end, we propose a novel Graph Subtree Discrepancy to measure the graph distribution shift between source and target graphs. Then the generalization error bounds on cross-network transfer learning, including both cross-network node classification and link prediction tasks, can be derived in terms of the source knowledge and the Graph Subtree Discrepancy across domains. This thereby motivates us to propose a generic graph adaptive network (GRADE) to minimize the distribution shift between source and target graphs for cross-network transfer learning. Experimental results verify the effectiveness and efficiency of our GRADE framework on both cross-network node classification and cross-domain recommendation tasks.

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.

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.

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning researches, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey paper reviews more than forty representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments. The models are performed on three different datasets, i.e., Amazon Reviews, Reuters-21578, and Office-31. And the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned representations should also be discriminative in prediction. To learn such representations, domain adaptation frameworks usually include a domain invariant representation learning approach to measure and reduce the domain discrepancy, as well as a discriminator for classification. Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WD-GRL). WDGRL utilizes a neural network, denoted by the domain critic, to estimate empirical Wasserstein distance between the source and target samples and optimizes the feature extractor network to minimize the estimated Wasserstein distance in an adversarial manner. The theoretical advantages of Wasserstein distance for domain adaptation lie in its gradient property and promising generalization bound. Empirical studies on common sentiment and image classification adaptation datasets demonstrate that our proposed WDGRL outperforms the state-of-the-art domain invariant representation learning approaches.

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。

虽然在许多域内生成并提供了大量的未标记数据，但对视觉数据的自动理解的需求高于以往任何时候。大多数现有机器学习模型通常依赖于大量标记的训练数据来实现高性能。不幸的是，在现实世界的应用中，不能满足这种要求。标签的数量有限，手动注释数据昂贵且耗时。通常需要将知识从现有标记域传输到新域。但是，模型性能因域之间的差异（域移位或数据集偏差）而劣化。为了克服注释的负担，域适应（DA）旨在在将知识从一个域转移到另一个类似但不同的域中时减轻域移位问题。无监督的DA（UDA）处理标记的源域和未标记的目标域。 UDA的主要目标是减少标记的源数据和未标记的目标数据之间的域差异，并在培训期间在两个域中学习域不变的表示。在本文中，我们首先定义UDA问题。其次，我们从传统方法和基于深度学习的方法中概述了不同类别的UDA的最先进的方法。最后，我们收集常用的基准数据集和UDA最先进方法的报告结果对视觉识别问题。

Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially-costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions.

Deep domain adaptation has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature subspaces or reuse important source instances with shallow representations, deep domain adaptation methods leverage deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning. There have been comprehensive surveys for shallow domain adaptation, but few timely reviews the emerging deep learning based methods. In this paper, we provide a comprehensive survey of deep domain adaptation methods for computer vision applications with four major contributions. First, we present a taxonomy of different deep domain adaptation scenarios according to the properties of data that define how two domains are diverged. Second, we summarize deep domain adaptation approaches into several categories based on training loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vision applications that go beyond image classification, such as face recognition, semantic segmentation and object detection. Fourth, some potential deficiencies of current methods and several future directions are highlighted.

使用图形结构化网络的域适应性通过共享图形参数来学习标签 - 歧视和网络不变的节点嵌入。大多数现有作品都集中在域均匀网络的适应性上。研究异质案例的少数作品仅考虑共享节点类型，但会忽略单个网络中的私人节点类型。但是，对于给定的源和目标异质网络，它们通常包含共享和私有节点类型，其中私有类型为图形域适应带来了额外的挑战。在本文中，我们研究了具有共享和私有节点类型的异质信息网络（HINS），并提出了跨HINS（GDA-HIN）的广义域自适应模型，以处理它们之间的域移动。 GDA-HIN不仅可以使相同型节点和边缘的分布在两个呼吸中对齐，而且还可以充分利用不同型节点和边缘来提高知识传递的性能。在几个数据集上进行的广泛实验表明，GDA-HIN可以在各种域名网络的各种域适应任务中胜过最先进的方法。

我们考虑无监督的域适应性（UDA），其中使用来自源域（例如照片）的标记数据，而来自目标域（例如草图）的未标记数据用于学习目标域的分类器。常规的UDA方法（例如，域对抗训练）学习域不变特征，以改善对目标域的概括。在本文中，我们表明，对比的预训练，它在未标记的源和目标数据上学习功能，然后在标记的源数据上进行微调，具有强大的UDA方法的竞争力。但是，我们发现对比前训练不会学习域不变特征，这与常规的UDA直觉不同。从理论上讲，我们证明了对比的预训练可以学习在跨域下微调但仍通过解开域和类信息来概括到目标域的特征。我们的结果表明，UDA不需要域的不变性。我们从经验上验证了基准视觉数据集的理论。

无监督的域适应（UDA）显示出近年来工作条件下的轴承故障诊断的显着结果。但是，大多数UDA方法都不考虑数据的几何结构。此外，通常应用全局域适应技术，这忽略了子域之间的关系。本文通过呈现新的深亚域适应图卷积神经网络（DSAGCN）来解决提到的挑战，具有两个关键特性：首先，采用图形卷积神经网络（GCNN）来模拟数据结构。二，对抗域适应和局部最大平均差异（LMMD）方法同时应用，以对准子域的分布并降低相关子域和全局域之间的结构差异。 CWRU和Paderborn轴承数据集用于验证DSAGCN方法的比较模型之间的效率和优越性。实验结果表明，将结构化子域与域适应方法对准，以获得无监督故障诊断的准确数据驱动模型。

最近的智能故障诊断（IFD）的进展大大依赖于深度代表学习和大量标记数据。然而，机器通常以各种工作条件操作，或者目标任务具有不同的分布，其中包含用于训练的收集数据（域移位问题）。此外，目标域中的新收集的测试数据通常是未标记的，导致基于无监督的深度转移学习（基于UDTL为基础的）IFD问题。虽然它已经实现了巨大的发展，但标准和开放的源代码框架以及基于UDTL的IFD的比较研究尚未建立。在本文中，我们根据不同的任务，构建新的分类系统并对基于UDTL的IFD进行全面审查。对一些典型方法和数据集的比较分析显示了基于UDTL的IFD中的一些开放和基本问题，这很少研究，包括特征，骨干，负转移，物理前导等的可转移性，强调UDTL的重要性和再现性 - 基于IFD，整个测试框架将发布给研究界以促进未来的研究。总之，发布的框架和比较研究可以作为扩展界面和基本结果，以便对基于UDTL的IFD进行新的研究。代码框架可用于\ url {https:/github.com/zhaozhibin/udtl}。

Given a resource-rich source graph and a resource-scarce target graph, how can we effectively transfer knowledge across graphs and ensure a good generalization performance? In many high-impact domains (e.g., brain networks and molecular graphs), collecting and annotating data is prohibitively expensive and time-consuming, which makes domain adaptation an attractive option to alleviate the label scarcity issue. In light of this, the state-of-the-art methods focus on deriving domain-invariant graph representation that minimizes the domain discrepancy. However, it has recently been shown that a small domain discrepancy loss may not always guarantee a good generalization performance, especially in the presence of disparate graph structures and label distribution shifts. In this paper, we present TRANSNET, a generic learning framework for augmenting knowledge transfer across graphs. In particular, we introduce a novel notion named trinity signal that can naturally formulate various graph signals at different granularity (e.g., node attributes, edges, and subgraphs). With that, we further propose a domain unification module together with a trinity-signal mixup scheme to jointly minimize the domain discrepancy and augment the knowledge transfer across graphs. Finally, comprehensive empirical results show that TRANSNET outperforms all existing approaches on seven benchmark datasets by a significant margin.

异质图神经网络（GNNS）在半监督学习设置中在节点分类任务上实现了强大的性能。但是，与更简单的GNN案例一样，基于消息的异质GNN可能难以在抵抗深模型中发生的过度厚度与捕获长期依赖关系图表结构化数据之间进行平衡。此外，由于不同类型的节点之间的异质关系，这种权衡的复杂性在异质图中复杂化。为了解决这些问题，我们提出了一种新型的异质GNN结构，其中层来自降低新型关系能量函数的优化步骤。相应的最小化器相对于能量函数参数是完全可区分的，因此可以应用双光线优化来有效地学习功能形式，其最小值为后续分类任务提供了最佳节点表示。特别是，这种方法使我们能够在不同的节点类型之间建模各种杂质关系，同时避免过度平滑效果。 8个异质图基准的实验结果表明，我们提出的方法可以达到竞争性节点分类的精度。

语义分割在广泛的计算机视觉应用中起着基本作用，提供了全球对图像​​的理解的关键信息。然而，最先进的模型依赖于大量的注释样本，其比在诸如图像分类的任务中获得更昂贵的昂贵的样本。由于未标记的数据替代地获得更便宜，因此无监督的域适应达到了语义分割社区的广泛成功并不令人惊讶。本调查致力于总结这一令人难以置信的快速增长的领域的五年，这包含了语义细分本身的重要性，以及将分段模型适应新环境的关键需求。我们提出了最重要的语义分割方法;我们对语义分割的域适应技术提供了全面的调查;我们揭示了多域学习，域泛化，测试时间适应或无源域适应等较新的趋势;我们通过描述在语义细分研究中最广泛使用的数据集和基准测试来结束本调查。我们希望本调查将在学术界和工业中提供具有全面参考指导的研究人员，并有助于他们培养现场的新研究方向。

当机器学习模型将其应用于与最初训练的数据相似但不同的域中的数据时，它的性能会降低。为了减轻此域移位问题，域Adaptation（DA）技术搜索了最佳转换，该转换将（当前）输入数据从源域转换为目标域，以学习域名不变的表示，以减少域差异。本文根据两个步骤提出了一个新颖的监督DA。首先，我们从几个样本中搜索从源到目标域的最佳类依赖性转换。我们考虑了最佳的运输方法，例如地球搬运工的距离，凹痕传输和相关对准。其次，我们使用嵌入相似技术在推理时选择相应的转换。我们使用相关指标和高阶矩匹配技术。我们对具有域移动的时间序列数据集进行了广泛的评估，包括模拟和各种在线手写数据集，以演示性能。

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]。