基于ESU图的活动社交网络用户参加活动推荐

活动社交网络(EBSNs)为用户提供了方便的组织、参加和分享社交活动的平台。该文面向EBSNs活动推荐问题,提出了包含活动(Event)、主办方(Sponsor)和用户(User)的ESU图模型,深入揭示了EBSNs的实体及其社交关系。因为用户参加活动受多个因素影响,我们提出了基于ESU图的活动推荐多因素决策模型,包括社交影响力、活动内容、活动地点及活动时间。根据ESU图特点,提出了基于双向重启随机游走算法BD-RWR的实体重要度计算方法。选取真实的EBSNs平台—豆瓣同城验证所提方法的有效性。实验结果表明,该文提出的ESU图模型及融合了多因素的活动推荐模型,与已有最新方法相比,有效地提升了用户参加活动的推荐效果。     

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威胁的攻击图,并给出内部社交网络威胁的攻击成功率计算方法。基于真实网络拓扑和脆弱性信息的实验结果表明,与现有攻击图的相关方法相比,该方法可有效发现网络中潜在的借助社交网络入侵的攻击路径。     

Discovering process models from event multiset

The aim of process mining is to discover the process model from the event log which is recorded by the information system. Typical steps of process mining algorithm can be described as: (1) generating event traces from event log, (2) analyzing event traces and obtaining ordering relations of tasks, (3) generating process model with ordering relations of tasks. The first two steps could be very time consuming involving millions of events and thousands of event traces. This paper presents a novel algorithm (λ-algorithm) which almost eliminates these two steps in generating event traces from event log and analyzing event traces so as to reduce the performance of process mining algorithm. Firstly, we retrieve the event multiset (input data of algorithm marked as MS) which records the frequency of each event but ignores their orders when extracted from event logs. The event in event multiset contains the information of post-activities. Secondly, we obtain ordering relations from event multiset. The ordering relations contain causal dependency, potential parallelism and non-potential parallelism. Finally, we discover a process models with ordering relations. The complexity of λ-algorithm is only bound up with the event classes (the set of events in event logs) that has significantly improved the performance of existing process mining algorithms and is expected to be more practical in real-world process mining based on event logs, as well as being able to detect SWF-nets, short-loops and most of implicit dependency (generated by non-free choice constructions).     

基于改进图注意机制的网络嵌入方法研究及应用

网络已被广泛用作抽象现实世界系统以及组织实体之间关系的数据结构;网络嵌入模型是将网络中的节点映射为连续向量空间表示的强大工具;基于图卷积(GCN,graph convolutional neural, GCN)的网络嵌入方法因受其模型迭代过程参数随机优化和聚合函数的影响,容易造成原始节点特征信息丢失的问题;为有效提升网络嵌入效果,针对于图神经网络模型在网络嵌入中节点表征学习的局限性,提出了一种基于二阶邻域基数保留策略的图注意力网络(SNCR-GAT,second-order neighborhood cardinality retention strategy graph attention network),通过聚合二阶邻域特征基数的方式,解决网络节点潜在特征学习过程中重要信息保留问题;通过在节点分类和可视化两个网络嵌入应用任务上进行实验,结果表明,SNCR-GAT模型在网络嵌入上的性能表现相比较基准方法更具优越性。     

Robust non-negative matrix factorization via joint sparse and graph regularization for transfer learning

In real-world applications, we often have to deal with some high-dimensional, sparse, noisy, and non-independent identically distributed data. In this paper, we aim to handle this kind of complex data in a transfer learning framework, and propose a robust non-negative matrix factorization via joint sparse and graph regularization model for transfer learning. First, we employ robust non-negative matrix factorization via sparse regularization model (RSNMF) to handle source domain data and then learn a meaningful matrix, which contains much common information between source domain and target domain data. Second, we treat this learned matrix as a bridge and transfer it to target domain. Target domain data are reconstructed by our robust non-negative matrix factorization via joint sparse and graph regularization model (RSGNMF). Third, we employ feature selection technique on new sparse represented target data. Fourth, we provide novel efficient iterative algorithms for RSNMF model and RSGNMF model and also give rigorous convergence and correctness analysis separately. Finally, experimental results on both text and image data sets demonstrate that our REGTL model outperforms existing start-of-art methods.     

Three-objective subgraph mining using multiobjective evolutionary programming

The existing methods for graph-based data mining (GBDM) follow the basic approach of applying a single-objective search with a user-defined threshold to discover interesting subgraphs. This obliges the user to deal with simple thresholds and impedes her/him from evaluating the mined subgraphs by defining different “goodness” (i.e., multiobjective) criteria regarding the characteristics of the subgraphs. In previous papers, we defined a multiobjective GBDM framework to perform bi-objective graph mining in terms of subgraph support and size maximization. Two different search methods were considered with this aim, a multiobjective beam search and a multiobjective evolutionary programming (MOEP). In this contribution, we extend the latter formulation to a three-objective framework by incorporating another classical graph mining objective, the subgraph diameter. The proposed MOEP method for multiobjective GBDM is tested on five synthetic and real-world datasets and its performance is compared against single and multiobjective subgraph mining approaches based on the classical Subdue technique in GBDM. The results highlight the application of multiobjective subgraph mining allows us to discover more diversified subgraphs in the objective space.     

A multi-modal discrete-event simulation model for military deployment

This paper introduces a logistics and transportation simulation that can be used to provide insights into potential outcomes of proposed military deployment plans. More specifically, we model a large-scale real-world military deployment planning problem (DPP) that involves planning the movement of military units from their home bases to their final destinations using different transportation assets on a multi-modal transportation network. We apply, for the first time, the event graph methodology and listener event graph object framework to create a simulation model of the DPP. We use and extend Simkit, an open-source Java Application Programming Interface for creating discrete-event simulation (DES) models. We use a medium-resolution modeling approach, as opposed to either high-resolution or low-resolution modeling paradigms, to reduce lengths of simulation runs without compromising reality. To accurately incorporate real and detailed transportation network data into the simulation, we use GeoKIT, a licensed, state-of-the-art, Java-based geographical information system. While our DES model is not a panacea for all, it allows for testing the feasibility and sensitivity of deployment plans under stochastic conditions prior to committing members of the military into harm’s way. The purpose of the paper is to acquaint the readers with the details of the DPP, the simulation model created, and the results of the analysis of a typical real-world case study.     

Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.     

Sequential pattern mining in databases with temporal uncertainty

Temporally uncertain data widely exist in many real-world applications. Temporal uncertainty can be caused by various reasons such as conflicting or missing event timestamps, network latency, granularity mismatch, synchronization problems, device precision limitations, data aggregation. In this paper, we propose an efficient algorithm to mine sequential patterns from data with temporal uncertainty. We propose an uncertain model in which timestamps are modeled by random variables and then design a new approach to manage temporal uncertainty. We integrate it into the pattern-growth sequential pattern mining algorithm to discover probabilistic frequent sequential patterns. Extensive experiments on both synthetic and real datasets prove that the proposed algorithm is both efficient and scalable.     

High-order pattern discovery from discrete-valued data

To uncover qualitative and quantitative patterns in a data set is a challenging task for research in the area of machine learning and data analysis. Due to the complexity of real-world data, high-order (polythetic) patterns or event associations, in addition to first-order class-dependent relationships, have to be acquired. Once the patterns of different orders are found, they should be represented in a form appropriate for further analysis and interpretation. The authors propose a novel method to discover qualitative and quantitative patterns (or event associations) inherent in a data set. It uses the adjusted residual analysis in statistics to test the significance of the occurrence of a pattern candidate against its expectation. To avoid exhaustive search of all possible combinations of primary events, techniques of eliminating the impossible pattern candidates are developed. The detected patterns of different orders are then represented in an attributed hypergraph which is lucid for pattern interpretation and analysis. Test results on artificial and real-world data are discussed toward the end of the paper     

融合图卷积网络模型的无监督社区检测算法

图卷积神经网络（Graph Convolutional Neural Network）能有效地提取非欧式距离数据中的特征信息。提出一种基于图卷积网络模型的无监督社区检测算法。选择图中某些节点添加人工标签来模拟在图上的信号输入,使其满足图卷积网络的传播特征的要求,通过修改后的图卷积网络传播规则将节点本身的标签传递至其相邻节点,通过对同一节点获得的不同标签进行比较后将节点归类,之后优化归类结果并输出社区划分矩阵。使用现实世界的数据集进行测试,并与一些其他社区检测算法进行对比评估。实验结果表明算法在不同类型的数据集中都能得到很好的社区划分效果。     

Towards Defense Against Adversarial Attacks on Graph Neural Networks via Calibrated Co-Training

Graph neural networks (GNNs) have achieved significant success in graph representation learning. Nevertheless, the recent work indicates that current GNNs are vulnerable to adversarial perturbations, in particular structural perturbations. This, therefore, narrows the application of GNN models in real-world scenarios. Such vulnerability can be attributed to the model’s excessive reliance on incomplete data views (e.g., graph convolutional networks (GCNs) heavily rely on graph structures to make predictions). By integrating the information from multiple perspectives, this problem can be effectively addressed, and typical views of graphs include the node feature view and the graph structure view. In this paper, we proposeC²oG, which combines these two typical views to train sub-models and fuses their knowledge through co-training. Due to the orthogonality of the views, sub-models in the feature view tend to be robust against the perturbations targeted at sub-models in the structure view.C²oGallows sub-models to correct one another mutually and thus enhance the robustness of their ensembles. In our evaluations,C²oGsignificantly improves the robustness of graph models against adversarial attacks without sacrificing their performance on clean datasets.     

KGDB: Knowledge Graph Database System with Unified Model and Query Language

Knowledge graph is an important cornerstone of artificial intelligence, which currently has two main data models: RDF graphs and property graphs. There are several query languages on these two data models, including SPARQL on RDF graphs and Cypher on property graphs. Over the last decade, various communities have developed different data management methods for RDF graphs and property graphs. Inconsistent data models and query languages hinder the wider application of knowledge graphs. In this paper, we propose a knowledge graphy database (KGDB) system with unified data model and query language. (1) We work out a unified storage scheme based on the relational model that supports the efficient storage of RDF graphs and property graphs, catering to the smooth storage and query of knowledge graph data. (2) The characteristic set-based clustering is used in KGDB for the storage of typeless entities. (3) It realizes the interoperability of SPARQL and Cypher by enabling them to operate on the same knowledge graph. Extensive experiments on real-world datasets and synthetic datasets reveal that KGDB is more efficient than existing knowledge graph database management systems in storage management and query efficiency. KGDB saves 30% of the storage space on average compared with gStore and Neo4j. In addition, KDGB is two orders of magnitude faster than gStore and Neo4j in the query of the real-world datasets, seen from experiments on the query of basic graph pattern matching.     

基于特征子空间邻域的局部保持流形学习算法*

局部保持流形学习算法通过保持局部邻域特性来挖掘隐藏在高维数据中的内在流形结构。然而,对于缺乏足够训练样本的高维数据集,或者高维数据集存在非线性结构和高维数据特征中存在冗余、干扰特征,使得在原特征空间中利用欧式距离定义的邻域关系并不能真实反映数据的内在流形结构,从而影响算法的性能。提出利用正约束寻找特征子空间的方法,使得在此子空间中更多的同类样本紧聚,并进一步在该子空间中构建邻域关系来挖掘高维数据的内在流形,形成基于特征子空间邻域特性的局部保持流形学习算法(NFS-LPP和NFS-NPE)。它们在一定程度上克服了高维小样本数据集难以正确挖掘内在流形结构的问题,在Yale和ORL人脸库上的分类和聚类实验验证了其有效性。     

Adaboost和随机图划分的无监督图像分类

针对当前大多数无监督图像分类方法不能对每个图像类进行特征选择和自动确定图像类别的数量问题,提出一种基于Adaboost和随机图划分的无监督图像分类方法。该方法包括两个部分:1)将图像分类问题看做是一个自动的随机图划分问题,其中图的每一个顶点代表一幅图像,通过划分形成的子图代表了图像类。再采用Ada-boost算法对每一个形成的图像类进行特征选择,从而得到每类图像的表达模型。2)采用一种基于蒙特卡洛马尔可夫链(MCMC)的随机采样算法(SWC)来对图进行划分。相比传统的随机采样算法,SWC具有更快的收敛速度。在两个图像数据集上的实验结果表明,本文方法的分类性能明显优于其他现有的无监督分类法。     

基于线图与PSO的网络重叠社区发现

从优化模块度的角度出发,引入线图理论,给出线图的硬划分与原 图的有重叠划分相对应的理论证明, 提出了一种基于线图与粒子群优化技术的网络重叠社区发现算法(Communities discovery based on line graph and particle swarm optimization, LGPSO), 该方法通过粒子群优化 (Particle swarm optimization, PSO)算法寻找网络对应线图的最优划分来发现网络重叠社区, 实验结果显示,该方法能够在无先验信息的条件下快速有效地揭示网络的重叠社区结构.     

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.     

融合知识图谱与深度学习的药物发现方法

海量增长的生物医学文献给文献挖掘技术带来巨大挑战.文中提出融合知识图谱与深度学习的药物发现方法,从已发表的文献中挖掘疾病的潜在治疗药物.首先抽取生物医学文献中实体间的关系,构造生物医学知识图谱,再通过知识图谱嵌入方法将知识图谱中的实体和关系转化为低维连续的向量,最后使用已知的药物疾病关系数据训练基于循环神经网络的药物发现模型.实验表明,文中方法不仅可以有效找到疾病的候选药物,还能提供相应的药物作用机制.