TG-SMR:AText Summarization Algorithm Based on Topic and Graph Models

Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization.However,most of the computing methods that are used in real systems are based on graph models,which are characterized by their simplicity and stability.Thus,this paper proposes an improved extractive text summarization algorithm based on both topic and graph models.The methodology of this work consists of two stages.First,the well-known TextRank algorithm is analyzed and its shortcomings are investigated.Then,an improved method is proposed with a new computational model of sentence weights.The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods.Finally,through experiments on the DUC2004 and DUC2006 datasets,our proposed improved graph model algorithm TG-SMR(Topic Graph-Summarizer)is compared to other text summarization systems.The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores.It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.

Parallelized User Clicks Recognition from Massive HTTP Data Based on Dependency Graph Model

With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.

Markov Graph Model Computation and Its Application to Intrusion Detection

Markov model is usually selected as the base model of user action in the intrusion detection system (IDS). However, the performance of the IDS depends on the status space of Markov model and it will degrade as the space dimension grows. Here, Markov Graph Model (MGM) is proposed to handle this issue. Specification of the model is described, and several methods for probability computation with MGM are also presented. Based on MGM, algorithms for building user model and predicting user action are presented. And the performance of these algorithms such as computing complexity, prediction accuracy, and storage requirement of MGM are analyzed.

Graph Modeling for Static Timing Analysis at Transistor Level in Nano-Scale CMOS Circuits

The development and the revolution of nanotechnology require more and effective methods to accurately estimating the timing analysis for any CMOS transistor level circuit. Many researches attempted to resolve the timing analysis, but the best method found till the moment is the Static Timing Analysis (STA). It is considered the best solution because of its accuracy and fast run time. Transistor level models are mandatory required for the best estimating methods, since these take into consideration all analysis scenarios to overcome problems of multiple-input switching, false paths and high stacks that are found in classic CMOS gates. In this paper, transistor level graph model is proposed to describe the behavior of CMOS circuits under predictive Nanotechnology SPICE parameters. This model represents the transistor in the CMOS circuit as nodes in the graph regardless of its positions in the gates to accurately estimating the timing analysis rather than inaccurate estimating which caused by the false paths at the gate level. Accurate static timing analysis is estimated using the model proposed in this paper. Building on the proposed model and the graph theory concepts, new algorithms are proposed and simulated to compute transistor timing analysis using RC model. Simulation results show the validity of the proposed graph model and its algorithms by using predictive Nano-Technology SPICE parameters for the tested technology. An important and effective extension has been achieved in this paper for a one that was published in international conference.

Stability analysis of reef fish communities based on symbiotic graph model

The community stability of coral reefs and fish is the focus of ecological monitoring of coral reefs.Among them,the realization of effective metrics of variations in reef fish communities(i.e.,the combined communities of coral reefs and fish)is important for analyzing the stability of communities as well as maintaining the ecological balance of coral reefs.Based on coral reef and fish data collected at St.John’s Island from 2004 to 2010,this study proposes a symbiotic graph modeling method to express the biological relationships of reef fish communities,and a Pyramid Match graph kernel method for fusing Attributes(PMA)to quantify community fluctuations to measure interannual variability of communities.The results showed that the community similarity was low in 2006,2007,and 2008.The total coral cover rate in the study area decreased by 32.04% from 2006 to 2007 and increased by 24% in 2008.The total number of fish fell from 3780 in 2006 to 2596 in 2007 and rose to 6249 in 2008.Among them,the proportion of herbivorous fish decreased to 30.84% in 2007.Furthermore,we have combined the Louvain algorithm with the proposed PMA method to effectively identify the regions that should be prioritized for protection.Experiments were conducted on real datasets with good results,demonstrating the potential of the proposed method to assist in the analysis of community stability and identification of priority conservation areas.

User Churn Prediction Hierarchical Model Based on Graph Attention Convolutional Neural Networks

The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

GraphMLP-Mixer:基于图-多层感知机架构的高效多行为序列推荐方法

在多行为序列推荐领域,图神经网络(GNNs)虽被广泛应用,但存在局限性,如对序列间协同信号建模不足和处理长距离依赖性等问题.针对这些问题,提出了一种新的解决框架GraphMLP-Mixer.该框架首先构造全局物品图来增强模型对序列间协同信号的建模,然后将感知机-混合器架构与图神经网络结合,得到图-感知机混合器模型对用户兴趣进行充分挖掘.GraphMLP-Mixer具有2个显著优势:一是能够有效捕捉用户行为的全局依赖性,同时减轻信息过压缩问题;二是其时间与空间效率显著提高,其复杂度与用户交互行为的数量成线性关系,优于现有基于GNN多行为序列推荐模型.在3个真实的公开数据集上进行实验,大量的实验结果验证了GraphMLP-Mixer在处理多行为序列推荐问题时的有效性和高效性.

COMPARISON OF THE ANALYTIC NETWORK PROCESS AND THE GRAPH MODEL FOR CONFLICT RESOLUTION

A comparison of two decision analysis tools for the analysis of strategic conflicts, the Analytic Network Process （ANP） and the graph model for conflict resolution, is carried out by applying them to the China-US TV dumping conflict. Firstly, the graph model is introduced along with practical procedures for modeling and analyzing conflicts using the decision support software, GMCR Ⅱ. Next, ANP is explained, emphasizing structural features and procedures for synthesizing priorities. Then a framework for employing ANP to analyze strategic conflicts is designed and used to compare ANP to the graph model. The case study of the China-US TV dumping conflict provides a basis for the graph model and ANP to be compared; different features of the approaches are highlighted. The study shows that because of different theoretical backgrounds, ANP and the graph model for conflict analysis both provide useful information which can be combined to furnish a better understanding of a strategic conflict.

DOMINATING ATTITUDES IN THE GRAPH MODEL FOR CONFLICT RESOLUTION

A formal methodology for analyzing the importance of weighing a decision maker＇s attitudes in a conflict is introduced and applied to the problem of negotiating a fair transfer of a brownfield property. A decision maker＇s attitudes are expressed in his consideration of his own preferences, as well as those of his opponents. Dominating attitudes are used to suggest that in a circumstance in which a decision maker takes into account multiple perspectives due to his attitudes, he may favor one perspective more heavily. The analysis of a brownfield acquisition conflict illustrates the types of insights that this methodology reveals.

ANALYZING MARKET COMPETITION BETWEEN AIRBUS AND BOEING USING A DUO HIERARCHICAL GRAPH MODEL FOR CONFLICT RESOLUTION

A duo hierarchical graph model for conflict resolution is developed to investigate market competition between Airbus and Boeing over aircraft sales in the Asia Pacific region. The duo hierarchical graph model, a significant extension of the graph model for conflict resolution methodology, contains two common decision makers, who take part in two related subconflicts, as well as local decision makers, who participate in only one subconflict. New stability definitions are proposed to describe forms of sanction unique to the hierarchical model. The interrelationships between stabilities in the overall graph model and in the two local models are investigated. Then the duo hierarchical graph model is applied to the competition between Airbus and Boeing in both the wide and narrow body markets in the Asia-Pacific region. The two types of Asian airlines have different operating strategies, so that the two markets constitute sub-competitions that can be modelled naturally using the duo hierarchical graph model. The stability results indicate a resolution for all decision makers that implies marketing strategies for the aircraft manufacturers and guidelines for aircraft purchase by the airlines. Thus, this model provides decision makers with a comprehensive understanding of the dynamics of the comoetition and guidance in identifving beneficial actions.

ROBUSTNESS OF EQUILIBRIA IN THE GRAPH MODEL FOR CONFLICT RESOLUTION

A novel approach for assessing the robustness of an equilibrium in conflict resolution is presented. Roughly, an equilibrium is robust if it is resilient, or resistant to deviation. Robustness assessment is based on a new concept called Level of Freedom, which evaluates the relative freedom of a decision maker to escape an equilibrium. Resolutions of a conflict can be affected by changes in decision makers＇ preferences, which may destabilize an equilibrium, causing the conflict to evolve. Hence, a conflict may become long-term and thereby continue to evolve, even after reaching an equilibrium. The new robustness measure is used to rank equilibria based on robustness, to facilitate distinguishing equiiibria that are relatively sustainable. An absolutely robust equilibrium is a special case in which the level of freedom is at an absolute minimum for each individual stability definition.

Layout graph model for semantic façade reconstruction using laser point clouds

Building façades can feature different patterns depending on the architectural style,function-ality,and size of the buildings;therefore,reconstructing these façades can be complicated.In particular,when semantic façades are reconstructed from point cloud data,uneven point density and noise make it difficult to accurately determine the façade structure.When inves-tigating façade layouts,Gestalt principles can be applied to cluster visually similar floors and façade elements,allowing for a more intuitive interpretation of façade structures.We propose a novel model for describing façade structures,namely the layout graph model,which involves a compound graph with two structure levels.In the proposed model,similar façade elements such as windows are first grouped into clusters.A down-layout graph is then formed using this cluster as a node and by combining intra-and inter-cluster spacings as the edges.Second,a top-layout graph is formed by clustering similar floors.By extracting relevant parameters from this model,we transform semantic façade reconstruction to an optimization strategy using simulated annealing coupled with Gibbs sampling.Multiple façade point cloud data with different features were selected from three datasets to verify the effectiveness of this method.The experimental results show that the proposed method achieves an average accuracy of 86.35%.Owing to its flexibility,the proposed layout graph model can deal with different types of façades and qualities of point cloud data,enabling a more robust and accurate reconstruc-tion of façade models.

Context-sensitive Web service discovery over the bipartite graph model

As service oriented architecture （SOA） matures, service consumption demand leads to an urgent requirement for service discovery. Unlike Web documents, services are intended to be executed to achieve objectives and/or desired goals of users. This leads to the notion that service discovery should take the ＂usage context＂ of service into account as well as service content （descriptions） which have been well explored. In this paper, we introduce the concept of service context which is used to represent service usage. In query processing, both service content and service context are ex- amined to identify services. We propose to represent ser- vice context by a weighted bipartite graph model. Based on the bipartite graph model, we reduce the gap between query space and service space by query expansion to improve re- call. We also design an iteration algorithm for result ranking by considering service contextsefulness as well as contentrelevance to improve precision. Finally, we develop a service search engine implementing this mechanism, and conduct some experiments to verify our idea.

A Two-Level Hierarchical Graph Model for Conflict Resolution with Application to International Climate Change Negotiations

A novel two-level hierarchical graph model is developed to analyze international climate change negotiations with hierarchical structures:the negotiations take place between two nations and between each nation and its provincial governments.The two national government are two decision makers at the top level.Within each nation,the two provincial governments negotiate with the national government at the lower level.The theoretical structure of this novel model,including decision makers,options,moves,and preference relations,are developed.The interrelationship between the stabilities in the two-level hierarchical graph model and the stabilities in local models are investigated by theorems.These theorems can be utilized to calculate complete stabilities in the two-level hierarchical graph model when the stabilities in local graph models are known.The international climate change negotiations as the illustrative example is then investigated in detail.The extra equilibrium,uniquely obtained by this novel methodology,suggests that opposition may still be from one provincial government when the national government does not sign the international climate agreement and implements existing environmental laws.Compared with other approaches,this novel methodology is an effective and flexible tool in analyzing hierarchical conflicts at two levels by providing decision makers with strategic resolutions with broader vision.

Continuous Multiplicative Attribute Graph Model

Network modeling is an important approach in many fields in analyzing complex systems. Recently new series of methods have emerged, by using Kronecker product and similar tools to model real systems. One of such approaches is the multiplicative attribute graph(MAG) model, which generates networks based on category attributes of nodes. In this paper we try to extend this model into a continuous one, give an overview of its properties, and discuss some special cases related to real-world networks, as well as the influence of attribute distribution and affinity function respectively.

Transportation,germs,culture:a dynamic graph model of COVID-19 outbreak

Backgrounds Various models have been applied to predict the trend of the epidemic since the outbreak of COVID-19.Methods:In this study,we designed a dynamic graph model,not for precisely predicting the number of infected cases,but for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors・Results^We demonstrated the impact of asymptomatic transmission in this outbreak and showed the effectiveness of city lockdown to halt virus spread within a city.We further illustrated that sudden emergence of a large number of cases could overwhelm the city medical system,and external medical aids are critical to not only containing the further spread of the virus but also reducing fatality.Conclusions Our model simulation showed that highly populated modern cities are particularly vulnerable and lessons learned in China could facilitate other countries to plan the proactive and decisive actions・We shall pay close attention to the asymptomatic transmission being suggested by rapidly accumulating evidence as dramatic changes in quarantine protocol are required to contain SARS・CoV・2 from spreading globally.

基于Graph WaveNet模型的机场网络延误预测

文中提出一种基于深度Graph WaveNet(GWN)模型的机场网络延误预测方法,对机场网络整体建模,将其转换为图结构并对网络中所有机场进行离港航班多步延误预测.GWN模型融合时间和空间卷积网络,时间卷积层引入扩展因果卷积和门控机制提升模型效率;空间卷积层采用双向卷积及自适应邻接矩阵充分挖掘延误信息的空间关联性.选择美国51个机场构建机场网络并进行延误预测分析.结果表明:GWN模型对机场未来3天离港航班准点率预测的平均绝对误差分别为4.718%、5.145%和5.240%,显著优于其它基线模型,且对不同量级机场均有稳定的预测表现,在多步预测上具有突出优势.

A novel configuration model for random graphs with given degree sequence

Recently, random graphs in which vertices are characterized by hidden variables controlling the establishment of edges between pairs of vertices have attracted much attention. This paper presents a specific realization of a class of random network models in which the connection probability between two vertices （i, j） is a specific function of degrees ki and kj. In the framework of the configuration model of random graphsp we find the analytical expressions for the degree correlation and clustering as a function of the variance of the desired degree distribution. The obtained expressions are checked by means of numerical simulations. Possible applications of our model are discussed.

Thermal-hydraulic modeling and analysis of hydraulic system by pseudo-bond graph

To increase the efficiency and reliability of the thermodynamics analysis of the hydraulic system, the method based on pseudo-bond graph is introduced. According to the working mechanism of hydraulic components, they can be separated into two categories: capacitive components and resistive components. Then, the thermal-hydraulic pseudo-bond graphs of capacitive C element and resistance R element were developed, based on the conservation of mass and energy. Subsequently, the connection rule for the pseudo-bond graph elements and the method to construct the complete thermal-hydraulic system model were proposed. On the basis of heat transfer analysis of a typical hydraulic circuit containing a piston pump, the lumped parameter mathematical model of the system was given. The good agreement between the simulation results and experimental data demonstrates the validity of the modeling method.