Graph相关(图学习|图神经网络|图优化等)(12篇)
[ 1 ] Hybrid Graph Embedding Techniques in Estimated Time of Arrival Task
标题：估计到达时间任务中的混合图嵌入技术
链接：https://arxiv.org/abs/2110.04228
作者：Vadim Porvatov,Natalia Semenova,Andrey Chertok
机构： Sberbank, Moscow , Russia, National University of Science and Technology “MISIS”, Moscow , Russia, Artificial Intelligence Research Institute (AIRI)
备注：Accepted in ICCNA 2021
摘要：最近，深度学习在计算估计到达时间（ETA）方面取得了令人满意的结果，ETA被认为是预测从起点到给定路径上某个位置的旅行时间。ETA在智能出租车服务或汽车导航系统中起着至关重要的作用。通常的做法是使用嵌入向量来表示道路网络的元素，例如路段和十字路口。道路要素有其自身的属性，如长度、人行横道的存在、车道数等。然而，道路网络中的许多路段被过少的浮动车穿过，即使是在大型叫车平台上，也会受到各种时间事件的影响。作为本研究的主要目的，我们探讨了不同空间嵌入策略的泛化能力，并提出了一种两阶段的方法来处理这些问题。
摘要：Recently, deep learning has achieved promising results in the calculation of Estimated Time of Arrival (ETA), which is considered as predicting the travel time from the start point to a certain place along a given path. ETA plays an essential role in intelligent taxi services or automotive navigation systems. A common practice is to use embedding vectors to represent the elements of a road network, such as road segments and crossroads. Road elements have their own attributes like length, presence of crosswalks, lanes number, etc. However, many links in the road network are traversed by too few floating cars even in large ride-hailing platforms and affected by the wide range of temporal events. As the primary goal of the research, we explore the generalization ability of different spatial embedding strategies and propose a two-stage approach to deal with such problems.
[ 2 ] TopoDetect: Framework for Topological Features Detection in Graph Embeddings
标题：拓扑检测：图嵌入中的拓扑特征检测框架
链接：https://arxiv.org/abs/2110.04173
作者：Maroun Haddad,Mohamed Bouguessa
机构：Department of Computer Science, University of Quebec at Montreal, Montreal, Quebec, Canada
摘要：TopoDetect是一个Python包，它允许用户调查是否在图形表示模型的嵌入中保留了重要的拓扑特征，例如节点的度、节点的三角形计数或节点的局部聚类分数。此外，该框架还可以根据节点之间拓扑特征的分布来可视化嵌入。此外，TopoDetect使我们能够通过评估嵌入对下游学习任务（如聚类和分类）的性能来研究保留这些特征的效果。
摘要：TopoDetect is a Python package that allows the user to investigate if important topological features, such as the Degree of the nodes, their Triangle Count, or their Local Clustering Score, are preserved in the embeddings of graph representation models. Additionally, the framework enables the visualization of the embeddings according to the distribution of the topological features among the nodes. Moreover, TopoDetect enables us to study the effect of the preservation of these features by evaluating the performance of the embeddings on downstream learning tasks such as clustering and classification.
[ 3 ] New Insights into Graph Convolutional Networks using Neural Tangent Kernels
标题：利用神经切核对图卷积网络的新认识
链接：https://arxiv.org/abs/2110.04060
作者：Mahalakshmi Sabanayagam,Pascal Esser,Debarghya Ghoshdastidar
机构：Technical University of Munich
摘要：图卷积网络（GCN）已成为学习网络结构化数据的有力工具。尽管在经验上取得了成功，但GCN表现出某些没有严格解释的行为——例如，GCN的性能随着网络深度的增加而显著下降，而随着使用跳过连接的深度的增加而略有改善。本文重点介绍了NTKs的半监督图和切线学习。我们推导出对应于无限宽GCN的NTK（有和没有跳过连接）。随后，我们使用导出的NTK来确定，通过适当的归一化，网络深度并不总是显著降低GCN的性能——我们还通过大量模拟验证了这一事实。此外，我们建议NTK作为GCN的有效“代理模型”，它不会因超参数调整而受到性能波动的影响，因为它是一个超参数自由确定性内核。通过使用替代NTK对GCN的不同跳过连接进行比较，证明了该想法的有效性。
摘要：Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structured data. Although empirically successful, GCNs exhibit certain behaviour that has no rigorous explanation -- for instance, the performance of GCNs significantly degrades with increasing network depth, whereas it improves marginally with depth using skip connections. This paper focuses on semi-supervised learning on graphs, and explains the above observations through the lens of Neural Tangent Kernels (NTKs). We derive NTKs corresponding to infinitely wide GCNs (with and without skip connections). Subsequently, we use the derived NTKs to identify that, with suitable normalisation, network depth does not always drastically reduce the performance of GCNs -- a fact that we also validate through extensive simulation. Furthermore, we propose NTK as an efficient `surrogate model' for GCNs that does not suffer from performance fluctuations due to hyper-parameter tuning since it is a hyper-parameter free deterministic kernel. The efficacy of this idea is demonstrated through a comparison of different skip connections for GCNs using the surrogate NTKs.
[ 4 ] Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks
标题：基于分层图神经网络的全局上下文增强型社交推荐
链接：https://arxiv.org/abs/2110.04039
作者：Huance Xu,Chao Huang,Yong Xu,Lianghao Xia,Hao Xing,Dawei Yin
机构：†South China University of Technology, §JD Finance America Corporation, ‡VIPS Research, ♮Baidu inc
备注：Published as a full paper at ICDM 2020
摘要：社交推荐旨在利用用户之间的社交关系来提高推荐性能。随着深度学习技术的复兴，人们致力于开发各种基于神经网络的社会推荐系统，如注意机制和基于图形的消息传递框架。然而，有两个重要的挑战尚未得到很好的解决：（i）大多数现有的社会推荐模型未能充分探讨多类型用户项目交互行为以及潜在的跨关系相互依赖性。（ii）虽然学习到的社会状态向量能够建模成对的用户依赖关系，但它在跨用户捕获全局社会上下文方面的表示能力仍然有限。为了解决这些局限性，我们提出了一种新的基于层次图神经网络的社会推荐框架（SR-HGNN）。特别是，我们首先设计了一个关系感知的重构图神经网络，将跨类型协作语义注入到推荐框架中。此外，我们还基于低层用户嵌入和高层全局表示之间的互信息学习范式，进一步将SR-HGNN扩展为一个社会关系编码器，从而赋予SR-HGNN捕获全局社会上下文信号的能力。三个公共基准上的实证结果表明，SR-HGNN显著优于最先进的推荐方法。源代码可从以下网址获得：https://github.com/xhcdream/SR-HGNN.
摘要：Social recommendation which aims to leverage social connections among users to enhance the recommendation performance. With the revival of deep learning techniques, many efforts have been devoted to developing various neural network-based social recommender systems, such as attention mechanisms and graph-based message passing frameworks. However, two important challenges have not been well addressed yet: (i) Most of existing social recommendation models fail to fully explore the multi-type user-item interactive behavior as well as the underlying cross-relational inter-dependencies. (ii) While the learned social state vector is able to model pair-wise user dependencies, it still has limited representation capacity in capturing the global social context across users. To tackle these limitations, we propose a new Social Recommendation framework with Hierarchical Graph Neural Networks (SR-HGNN). In particular, we first design a relation-aware reconstructed graph neural network to inject the cross-type collaborative semantics into the recommendation framework. In addition, we further augment SR-HGNN with a social relation encoder based on the mutual information learning paradigm between low-level user embeddings and high-level global representation, which endows SR-HGNN with the capability of capturing the global social contextual signals. Empirical results on three public benchmarks demonstrate that SR-HGNN significantly outperforms state-of-the-art recommendation methods. Source codes are available at: https://github.com/xhcdream/SR-HGNN.
[ 5 ] Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
标题：基于时空图扩散网络的交通流预测
链接：https://arxiv.org/abs/2110.04038
作者：Xiyue Zhang,Chao Huang,Yong Xu,Lianghao Xia,Peng Dai,Liefeng Bo,Junbo Zhang,Yu Zheng
机构：South China University of Technology, China, JD Finance America Corporation, USA, Communication and Computer Network Laboratory of Guangdong, China, Peng Cheng Laboratory, China
备注：Published as a paper at AAAI 2021
摘要：准确预测城市范围内的交通流量在各种时空挖掘应用中发挥着关键作用，如智能交通控制和公共风险评估。虽然之前的工作在学习交通时间动态和空间相关性方面做出了重大努力，但当前模型存在两个关键限制。首先，现有的方法只考虑相邻区域之间的空间相关性，而忽略了区域间的全局相关性。此外，这些方法无法对复杂的交通流过渡规律进行编码，这些规律在本质上具有时间依赖性和多分辨率。为了应对这些挑战，我们开发了一种新的交通预测框架-时空图扩散网络（ST-GDN）。特别是，ST-GDN是一种层次结构的图神经结构，它不仅学习局部区域的地理依赖，而且从全局角度学习空间语义。此外，还开发了一个多尺度注意网络，使ST-GDN具有捕获多层次时间动态的能力。在几个实际交通数据集上的实验表明，ST-GDN优于不同类型的最先进基线。有关实现的源代码，请访问https://github.com/jill001/ST-GDN.
摘要：Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment. While previous work has made significant efforts to learn traffic temporal dynamics and spatial dependencies, two key limitations exist in current models. First, only the neighboring spatial correlations among adjacent regions are considered in most existing methods, and the global inter-region dependency is ignored. Additionally, these methods fail to encode the complex traffic transition regularities exhibited with time-dependent and multi-resolution in nature. To tackle these challenges, we develop a new traffic prediction framework-Spatial-Temporal Graph Diffusion Network (ST-GDN). In particular, ST-GDN is a hierarchically structured graph neural architecture which learns not only the local region-wise geographical dependencies, but also the spatial semantics from a global perspective. Furthermore, a multi-scale attention network is developed to empower ST-GDN with the capability of capturing multi-level temporal dynamics. Experiments on several real-life traffic datasets demonstrate that ST-GDN outperforms different types of state-of-the-art baselines. Source codes of implementations are available at https://github.com/jill001/ST-GDN.
[ 6 ] Learning Sparse Graphs with a Core-periphery Structure
标题：学习具有核心-外围结构的稀疏图
链接：https://arxiv.org/abs/2110.04022
作者：Sravanthi Gurugubelli,Sundeep Prabhakar Chepuri
机构：Indian Institute of Science, Bangalore, India
摘要：在本文中，我们主要研究具有核心-外围结构的稀疏图的学习。我们提出了一个与核心-外围结构网络相关的数据生成模型，通过潜在图结构来建模节点属性对图中节点的核心分数的依赖性。利用所提出的模型，我们联合推断出一个稀疏图和节点核心分数，该分数在网络的核心部分（分别是外围部分）诱导密集（稀疏）连接。在各种真实数据上的数值实验表明，该方法仅从节点属性学习核心-外围结构图，同时学习与现有使用图作为输入并忽略常用节点属性估计核心分数的工作一致的核心分数分配。
摘要：In this paper, we focus on learning sparse graphs with a core-periphery structure. We propose a generative model for data associated with core-periphery structured networks to model the dependence of node attributes on core scores of the nodes of a graph through a latent graph structure. Using the proposed model, we jointly infer a sparse graph and nodal core scores that induce dense (sparse) connections in core (respectively, peripheral) parts of the network. Numerical experiments on a variety of real-world data indicate that the proposed method learns a core-periphery structured graph from node attributes alone, while simultaneously learning core score assignments that agree well with existing works that estimate core scores using graph as input and ignoring commonly available node attributes.
[ 7 ] Graphs as Tools to Improve Deep Learning Methods
标题：图作为改进深度学习方法的工具
链接：https://arxiv.org/abs/2110.03999
作者：Carlos Lassance,Myriam Bontonou,Mounia Hamidouche,Bastien Pasdeloup,Lucas Drumetz,Vincent Gripon
机构：arXiv:,.,v, [cs.LG] , Oct
备注：arXiv admin note: text overlap with arXiv:2012.07439
摘要：近年来，深度神经网络（DNN）的普及程度有了显著提高。然而，尽管它们在许多机器学习挑战中是最先进的，但它们仍然受到一些限制。例如，DNN需要大量的训练数据，在某些实际应用中可能无法获得这些数据。此外，当输入中加入小扰动时，DNN容易出现误分类错误。DNN也被视为黑匣子，因此他们的决定常常因缺乏可解释性而受到批评。在本章中，我们回顾了最近的一些工作，这些工作旨在使用图形作为工具来改进深度学习方法。这些图是根据深度学习体系结构中的特定层定义的。它们的顶点表示不同的样本，它们的边取决于相应中间表示的相似性。然后，可以使用各种方法来利用这些图形，其中许多方法建立在图形信号处理之上。本章由四个主要部分组成：DNN中间层可视化工具、数据表示去噪、优化图形目标函数和正则化学习过程。
摘要：In recent years, deep neural networks (DNNs) have known an important rise in popularity. However, although they are state-of-the-art in many machine learning challenges, they still suffer from several limitations. For example, DNNs require a lot of training data, which might not be available in some practical applications. In addition, when small perturbations are added to the inputs, DNNs are prone to misclassification errors. DNNs are also viewed as black-boxes and as such their decisions are often criticized for their lack of interpretability. In this chapter, we review recent works that aim at using graphs as tools to improve deep learning methods. These graphs are defined considering a specific layer in a deep learning architecture. Their vertices represent distinct samples, and their edges depend on the similarity of the corresponding intermediate representations. These graphs can then be leveraged using various methodologies, many of which built on top of graph signal processing. This chapter is composed of four main parts: tools for visualizing intermediate layers in a DNN, denoising data representations, optimizing graph objective functions and regularizing the learning process.
[ 8 ] Stable Prediction on Graphs with Agnostic Distribution Shift
标题：具有不可知分布移位的图的稳定预测
链接：https://arxiv.org/abs/2110.03865
作者：Shengyu Zhang,Kun Kuang,Jiezhong Qiu,Jin Yu,Zhou Zhao,Hongxia Yang,Zhongfei Zhang,Fei Wu
机构： College of Computer Science and Technology, Zhejiang University, China, Department of Computer Science and Technology, Tsinghua University, China, Alibaba Group, China
备注：11 pages, 6 figures
摘要：图形是一种灵活有效的工具，在实践中可以表示复杂的结构，图形神经网络（GNN）已被证明在具有随机分离的训练和测试数据的各种图形任务中是有效的。然而，在实际应用中，训练图的分布可能与测试图的分布不同（例如，用户在用户项目训练图上的交互以及他们对项目的实际偏好，即测试环境，已知在推荐系统中存在不一致）。此外，当训练GNN时，测试数据的分布总是不可知的。因此，我们面临着图形学习的训练和测试之间的不可知分布转移，这将导致传统GNN在不同测试环境中的不稳定推理。为了解决这个问题，我们提出了一个新的GNNs稳定预测框架，它允许在图上进行局部和全局稳定的学习和预测。特别是，由于每个节点在GNN中部分由其邻居表示，因此我们建议通过重新加权信息传播/聚合过程来捕获每个节点的稳定属性（局部稳定）。对于全局稳定性，我们提出了一个稳定的正则化器，它可以减少异构环境下的训练损失，从而使GNN具有良好的通用性。我们在几个图形基准和一个嘈杂的工业推荐数据集上进行了广泛的实验，该数据集是在产品促销节期间连续5天收集的。结果表明，该方法在具有不可知分布移位（包括节点标签和属性引起的移位）的图的稳定预测方面优于各种SOTA GNN。
摘要：Graph is a flexible and effective tool to represent complex structures in practice and graph neural networks (GNNs) have been shown to be effective on various graph tasks with randomly separated training and testing data. In real applications, however, the distribution of training graph might be different from that of the test one (e.g., users' interactions on the user-item training graph and their actual preference on items, i.e., testing environment, are known to have inconsistencies in recommender systems). Moreover, the distribution of test data is always agnostic when GNNs are trained. Hence, we are facing the agnostic distribution shift between training and testing on graph learning, which would lead to unstable inference of traditional GNNs across different test environments. To address this problem, we propose a novel stable prediction framework for GNNs, which permits both locally and globally stable learning and prediction on graphs. In particular, since each node is partially represented by its neighbors in GNNs, we propose to capture the stable properties for each node (locally stable) by re-weighting the information propagation/aggregation processes. For global stability, we propose a stable regularizer that reduces the training losses on heterogeneous environments and thus warping the GNNs to generalize well. We conduct extensive experiments on several graph benchmarks and a noisy industrial recommendation dataset that is collected from 5 consecutive days during a product promotion festival. The results demonstrate that our method outperforms various SOTA GNNs for stable prediction on graphs with agnostic distribution shift, including shift caused by node labels and attributes.
[ 9 ] CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation
标题：CCGG：一种用于类条件图生成的深度自回归模型
链接：https://arxiv.org/abs/2110.03800
作者：Matin Yousefabadi,Yassaman Ommi,Faezeh Faez,Amirmojtaba Sabour,Mahdieh Soleymani Baghshah,Hamid R. Rabiee
机构： Department of Computer Engineering, Sharif university of Technology, Tehran, Iran, Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
摘要：图形数据结构是研究连通实体的基础。随着将数据表示为图形的应用程序数量的增加，图形生成问题最近已成为许多信号处理领域的一个热门话题。然而，尽管条件图生成具有重要意义，但在以前的研究中，创建具有所需特征的图的条件图生成相对较少。本文通过引入类条件图生成器（CCGG），解决了以类标签作为生成约束的类条件图生成问题。我们通过添加类别信息作为图形生成器模型的额外输入，并在其总损失中包含分类损失以及梯度传递技巧，构建了CCGG。我们的实验表明，CCGG在各种数据集上都优于现有的条件图生成方法。它还能够根据基于分布的评估指标来维护生成的图的质量。
摘要：Graph data structures are fundamental for studying connected entities. With an increase in the number of applications where data is represented as graphs, the problem of graph generation has recently become a hot topic in many signal processing areas. However, despite its significance, conditional graph generation that creates graphs with desired features is relatively less explored in previous studies. This paper addresses the problem of class-conditional graph generation that uses class labels as generation constraints by introducing the Class Conditioned Graph Generator (CCGG). We built CCGG by adding the class information as an additional input to a graph generator model and including a classification loss in its total loss along with a gradient passing trick. Our experiments show that CCGG outperforms existing conditional graph generation methods on various datasets. It also manages to maintain the quality of the generated graphs in terms of distribution-based evaluation metrics.
[ 10 ] Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding
标题：Knowledge Shees：一种知识图嵌入的Sheaf理论框架
链接：https://arxiv.org/abs/2110.03789
作者：Thomas Gebhart,Jakob Hansen,Paul Schrater
机构：Department of Computer Science, University of Minnesota, Department of Mathematics, The Ohio State University
摘要：知识图嵌入涉及到学习实体（图的顶点）和关系（图的边）的表示法，这样得到的表示法对知识图表示的已知事实信息进行编码，这些信息在内部是一致的，可以用于新关系的推断。我们证明了知识图嵌入自然地用拓扑和范畴语言 extit{cellular Treans}表示：学习知识图嵌入对应于在图上学习 extit{knowledge sheaf}，受一定约束。除了为知识图嵌入模型的推理提供一个通用框架外，这一层理论观点还承认了对嵌入的一大类先验约束的表达，并提供了新的推理能力。我们利用最近发展起来的Laplacian层谱理论来理解嵌入的局部和全局一致性，并通过对Laplacian层的调和延拓来开发复合关系推理的新方法。然后，我们实施这些想法，以突出这种新视角所激发的扩展的好处。
摘要：Knowledge graph embedding involves learning representations of entities -- the vertices of the graph -- and relations -- the edges of the graph -- such that the resulting representations encode the known factual information represented by the knowledge graph are internally consistent and can be used in the inference of new relations. We show that knowledge graph embedding is naturally expressed in the topological and categorical language of extit{cellular sheaves}: learning a knowledge graph embedding corresponds to learning a extit{knowledge sheaf} over the graph, subject to certain constraints. In addition to providing a generalized framework for reasoning about knowledge graph embedding models, this sheaf-theoretic perspective admits the expression of a broad class of prior constraints on embeddings and offers novel inferential capabilities. We leverage the recently developed spectral theory of sheaf Laplacians to understand the local and global consistency of embeddings and develop new methods for reasoning over composite relations through harmonic extension with respect to the sheaf Laplacian. We then implement these ideas to highlight the benefits of the extensions inspired by this new perspective.
[ 11 ] Label Propagation across Graphs: Node Classification using Graph Neural Tangent Kernels
标题：跨图的标签传播：基于图神经切核的节点分类
链接：https://arxiv.org/abs/2110.03763
作者：Artun Bayer,Arindam Chowdhury,Santiago Segarra
机构：Electrical and Computer Engineering, Rice University, USA
备注：Under review at IEEE ICASSP 2022
摘要：近几年来，图形神经网络（GNNs）在节点分类任务上取得了优异的性能。通常情况下，这是在一个半监督学习设置中构建的，其中整个图形（包括要标记的目标节点）可用于训练。在一定程度上受可伸缩性的驱动，最近的工作集中在归纳的情况下，其中只有图形的标记部分可用于训练。在这种情况下，我们当前的工作考虑了一个具有挑战性的归纳设置，其中一组标记的图可用于训练，而未标记的目标图是完全独立的，即标记的和未标记的节点之间没有连接。在假设测试图和训练图来自相似分布的隐式假设下，我们的目标是开发一个可推广到未观测连通结构的标记函数。为此，我们使用一个对应于无限宽GNN的图神经切线核（GNTK）来根据拓扑和节点特征查找不同图中节点之间的对应关系。我们通过剩余连接来增强GNTK的能力，并以经验的方式说明其在标准基准上的性能提升。
摘要：Graph neural networks (GNNs) have achieved superior performance on node classification tasks in the last few years. Commonly, this is framed in a transductive semi-supervised learning setup wherein the entire graph, including the target nodes to be labeled, is available for training. Driven in part by scalability, recent works have focused on the inductive case where only the labeled portion of a graph is available for training. In this context, our current work considers a challenging inductive setting where a set of labeled graphs are available for training while the unlabeled target graph is completely separate, i.e., there are no connections between labeled and unlabeled nodes. Under the implicit assumption that the testing and training graphs come from similar distributions, our goal is to develop a labeling function that generalizes to unobserved connectivity structures. To that end, we employ a graph neural tangent kernel (GNTK) that corresponds to infinitely wide GNNs to find correspondences between nodes in different graphs based on both the topology and the node features. We augment the capabilities of the GNTK with residual connections and empirically illustrate its performance gains on standard benchmarks.
[ 12 ] StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis
标题：用于通道间和通道内多分辨率脑图对齐和合成的阶梯图形网络(STRIDWAY GraphNet)
链接：https://arxiv.org/abs/2110.04279
作者：Islem Mhiri,Mohamed Ali Mahjoub,Islem Rekik
机构：ID ,⋆, BASIRA Lab, Istanbul Technical University
备注：arXiv admin note: substantial text overlap with arXiv:2107.06281
摘要：综合多模态医学数据提供补充知识，帮助医生做出准确的临床决策。尽管前景看好，但现有的多模态脑图合成框架存在一些局限性。首先，它们主要只处理一个问题（模态内或模态间），限制了它们的通用性，即同时合成模态间和模态内。第二，虽然很少有技术能够在单一模式（即内部模式）内处理超分辨率低分辨率脑图，但模式间图的超分辨率仍有待探索，尽管这可以避免昂贵的数据收集和处理。更重要的是，目标域和源域可能具有不同的分布，这会导致它们之间的域断开。为了填补这些空白，我们提出了一个多分辨率GraphNet（SG-Net）框架，以基于给定的模态和域间和域内的超分辨率脑图联合推断目标图模态。我们的SG网络基于三个主要贡献：（i）基于新的图形生成对抗网络，在内部（如形态功能）和内部（如功能）域从源图形预测目标图形，（ii）生成高分辨率脑图而无需诉诸耗时且昂贵的MRI处理步骤，以及（iii）使用模态间对准器来放松损失函数以优化，强制源分布以匹配地面真值图。此外，我们还设计了一个新的保留基本真值的损失函数来指导两个生成器更准确地学习基本真值脑图的拓扑结构。我们使用多分辨率阶梯从源图预测目标脑图的综合实验表明，与其变体和最先进的方法相比，我们的方法具有更好的性能。
摘要：Synthesizing multimodality medical data provides complementary knowledge and helps doctors make precise clinical decisions. Although promising, existing multimodal brain graph synthesis frameworks have several limitations. First, they mainly tackle only one problem (intra- or inter-modality), limiting their generalizability to synthesizing inter- and intra-modality simultaneously. Second, while few techniques work on super-resolving low-resolution brain graphs within a single modality (i.e., intra), inter-modality graph super-resolution remains unexplored though this would avoid the need for costly data collection and processing. More importantly, both target and source domains might have different distributions, which causes a domain fracture between them. To fill these gaps, we propose a multi-resolution StairwayGraphNet (SG-Net) framework to jointly infer a target graph modality based on a given modality and super-resolve brain graphs in both inter and intra domains. Our SG-Net is grounded in three main contributions: (i) predicting a target graph from a source one based on a novel graph generative adversarial network in both inter (e.g., morphological-functional) and intra (e.g., functional-functional) domains, (ii) generating high-resolution brain graphs without resorting to the time consuming and expensive MRI processing steps, and (iii) enforcing the source distribution to match that of the ground truth graphs using an inter-modality aligner to relax the loss function to optimize. Moreover, we design a new Ground Truth-Preserving loss function to guide both generators in learning the topological structure of ground truth brain graphs more accurately. Our comprehensive experiments on predicting target brain graphs from source graphs using a multi-resolution stairway showed the outperformance of our method in comparison with its variants and state-of-the-art method.