A survey on dynamic graph processing on GPUs: concepts,terminologies and systems

Graphs that are used to model real-world entities with vertices and relationships among entities with edges, have proven to be a powerful tool for describing real-world problems in applications. In most real-world scenarios, entities and their relationships are subject to constant changes. Graphs that record such changes are called dynamic graphs. In recent years, the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics results. As the scale of dynamic graphs becomes larger, higher performance requirements are demanded to dynamic graph processing systems. With the massive parallel processing power and high memory bandwidth, GPUs become mainstream vehicles to accelerate dynamic graph processing tasks. GPU-based dynamic graph processing systems mainly address two challenges: maintaining the graph data when updates occur (i.e., graph updating) and producing analytics results in time (i.e., graph computing). In this paper, we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph computing. To comprehensively discuss existing dynamic graph processing systems on GPUs, we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph computing. In addition, we discuss the challenges and future research directions of dynamic graph processing on GPUs.     

大规模时序图数据的查询处理与挖掘技术综述

时序图作为一种带有时间维度的图结构,在图数据的查询处理与挖掘工作中扮演着越来越重要的角色.与传统的静态图不同,时序图的结构会随时间序列发生改变,即时序图的边由时间激活.而且由于时序图上每条边都有记录时间的标签,所以时序图包含的信息量相较于静态图也更为庞大,这使得现有的数据查询处理方法不能很好地应用于时序图中.因此如何解决时序图上的数据查询处理与挖掘问题得到研究者们的关注.对现有的时序图上的查询处理与挖掘方法进行了综述,详细介绍了时序图的应用背景和基本定义,梳理了现有的时序图模型,并从图查询处理方法、图挖掘方法和时序图管理系统3个方面对时序图上现有的工作进行了详细的介绍和分析.最后对时序图上可能的研究方向进行了展望,为相关研究提供参考.     

Modeling and Verifying Graph Transformations in Proof Assistants

This paper takes first steps towards a formalization of graph transformations in a general setting of interactive theorem provers, which will form the basis for proofs of correctness of graph transformation systems. Whereas graph rewriting is usually performed by mapping a pattern graph into a source graph by means of a graph morphism and then carrying out operations on the image node and edge set, this article generalises the notion of pattern graph to path expressions, which are formulae in a fragment of first-order logic. We examine the correspondence with traditional graph rewriting and show that this interpretation is beneficial when formally reasoning about model transformations with the aid of proof assistants.     

混合图的H-Estrada指标

混合图的Hermitian邻接矩阵是共轭矩阵,其全体特征值称为混合图的H-谱。借助该矩阵的许多性质能更有效地研究混合图。基于H-谱引出混合图的一个重要拓扑指标--H-Estrada指标。拟用数学分析的方法,对其性质做数理研究。     

A spectral approach to detecting subtle anomalies in graphs

In this paper we investigate the problem of detecting small and subtle subgraphs embedded into a large graph with a common structure. We call the embedded small and subtle subgraphs as signals or anomalies and the large graph as background. Those small and subtle signals, which are structurally dissimilar to the background, are often hidden within graph communities and cannot be revealed in the graph’s global structure. We explore the minor eigenvectors of the graph’s adjacency matrix to detect subtle anomalies from a background graph. We demonstrate that when there are such subtle anomalies, there exist some minor eigenvectors with extreme values on some entries corresponding to the anomalies. Under the assumption of the Erdos–Renyi random graph model, we derive the formula that show how eigenvector entries are changed and give detectability conditions. We then extend our theoretical study to the general case where multiple anomalies are embedded in a background graph with community structure. We develop an algorithm that uses the eigenvector kurtosis to filter out the eigenvectors that capture the signals. Our theoretical analysis and empirical evaluations on both synthetic data and real social networks show effectiveness of our approach to detecting subtle signals.     

融合社交网络威胁的攻击图生成方法

针对现有攻击图生成和分析方法多数未考虑社交网络威胁的问题,提出一种基于知识图谱融合社交网络威胁的攻击图生成方法.根据攻击图的构建需求和收集的内网环境数据,设计融合社交网络威胁的网络安全本体模型和知识图谱,以实现对社交网络和物理网络数据的关联分析以及对攻击图输入信息的扩展,基于知识图谱采用广度优先搜索算法生成融合社交网络...     

基于持久性内存的属性图存储系统

属性图是一种流行的图数据模型, 在各种图系统中得到了广泛应用. 然而, 面向事务型负载的图数据库系统在执行图分析任务的场景下面临着高延迟等挑战. 传统的图分析系统往往是基于简单图模型, 而且大多不支持图的事务型负载. 因此, 迫切需要一个能够在属性图上高效处理事务型负载和图分析任务的图存储系统. 持久性内存的问世, 使得我们有机会重新设计图存储系统, 以充分发挥这种设备的特点. 为此, 本文提出了一种基于持久性内存的属性图存储系统, 名为TAG. TAG采用了一种新颖的混合架构的图存储方式, 以充分发挥持久性内存和主存的优势. 其次, 通过拓扑和索引结合的方式, 将图的拓扑嵌入到系统的索引中以加速图的拓扑查询. 最后, TAG通过基于标签的方式来组织图的属性数据, 进一步优化图的属性访问. 实验结果表明, TAG显著优于其他图数据库系统, 与图分析系统相比, TAG也有着相近的性能表现.     

基于l1范数和k近邻叠加图的半监督分类算法*

为了构造一个能够较好反映数据真实分布的图以提高分类性能,文中提出基于l¹范数和k近邻叠加图的半监督分类算法。首先构造一个l¹范数图,作为主图,然后构造一个k近邻图,作为辅图,最后将二者按一定比例叠加,得到l¹范数和k近邻叠加(LNKNNS)图。实验中选择标记样本比例从5%到25%,将基于LNKNNS图的半监督分类算法在USPS数据库上对比其它图(指数权重图、k近邻图、低秩表示图和l¹范数图)的算法。实验表明,文中算法的分类识别率更高,更适合基于图的半监督学习。     

A graph model for mutual information based clustering

We propose a graph model for mutual information based clustering problem. This problem was originally formulated as a constrained optimization problem with respect to the conditional probability distribution of clusters. Based on the stationary distribution induced from the problem setting, we propose a function which measures the relevance among data objects under the problem setting. This function is utilized to capture the relation among data objects, and the entire objects are represented as an edge-weighted graph where pairs of objects are connected with edges with their relevance. We show that, in hard assignment, the clustering problem can be approximated as a combinatorial problem over the proposed graph model when data is uniformly distributed. By representing the data objects as a graph based on our graph model, various graph based algorithms can be utilized to solve the clustering problem over the graph. The proposed approach is evaluated on the text clustering problem over 20 Newsgroup and TREC datasets. The results are encouraging and indicate the effectiveness of our approach.     

多视图对比增强的异质图结构学习方法

异质图神经网络作为一种异质图表示学习的方法,可以有效地抽取异质图中的复杂结构与语义信息,在节点分类和连接预测任务上取得了优异的表现,为知识图谱的表示与分析提供了有力的支撑.现有的异质图由于存在一定的噪声交互或缺失部分交互,导致异质图神经网络在节点聚合、更新时融入错误的邻域特征信息,从而影响模型的整体性能.为解决该问题,提出了多视图对比增强的异质图结构学习模型.该模型首先利用元路径保持异质图中的语义信息,并通过计算每条元路径下节点之间特征相似度生成相似度图,将其与元路径图融合,实现对图结构的优化.通过将相似度图与元路径图作为不同视图进行多视图对比,实现无监督信息的情况下优化图结构,摆脱对监督信号的依赖.最后,为解决神经网络模型在训练初期学习能力不足、生成的图结构中往往存在错误交互的问题,设计了一个渐进式的图结构融合方法.通过将元路径图和相似度图递增地加权相加,改变图结构融合过程中相似度图所占的比例,在抑制了因模型学习能力弱引入过多的错误交互的同时,达到了用相似度图中的交互抑制原有干扰交互或补全缺失交互的目的,实现了对异质图结构的优化.选择节点分类与节点聚类作为图结构学习的验证任务,在4种...     

基于深度BiLSTM和图卷积网络的方面级情感分析

现有方面级情感分析方法,存在无法获取最优文本表示和使用普通图卷积网络不能提取依存图中深层结构信息的问题。为此,提出了一种基于深度BiLSTM（DBiLSTM）和紧密连接的图卷积网络（DDGCN）模型。首先,通过DBiLSTM获取方面词与上下文单词间的深层语义信息;其次,在原始图卷积网络中加入紧密连接,以生成能提取深层结构信息的紧密图卷积网络;然后,利用改进后的图卷积网络捕获依存图上的结构信息;最终,将融合2种深层信息的文本表示用于情感分类。3个数据集上的实验结果表明,DDGCN模型相比对比模型在准确度和F1上均有提升。     

一些新的Hamilton图的必要条件

寻求Hamilton图的适当的特征刻画是图论的一个重大未解决问题,根据图的结构特征,设计了图的顶点的分层方法,研究了Hamilton图中层与层间对外顶点数和对外边数应该满足的关系,分析了Hamilton图中每层顶点数与每层对外项点数的关系,探讨了图与其Hamilton演化图的Hamilton性关系,最后得到一些新的Hamilton图的必要条件。所获得的新的Hamilton图的必要条件实用性强,使用方便,能判断一些原必要条件不能判断的非Hamilton图。     

基于最优传输的层次化图核

已有的图核大多关注图的局部属性,利用局部的拓扑特征构建图的相似性度量,忽略图的层次结构信息.为了解决这个问题,文中提出基于最优传输的层次化图核.首先,将每个图表示成层次化的图结构.在层次化图结构构建过程中,利用K-means聚类算法构造每层图的节点,节点间的概率连接作为图的边.然后,利用带有熵约束的最优传输计算两图的层次结构上每层图之间的最优传输距离.最后,基于最优传输距离计算基于最优传输的层次化图核.在6个真实图数据集上的实验表明,文中方法可提升分类性能.     

Some Compartmentalized Secure Task Assignment Models for Distributed Systems

This paper formulates abstract problems of assigning subtasks to agents (processors) in a distributed system with a goal that they can perform its global task efficiently. The paper models the distributed system with a graph that describes the communication capabilities of the constituting agents. This graph is referred to as the "organizational graph.” In addition, the desired task-performing activity is modeled with another graph describing the required communications. Then, a few variants of the task assignment problem are formulated with potentially conflicting objectives (or constraints) of load balancing and communication costs. For some of these variants this paper provides efficient algorithms that solve the assignment problem. Some problems are proven NP-complete, and some others are left open.     

知识图谱数据管理研究综述

知识图谱是人工智能的重要基石.各领域大规模知识图谱的构建和发布对知识图谱数据管理提出了新的挑战.以数据模型的结构和操作要素为主线,对目前的知识图谱数据管理理论、方法、技术与系统进行研究综述.首先,介绍知识图谱数据模型,包括RDF图模型和属性图模型,介绍5种知识图谱查询语言,包括SPARQL、Cypher、Gremlin、PGQL和G-CORE;然后,介绍知识图谱存储管理方案,包括基于关系的知识图谱存储管理和原生知识图谱存储管理;其次,探讨知识图谱上的图模式匹配、导航式和分析型3种查询操作.同时,介绍主流的知识图谱数据库管理系统,包括RDF三元组库和原生图数据库,描述目前面向知识图谱的分布式系统与框架,给出知识图谱评测基准.最后,展望知识图谱数据管理的未来研究方向.     

基于时空融合图网络学习的视频异常事件检测

视频中异常事件所体现的时空特征存在着较强的相关关系.针对视频异常事件发生的时空特征相关性而影响检测性能问题,提出了基于时空融合图网络学习的视频异常事件检测方法,该方法针对视频片段的特征分别构建空间相似图和时间连续图,将各片段对应为图中的节点,考虑各节点特征与其他节点特征的Top-k相似性动态形成边的权重,构成空间相似图;考虑各节点的m个时间段内的连续性形成边的权重,构成时间连续图．将空间相似图和时间连续图进行自适应加权融合形成时空融合图卷积网络,并学习生成视频特征.在排序损失中加入图的稀疏项约束降低图模型的过平滑效应并提升检测性能.在UCF-Crime和ShanghaiTech等视频异常事件数据集上进行了实验,以接收者操作曲线(receiver operating characteristic curve, ROC)以及曲线下面积(area under curve, AUC)值作为性能度量指标.在UCF-Crime数据集下,提出的方法在AUC上达到80.76%,比基准线高5.35%;在ShanghaiTech数据集中,AUC达到89.88%,比同类最好的方法高5.44%.实验结果表明:所提出的方法可有效提高视频异常事件检测的性能.     

Achieving Optimal Inter-Node Communication in Graph Partitioning Using Random Selection and Breadth-First Search

Processing large graph datasets represents an increasingly important area in computing research and applications. The size of many graph datasets has increased well beyond the processing capacity of a single computing node, thereby necessitating distributed approaches. As these datasets are processed over a distributed system of nodes, this leads to an inter-node communication cost problem that negatively affects system performance. Previously proposed algorithms implemented breadth-first search (BFS) for graph searching and focused on the execution, parallel performance and not the communication. In this paper a new methodology is proposed that combines BFS with random selection in order to partition large graph datasets and effectively minimize inter-node communication. The new method is discussed and applied to the single-source shortest path and PageRank algorithms using three graphs that are representative of real-world scenarios. Experimental results show that graph inter-node communication for canonical graphs representative of real-world data is improved up to 42 % in case of Powerlaw graph, up to 27 % in case of Random near K-regular graph (with low degree), and up to 7 % in case of Random near K-regular graph (with high degree).     

图卷积神经网络基础图结构影响研究

为进一步提高基于图卷积神经网络的半监督图节点分类的准确率,本文研究了基础图结构对图卷积神经网络的影响.通过对数据集(Cora、Citeseer及Pubmed)的图结构进行可视化,发现数据集(Cora、Citeseer)的图结构均为非连通图.通过研究非连通图中图拉普拉斯矩阵的"0"特征值和特征向量的特性,提出了通过对图拉...     

关于互连网络的几个猜想

n-立方体是著名的互连网络,星图、煎饼图和冒泡排序图是由凯莱图模型设计出来的重要的互连网络。对换树（transposition tree）的凯莱图是一类特殊的凯莱图,星图和冒泡排序图分别是对换树为星和路的凯莱图。给出了关于n-立方体、星图、煎饼图、冒泡排序图和对换树的凯莱图的各一个猜想;提出了对换图的凯莱图的概念,进而由这一概念设计出了两个互连网络——圈图和轮图,并证明冒泡排序图和星图分别可嵌入圈图和轮图。