THE STATIONARY DISTRIBUTION OF A CONTINUOUS－TIME RANDOM GRAPH PROCESS WITH INTERACTING EDGES

In previous papers, the stationary distributions of a class of discrete and continuoustime random graph processes with state space consisting of the simple and directed graphs on Nvenices were studied. In this paper, the random graph graph process is extended one impotent stepfurther by allowing interaction of edges. Similarly, We obtha the expressions of the stationarydistributions and prove that the process is ergodic under different editions.

Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis

The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders.However,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia diagnosis.In this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven models.Specifically,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation robust.Also,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions.The typical topological properties and discriminative features can be preserved entirely.Furthermore,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive diseases.Attempts on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional networks.The classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related augmentedmodels.Overall,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks.

Modeling and application of marketing and distribution data based on graph computing

Integrating marketing and distribution businesses is crucial for improving the coordination of equipment and the efficient management of multi-energy systems.New energy sources are continuously being connected to distribution grids;this,however,increases the complexity of the information structure of marketing and distribution businesses.The existing unified data model and the coordinated application of marketing and distribution suffer from various drawbacks.As a solution,this paper presents a data model of"one graph of marketing and distribution"and a framework for graph computing,by analyzing the current trends of business and data in the marketing and distribution fields and using graph data theory.Specifically,this work aims to determine the correlation between distribution transformers and marketing users,which is crucial for elucidating the connection between marketing and distribution.In this manner,a novel identification algorithm is proposed based on the collected data for marketing and distribution.Lastly,a forecasting application is developed based on the proposed algorithm to realize the coordinated prediction and consumption of distributed photovoltaic power generation and distribution loads.Furthermore,an operation and maintenance(O&M)knowledge graph reasoning application is developed to improve the intelligent O&M ability of marketing and distribution equipment.

Researches on the Distribution Domains of G(4) G(4)的分布域之研究

讨论了有固定的最大度△=4和n个顶点的一类无自环,无向、连通平面图G(△)的异构类的分布域R(v,n)(圈秩v=1---n+1)的三种情况,从而获得R(v,n)的A型、B型和C型的分布定理,并举例说明分布域在图论和碳氢化合物上的应用。

Cooperative Vehicular Content Distribution in Edge Computing Assisted 5G-VANET

By leveraging the 5G enabled vehicular ad hoc network(5G-VANET), it is widely recognized that connected vehicles have the potentials to improve road safety, transportation intelligence and provide in-vehicle entertainment experience. However, many enabling applications in 5G-VANET rely on the efficient content sharing among mobile vehicles, which is a very challenging issue due to the extremely large data volume, rapid topology change, and unbalanced traffic. In this paper, we investigate content prefetching and distribution in 5G-VANET. We first introduce an edge computing based hierarchical architecture for efficient distribution of large-volume vehicular data. We then propose a multi-place multi-factor prefetching scheme to meet the rapid topology change and unbalanced traffic. The content requests of vehicles can be served by neighbors, which can improve the sharing efficiency and alleviate the burden of networks. Furthermore, we use a graph theory based approach to solve the content distribution by transforming it into a maximum weighted independent set problem. Finally, the proposed scheme is evaluated with a greedy transmission strategy to demonstrate its efficiency.

The Visualization Analysis of Research Hotspot and Frontier Technology of the Smart Power Distribution and Utilization Based on the Cite Space

In order to make an intensive study of the development of smart power distribution and utilization technology in China, their research hotspots and frontier technology are selected out through combining the informatics method, and using the CiteSpace which can take keyword cooccurrence analysis and draw the visualization graph. According to this result, we can infer the development trend of smart power distribution and utilization in the future, and providing reference for the researcher whose engage in this domain. The electric related literature was collected from the CNKI database in China. Under the smart power distribution and utilization domain, we also analyze the development of the power quality and the energy internet in detail.

Parametric message passing-based relative navigation in joint tactical information distribution system

Relative navigation is a key feature in the joint tactical information distribution system（JTIDS）.A parametric message passing algorithm based on factor graph is proposed to perform relative navigation in JTIDS.First of all,the joint posterior distribution of all the terminals＇ positions is represented by factor graph.Because of the nonlinearity between the positions and time-of-arrival（TOA） measurement,messages cannot be obtained in closed forms by directly using the sum-product algorithm on factor graph.To this end,the Euclidean norm is approximated by Taylor expansion.Then,all the messages on the factor graph can be derived in Gaussian forms,which enables the terminals to transmit means and covariances.Finally,the impact of major error sources on the navigation performance are evaluated by Monte Carlo simulations,e.g.,range measurement noise,priors of position uncertainty and velocity noise.Results show that the proposed algorithm outperforms the extended Kalman filter and cooperative extended Kalman filter in both static and mobile scenarios of the JTIDS.

Precise Asymptotic Distribution of the Number of Isolated Nodes in Wireless Networks with Lognormal Shadowing

In this paper, we study the connectivity of multihop wireless networks under the log-normal shadowing model by investigating the precise distribution of the number of isolated nodes. Under such a realistic shadowing model, all previous known works on the distribution of the number of isolated nodes were obtained only based on simulation studies or by ignoring the important boundary effect to avoid the challenging technical analysis, and thus cannot be applied to any practical wireless networks. It is extremely challenging to take the complicated boundary effect into consideration under such a realistic model because the transmission area of each node is an irregular region other than a circular area. Assume that the wireless nodes are represented by a Poisson point process with densitynover a unit-area disk, and that the transmission power is properly chosen so that the expected node degree of the network equals lnn + ξ (n), where ξ (n) approaches to a constant ξ as n →?∞. Under such a shadowing model with the boundary effect taken into consideration, we proved that the total number of isolated nodes is asymptotically Poisson with mean e$ {-ξ}. The Brun’s sieve is utilized to derive the precise asymptotic distribution. Our results can be used as design guidelines for any practical multihop wireless network where both the shadowing and boundary effects must be taken into consideration.

Calculation of the Water Distribution Networks Based on the Theory of Slow Transient Flow

Urban water supply network is a modern urban survival and development of the infrastructure of a city,and its normal running conditions have important significance. The actual hydraulic process in the variableload water distribution networks can be treated as the slow transient flow which belongs to the unsteady flow. This paper analyzes the multi-loops network slow transient model based on graph theory,and the link flow matrix is treated as the variables of the discrete solution model to simulate the process of the slow transient flow in the network. With the simulation of hydraulic regime in an actual pipe network,the changing laws of the flow in the pipes,nodal hydraulic heads and other hydraulic factors with the passage of time are obtained. Since the transient processes offer much more information than a steady process,the slow transient theory is not only practical on analyzing the hydraulic condition of the network,but also on identifying hydraulic resistance coefficients of pipes and detecting the leakage in networks.

Average Convergence for Directed&Undirected Graphs in Distributed Systems

Consensus control of multi-agent systems is an innovative paradigm for the development of intelligent distributed systems.This has fascinated numerous scientific groups for their promising applications as they have the freedom to achieve their local and global goals and make their own decisions.Network communication topologies based on graph and matrix theory are widely used in a various real-time applications ranging from software agents to robotics.Therefore,while sustaining the significance of both directed and undirected graphs,this research emphases on the demonstration of a distributed average consensus algorithm.It uses the harmonic mean in the domain of multi-agent systems with directed and undirected graphs under static topologies based on a control input scheme.The proposed agreement protocol focuses on achieving a constant consensus on directional and undirected graphs using the exchange of information between neighbors to update their status values and to be able to calculate the total number of agents that contribute to the communication network at the same time.The proposed method is implemented for the identical networks that are considered under the directional and non-directional communication links.Two different scenarios are simulated and it is concluded that the undirected approach has an advantage over directed graph communication in terms of processing time and the total number of iterations required to achieve convergence.The same network parameters are introduced for both orientations of the communication graphs.In addition,the results of the simulation and the calculation of various matrices are provided at the end to validate the effectiveness of the proposed algorithm to achieve consensus.

Similarity matching method of power distribution system operating data based on neural information retrieval

Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy.Therefore,improvement of the ability of data-driven operation management,intelligent analysis,and mining is urgently required.To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation,maintenance experience,and knowledge by rule and line,a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology.Based on the processing flow of the operating data of the power distribution system,a technical framework of neural information retrieval is established.Combined with the natural graph characteristics of the power distribution system,a unified graph data structure and a data fusion method of data access,data complement,and multi-source data are constructed.Further,a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed.The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set.The model is verified on the operating section of the power distribution system of a provincial grid area.The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.

DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms.Image and text descriptions added to the knowledge graph enrich the node information,which accounts for the advantage of the multi-modal knowledge graph.In the field of cross-modal retrieval platforms,multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational infor-mation provided by knowledge graphs.The representation learning method is sig-nificant to the application of multi-modal knowledge graphs.This paper proposes a distributed collaborative vector retrieval platform(DCRL-KG)using the multi-modal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data retrieval.Firstly,use distributed technology to classify and store the data in the knowledge graph to improve retrieval efficiency.Secondly,this paper uses BabelNet to expand the knowledge graph through multi-ple filtering processes and increase the diversification of information.Finally,this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image retrieval.The paper uses sentence retrieval and image retrieval experi-ments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space.

Constructing Multiple Scene Graphs in Distributed Environment

Scene graph is a infrastructure of the virtual reality system to organize the virtual scene with abstraction, it can provide facility for the rendering engine and should be integrated effectively on demand into a real-time system, where a large quantities of scene objects and resources can be manipulated and managed with high flexibility and reliability. We present a new scheme of multiple scene graphs to accommodate the features of rendering engine and distributed systems. Based upon that, some other functions, e.g. block query, interactive editing, permission management, instance response, ＂redo＂ and ＂undo＂, are implemented to satisfy various requirements. At the same time, our design has compatibility to popular C/S architecture with good concurrent performance. Above all, it is convenient to be used for further development. The results of experiments including responding time demonstrate its good performance.

Energy Economic Dispatch for Photovoltaic-Storage via Distributed Event-Triggered Surplus Algorithm

This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy roles,and energy supply and demand dynamics.The systemmodel is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously.To strike a balance between optimality and feasibility,renewable energy resources are modeled with considerations for forecasting errors,Gaussian distribution,and penalty factors.Furthermore,this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs.Rooted in surplus theory and finite time projection,the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints.The algorithm greatly reduces the communication burden through event triggering mechanism.Finally,both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.

Spark-GraphX框架下的大规模加权图最短路径查询

最短路径问题一直是计算机等学科的热点研究问题,常应用于社交网、交通网等诸多领域.图规模爆炸式的增长导致传统单机环境下的存储、查询已无法满足大规模图的处理需求.提出一种基于Spark-Graph X平台的大规模图最短路径查询方法(LSGSP-SG):首先利用经典算法对大规模图进行分割并标记,将割点的信息记录在文本文件中,然后利用大数据平台Spark的Graph X框架进行迭代式分布计算并进行各个计算机节点的消息通信及同步,最后返回最短路径查询结果.

GraphHP:一个图迭代处理的混合平台

BSP(Bulk Synchronous Parallel,BSP)计算模型是建立大规模迭代式图处理分布式系统的重要基础.现有平台(如Pregel、Giraph、Hama)虽然已经实现了较高的可扩展性,但主机之间高频同步和通信负荷严重影响了并行计算的效率.为了解决这个关键性问题,本文提出了一种基于混合式模型的执行平台GraphHP(Graph Hybrid Processing).它不仅继承了以顶点为中心的BSP编程接口,而且能够显著减少同步和通信负荷.通过在图分区内部和分区之间建立混合执行模型,GraphHP实现了伪超步迭代计算,把分区内部计算从分布式同步和通信中分离出来.这种混合执行模型不需要繁重的调度算法或者以图为中心的串行算法,就能有效减少同步和通信负荷.最后,本文评估了经典的BSP应用在GraphHP平台的实现方式.实验表明它比现有的BSP实现平台效率更高.本文提出的GraphHP平台虽然是基于Hama实现的,但它很容易迁移到其他的BSP平台.

On Simple MCD-Graphs

A simple graph G on n vettices is said to be a simple MCD-graph if G has no two cyties having the same length and has the maximum possible number of edges.Two results of the number of cy cles in G are given by introdueing the Concept of a path decomposition and by them,the following theorem is proved:If G is a simple MCD-graph,then G is not a 2-connected planar graph and for all n except seven integer,G is not a 2-connected graph on n vertices containing a subgraph homeomor phic to K_4.

基于改进causality graph的分布式可伸缩事件关联机制

传统事件关联技术无法有效满足分布式网络管理的需求,本文提出一种分布式可伸缩事件关联机制,采用先分布再集中的关联模式与自适应可伸缩的关联关系。定义了本地关联和网络关联两个过程,首先由设备进行本地关联,而后各地关联结果汇总到管理平台进行网络关联;将事件的关联关系与管理任务的关联关系相结合,根据管理任务在设备端的动态配置情况构建自适应可伸缩的关联关系,并支持对逻辑事件的推理。同时,在改进Causality Graph算法的基础上提出了实现该机制的相关算法。原型系统的应用实例验证了机制的有效性和优越性。

Data-driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods,which often lead to locally optimal solutions and require heavy online inference time consumption.To improve the quality of the solution and reduce the inference time burden,this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs(topology and power loads)and reactive power scheduling schemes of distribution networks,from a data-driven perspective.The graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix,a customized loss function,and the use of max-pooling layers.Additionally,a rulebased strategy is proposed to adjust infeasible solutions that violate constraints.Simulation results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load conditions.Moreover,its online inference time is significantly faster than traditional physical model based methods,particularly for large-scale distribution networks.

应用SAS/Graph模块分析棉铃空间分布情况

为解决棉铃空间分布传统分析展示不直观问题,运用SAS/Graph模块,可视化地展示棉铃空间分布情况。结果表明:中部果枝第一节位是成铃的关键区域,其次为中部果枝的第二节位;棉铃空间分布试验的3种密度均表现为中部果枝成铃率较高,下部果枝和上部果枝随密度增加成铃率降低,且随着密度增大成铃集中区域的主茎节位数下移。因此,为提高棉花产量,在保证中部果枝第一节位成铃的前提下,可从提高初始1~2台果枝的成铃率入手,当棉田实际保留株数较少时,可采取适当措施来保证果枝第二节位的成铃,不鼓励单果枝成铃三个及以上。