从不确定图中挖掘频繁子图模式

研究不确定图数据的挖掘,主要解决不确定图数据的频繁子图模式挖掘问题.介绍了一种数据模型来表示图的不确定性,以及一种期望支持度来评价子图模式的重要性.利用期望支持度的Apriori性质,给出了一种基于深度优先搜索策略的挖掘算法.该算法使用高效的期望支持度计算方法和搜索空间裁剪技术,使得计算子图模式的期望支持度所需的子图同构测试的数量从指数级降低到线性级.实验结果表明,该算法比简单的深度优先搜索算法快3～5个数量级,有很高的效率和可扩展性.     

基于拓扑序列的DAG子图包含查询算法研究

图模型具有强大的表达能力,被广泛用于各种应用领域的数据建模.如何在大规模图数据库中进行高效子图包含查询是当前的研究难点之一.由于子图同构是一个NP完全问题,在现有的子图包含查询算法中,基于图特征的索引技术被广泛用来提高查询处理性能,但是这些索引结构的维护代价较高.针对有向无环图提出了一种基于拓扑序列的子图包含查询算法,...     

基于共生频繁项树和逆矩阵的图挖掘

针对大数据时代的图挖掘算法中必须避免进行子图同构检测的问题,采用社会网络中的信息传播模型研究在单个大图中挖掘近邻频繁模式.首先计算节点标号对邻居节点的关联强度,运行联合概率分布来计算节点标号集合的概率支持度,以概率支持度为判断标准,运用改进的逆矩阵+共生频繁项树(COFI-树)挖掘算法对每个节点的标号构成的项集组成的事务数据集进行频繁项集挖掘.实验分析结果显示,该方法快过传统的单个大图频繁子图挖掘算法,返回的结果也多过频繁子图挖掘算法,并且可以发现一些传统频繁子图挖掘算法发现不了的有趣模式.而且与基于FP-树的频繁模式挖掘算法相比,逆矩阵+COFI-树能够支持大规模数据集,对内存利用效率较高.     

选择-验证-过滤:一种迭代的子图包含查询处理机制

近年来,图模型广泛应用于生物信息、计算化学、语义网等领域.目前,"过滤-验证"机制被广泛用于子图包含查询,即首先根据图数据的特征构造索引,然后根据索引产生候选集,最后对候选集中的每一个图进行子图同构验证.在这类算法中,"过滤"阶段是关注的重点,力争过滤掉更多的数据;而"验证"阶段则只是单纯地进行候选图子图同构检测,并没有进一步优化查询性能的可能.因此,提出了一种新的子图包含查询的迭代处理机制:"选择-验证-过滤",可利用从子图同构验证过程中得到的信息,结合数据库中图数据之间的相关关系,进行迭代查询处理.该机制首先选择数据库中的图与查询图进行同构验证,然后根据本次验证得到的信息,结合图数据之间的子图映射关系,进行迭代查询处理.一旦子图同构验证成功则可直接获得查询结果,而若验证不成功,则可以缩小下次迭代的查询搜索空间.为提高验证成功概率,提出了一种基于搜索空间预测的图选择策略.大量实验表明,该算法具有较"过滤-验证"机制更高的查询处理性能.     

面向高级别代码克隆检测方法的设计与实现

代码克隆检测是软件工程中的基础研究，在软件分析和维护方面有着广泛应用。目前对于有文本差异的高级别（即学术界定义的级别3和级别4）克隆检测，现有方法存在检出率（回收率）不高的问题。基于程序依赖图PDG的检测方法是高级别克隆检测的一类重要方法，但这类方法依赖子图同构的精确图匹配算法，算法时间复杂度高且回收率较低。为此，提出了一种新的高级别代码克隆检测方法，使用基于 Weisfeiler-Lehman图核的非精确图匹配算法进行代码克隆检测。实验结果表明，与已有的代码克隆检测方法相比， 该方法可以检出更多的高级别克隆且计算时间较短。     

基于包含度的子图匹配方法

子图匹配是图论中最基本的操作.研究子图匹配的一个变种,即：在一个节点拥有若干元素的大图数据库中,找到与给定查询图结构同构并且对应节点元素的加权集合包含度大于给定值的所有子图,称作基于包含度的子图匹配（subgraph matching with inclusion degree,简称SMID）.该查询能够应用于多种场景,包括论文检索、社区发现、企业招聘等.为高效实现SMID,设计了同时包含节点元素和图结构信息的数据签名与查询签名,在离线处理阶段,利用数据签名为数据图建立动态签名树（DS-Tree）,以加快在线处理时图节点的匹配过程.为解决DS-Tree占用空间大的问题,设计了一种DS-Tree压缩方法,在对查询效率影响不大的情况下减小了索引空间.为进一步加快查询效率,还提出了支配子图查询算法.在真实数据和人工数据上的实验结果表明,所提出的方法在效率和扩展性方面优于现有其他方法.     

一种有效率的基于图的关系学习算法

多关系数据挖掘根据表示形式可以分为基于图的MRDM和基于逻辑的MRDM.本文讨论了基于图的数据挖掘和基于图的关系学习之间的关系,重点介绍基于图的关系学习算法Subdue及其优缺点,针对它的缺点提出优化的算法F_Subdue,改进了子图同构的计算,减少了子图同构的次数.在实际和人工数据集上运行的实验结果显示它比原算法更加有效率.最后给出结论并指明将来的工作.     

基于变换的大图点边可视化综述

大图可视化是信息可视化领域的前沿课题之一,也是在线社会网络、信息安全、电子商务等热点行业大数据分析的重要支撑技术.基于变换的大图点边可视化方法由于其具有在线处理时间短、可视复杂度低、交互方法灵活多样等优点,近年来在学术界与实际商用系统中得到广泛重视与应用.文中从图可视化的基本概念及其在大图上的关键挑战出发,梳理了基于变换的大图点边可视化方法的典型分类与主要流程;通过详述3类基于变换的大图点边可视化典型方法(图数据抽象、视图变换与视角转换),阐明了不同方案的优缺点与适用场景,并进一步指出了未来工作的可行方向与潜在难点.     

不确定图间α-β子图同构匹配算法

子图查询返回图数据集合中所有包含查询图的数据图.在查询图和数据图同时为不确定性图的前提下,提出了不确定图间的期望子图同构定义和α-β子图同构匹配定义.不确定图间的期望子图同构是确定图上子图同构在概率图模型上的直接推广,不确定图间α-β子图同构利用两个限制阈值来衡量查询图和数据图间的匹配质量.文章详细阐述了α-β子图同构匹配的语义特点,分析了其和期望子图同构的联系和差别,设计实现α-β子图同构匹配判定算法.     

一种基于混合特征选择的图聚类算法

图聚类是图挖掘研究领域目前的研究热点之一.现有基于非深度学习技术的多个中小规模图的聚类算法提取频繁子图并作为特征,主要存在所选特征无效或重要特征丢失的问题,影响了聚类的性能.因此,本文提出了一种基于混合特征选择的图聚类算法.首先提出了一种基于主成分分析原理(Principal Component Analysis, PCA)的评估函数,从图数据集中挖掘出区分特征子图,作为候选特征.其次,提出了一种分支定界技术,加速了区分子图的挖掘过程.接着,为了进一步提高聚类准确率,不失一般性地选择了一种流行的嵌入式特征选择算法,继续对候选特征集进行特性选择,并同时完成图聚类.最后,通过真实数据集上的实验验证了本文提出的基于混合特征选择的图聚类方法的有效性.     

层次序列索引的大规模动态标签图子图查询

标签图常用于智能交通网、生物信息网等新兴领域的建模。子图查询作为图数据分析的关键问题，引起了研究者的广泛关注。对现有子图查询算法的研究发现，随着图数据规模增大且频繁更新，传统子图查询算法普遍存在查询效率低，存储开销大，忽略顶点标签信息等问题。为此，提出了一种支持大规模动态标签图子图查询的层次序列索引（Dynamic Hierarchical Sequence，DHS），该索引提取数据图中带有顶点编号的层次拓扑序列关系以实现子图查询；针对图的动态变化，提出了更新点拓扑扩展式索引维护策略，仅从局部变化顶点及边开始进行增量式更新，大大降低了重建索引造成的巨大开销；提出了基于DHS索引的子图查询方法，仅需将查询图与数据图的层次序列进行匹配即可获得候选集，并在其上利用关系匹配策略获得最终查询结果。实验证明提出的方法在保证高效查询的同时降低了索引的创建及维护时间，提高了子图查询效率。     

A new algorithm for error-tolerant subgraph isomorphism detection

We propose a new algorithm for error-correcting subgraph isomorphism detection from a set of model graphs to an unknown input graph. The algorithm is based on a compact representation of the model graphs. This representation is derived from the set of model graphs in an off-line preprocessing step. The main advantage of the proposed representation is that common subgraphs of different model graphs are represented only once. Therefore, at run time, given an unknown input graph, the computational effort of matching the common subgraphs for each model graph onto the input graph is done only once. Consequently, the new algorithm is only sublinearly dependent on the number of model graphs. Furthermore, the new algorithm can be combined with a future cost estimation method that greatly improves its run-time performance     

Matching graphs with unique node labels

A special class of graphs is introduced in this paper. The graphs belonging to this class are characterised by the existence of unique node labels. A number of matching algorithms for graphs with unique node labels are developed. It is shown that problems such as graph isomorphism, subgraph isomorphism, maximum common subgraph (MCS) and graph edit distance (GED) have a computational complexity that is only quadratic in the number of nodes. Moreover, computing the median of a set of graphs is only linear in the cardinality of the set. In a series of experiments, it is demonstrated that the proposed algorithms run very fast in practice. The considered class makes the matching of large graphs, consisting of thousands of nodes, computationally tractable. We also discuss an application of the considered class of graphs and related matching algorithms to the classification and detection of abnormal events in computer networks.     

A (sub)graph isomorphism algorithm for matching large graphs

We present an algorithm for graph isomorphism and subgraph isomorphism suited for dealing with large graphs. A first version of the algorithm has been presented in a previous paper, where we examined its performance for the isomorphism of small and medium size graphs. The algorithm is improved here to reduce its spatial complexity and to achieve a better performance on large graphs; its features are analyzed in detail with special reference to time and memory requirements. The results of a testing performed on a publicly available database of synthetically generated graphs and on graphs relative to a real application dealing with technical drawings are presented, confirming the effectiveness of the approach, especially when working with large graphs.     

An Efficient Framework for Multiple Subgraph Pattern Matching Models

With the popularity of storing large data graph in cloud, the emergence of subgraph pattern matching on a remote cloud has been inspired. Typically, subgraph pattern matching is defined in terms of subgraph isomorphism, which is an NP-complete problem and sometimes too strict to find useful matches in certain applications. And how to protect the privacy of data graphs in subgraph pattern matching without undermining matching results is an important concern. Thus, we propose a novel framework to achieve the privacy-preserving subgraph pattern matching in cloud. In order to protect the structural privacy in data graphs, we firstly develop a k-automorphism model based method. Additionally, we use a cost-model based label generalization method to protect label privacy in both data graphs and pattern graphs. During the generation of the k-automorphic graph, a large number of noise edges or vertices might be introduced to the original data graph. Thus, we use the outsourced graph, which is only a subset of a k-automorphic graph, to answer the subgraph pattern matching. The efficiency of the pattern matching process can be greatly improved in this way. Extensive experiments on real-world datasets demonstrate the high efficiency of our framework.

     

不确定图间α-β子图同构匹配算法

子图查询返回图数据集合中所有包含查询图的数据图。在查询图和数据图同时为不确定性图的前提下,提出了不确定图间的期望子图同构定义和α-β子图同构匹配定义。不确定图间的期望子图同构是确定图上子图同构在概率图模型上的直接推广,不确定图间α-β子图同构利用两个限制阈值来衡量查询图和数据图间的匹配质量。文章详细阐述了α-β子图同构匹配的语义特点,分析了其和期望子图同构的联系和差别,设计实现α-β子图同构匹配判定算法。     

支持动态图数据的子图查询方法

近年来,子图查询作为图数据库管理的一项重要课题受到国内外学者的广泛关注。在现实应用中大部分图数据是频繁更新的,而现有方法对图数据的频繁更新的维护代价较高。子图查询本身就是NP完全问题,在动态图数据上子图查询问题就变得更加困难。针对上述问题,提出了支持动态图数据的子图查询方法。该方法首先构造出每张图的拓扑层次序列作为索引,在序列中加入标号以便数据更新后对索引进行维护,再根据序列间的匹配关系过滤出候选集合,最后采用图同构算法验证候选集中的图,最终得到结果集合。该方法的索引构造简单且体积小,并且在图数据库更新后无需重构索引,不仅支持动态图数据上的子图查询,在静态图数据上也表现出良好的性能。     

Fast processing of graph queries on a large database of small and medium-sized data graphs

We propose a new way of indexing a large database of small and medium-sized graphs and processing exact subgraph matching (or subgraph isomorphism) and approximate (full) graph matching queries. Rather than decomposing a graph into smaller units (e.g., paths, trees, graphs) for indexing purposes, we represent each graph in the database by its graph signature, which is essentially a multiset. We construct a disk-based index on all the signatures via bulk loading. During query processing, a query graph is also mapped into its signature, and this signature is searched using the index by performing multiset operations. To improve the precision of exact subgraph matching, we develop a new scheme using the concept of line graphs. Through extensive evaluation on real and synthetic graph datasets, we demonstrate that our approach provides a scalable and efficient disk-based solution for a large database of small and medium-sized graphs.     

Graph mining for discovering infrastructure patterns in configuration management databases

A configuration management database (CMDB) can be considered to be a large graph representing the IT infrastructure entities and their interrelationships. Mining such graphs is challenging because they are large, complex, and multi-attributed and have many repeated labels. These characteristics pose challenges for graph mining algorithms, due to the increased cost of subgraph isomorphism (for support counting) and graph isomorphism (for eliminating duplicate patterns). The notion of pattern frequency or support is also more challenging in a single graph, since it has to be defined in terms of the number of its (potentially, exponentially many) embeddings. We present CMDB-Miner, a novel two-step method for mining infrastructure patterns from CMDB graphs. It first samples the set of maximal frequent patterns and then clusters them to extract the representative infrastructure patterns. We demonstrate the effectiveness of CMDB-Miner on real-world CMDB graphs, as well as synthetic graphs.