Weighted Graph Model Research Articles

We Put the “UN” in FUN: The Mathematical Guide to Saving the World

We Put the “UN” in FUN: The Mathematical Guide to Saving the World

The search method for key transmission sections based on an improved spectral clustering algorithm

With the increased complexity of power systems stemming from the connection of high-proportion renewable energy sources, coupled with the escalating volatility and uncertainty, the key transmission sections that serve as indicators of the power grid’s security status are also subject to frequent changes, posing challenges to grid monitoring. The search method for key transmission sections based on an improved spectral clustering algorithm is proposed in this paper. A branch weight model, considering the impact of node voltage and power flow factors, is initially established to comprehensively reflect the electrical connectivity between nodes. Subsequently, a weighted graph model is constructed based on spectral graph theory, and an improved spectral clustering algorithm is employed to partition the power grid. Finally, a safety risk indicator is utilized to identify whether the partitioned sections are key transmission sections. Results from case studies on the IEEE39-node system and actual power grid examples demonstrate that the proposed method accurately and effectively searches for all key transmission sections of the system and identifies their security risks. The application in real power grid scenarios validates its ability to screen out some previously unrecognized key transmission sections.

Implementing A resistance-relief approach into evaluating urban disaster management capacity: A case study of Xuzhou

In the field of disaster management, urban planning is a crucial tool for reducing the vulnerability of areas prone to natural disasters. Assessing the disaster management efficacy of urban planning is essential. However, the theoretical evaluation frameworks and precise assessment methods currently available are quite limited. Therefore, this study developed a new Resistance-Relief (2Rs) approach to evaluate disaster management capabilities in different regions. Based on the “2Rs” evaluation framework, an indicator system was developed to assess disaster management capabilities at the level of urban planning and construction. On this basis, a directed weighted graph model was established to comprehensively evaluate a city's disaster resistance and disaster relief capabilities, addressing the accuracy and comprehensiveness issues commonly found in single-dimensional evaluations. Furthermore, an indicator sensitivity analysis method based on the cloud model was proposed to analyze the sensitivity of evaluation indicators and the robustness of evaluation results. Using actual urban planning and construction data, this method was applied to assess seven disaster management zones in Xuzhou City, China. The results revealed inadequacies in the city's disaster preparedness and mitigation construction, as well as key factors affecting Xuzhou's disaster management capabilities. This new method contributes to further improvements in both disaster risk management and urban disaster prevention and mitigation planning.

Approximation of seismic velocities from the spectrum of weighted graphs

Extracting seismic velocities from recorded seismic data requires converting the shot gathers to mid-point gathers, calculating the velocity spectrum, and picking the velocity values. In this paper, we propose to use graph theory to extract the seismic velocity values directly from the mid-point gathers. We use spectral data of a weighted graph model for approximating the seismic velocity. We develop a regression model to predict the seismic velocity from the largest eigenvalue of the graph representing the physical system. The approach is tested on a synthetic seismic data that represent a typical near-surface geological situation. The method was able to predict the seismic velocity of the second layer with 99.4% accuracy.

Generative Boltzmann Adversarial Network in Manet Attack Detection and QOS Enhancement with Latency

Mobile Ad-Hoc Network (MANET) are considered as self-configured network those does not have any centralized base station for the network monitoring and control. MANET environment does not control architecture for routing for the frequent maintenance of topology. The drastic development of Internet leads to adverse effect of development in MANET for different multimedia application those are sensitive to latency. Upon the effective maintenance of the QoS routing route discovery is performed to calculate queue and contention delay. However, the MANET requirement comprises of the complex procedure to withstand the Quality of Service (QoS) with Artificial Intelligence (AI). In MANET it is necessary to compute the MANET attacks with improved QoS with the reduced latency as existing model leads to higher routing and increased latency. In this paper proposed a Generative Boltzmann Networking Weighted Graph (GBNWG) model for the QoS improvement in MANET to reduce latency. With GBNWG model the MANET model network performance are computed with the weighted graph model. The developed weighted graph computes the routes in the MANET network for the estimation of the available path in the routing metrices. The proposed GBNWG model is comparatively estimated with the conventional QOD technique. Simulation analysis stated that GBNWG scheme exhibits the improved performance in the QoS parameters. The GBNWG scheme improves the PDR value by 12%, 41% reduced control packets and 45% improved throughput value.

Research on automatic detection method of Microgrid bad data considering manifold sorting

In order to improve the quality of Microgrid data, an automatic detection method of Microgrid bad data considering manifold ordering is proposed. The ELM network design and training model was constructed to extract the characteristic quantities of Microgrid data. Then, after analyzing the generation conditions of bad data, the quartile distance method is used to divide the identification interval of bad data, so as to improve the detection accuracy fundamentally. Then the Microgrid data are mapped to corresponding points in multi-dimensional vectors to form a weighted graph model. Considering the approximate manifold structure of data, a high-dimensional data automatic detection method based on manifold ordering is designed. The sorting scores of data nodes are calculated by confidence propagation, and the automatic detection results of bad data are obtained. Experimental results show that the root-mean-square error of monitoring results decreases obviously when the proposed method is used to detect bad data of Microgrid, indicating that the proposed method improves the accuracy of data detection.

A Smart Evacuation Guidance System for Large Buildings

In large buildings, during the situation of fire or other hazards, a smart evacuation guidance system needs to fully consider manifold aspects of hazards to guide evacuees through exit gates as fast as possible by dynamic and safe routes. In this paper, we propose a smart evacuation guidance system with a dynamic evacuation routing approach by using the LCDT (Length-Capacity-Density-Trustiness) weighted graph model and partial view (PV) information which represents the hazard intensity and the crowd congestion information of a group of sections/floors in the building. The proposed system is designed as a distributed system with multiple layers of computing by using smart indicators. Given such a system, we develop an efficient distributed approach, so-called LCDT&PV, to find out effective evacuation routes dynamically. We then propose an estimating congestion strategy in order to improve the efficiency of dynamic evacuation routes. To validate the proposed system, we implement a simulator to compare the proposed evacuation routing approach with baseline approaches. Experimental results show that the proposed approach reduces up to 30% of the total evacuation time compared with others. Furthermore, through the results of initial smart indicator implementation, which can interact with the simulator, we show the viability of the proposed system.

Modelling how cleaner fish approach an ephemeral reward task demonstrates a role for ecologically tuned chunking in the evolution of advanced cognition.

What makes cognition “advanced” is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events (“chaining”) to representing various cue combinations as “chunks.” Here we develop a weighted graph model to study the mechanism enabling chunking ability and the conditions for its evolution and success, based on the ecology of the cleaner fish Labroides dimidiatus. In some environments, cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving this task in its common simplified laboratory format that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral–ephemeral and permanent–permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine-tuning of this ability, which may be the major target of selection during the evolution of advanced associative learning.

Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis

In the area of network analysis, centrality metrics play an important role in defining the “most important” actors in a social network. However, nowadays, most types of networks are dynamic, meaning their topology changes over time. The connection weights and the strengths of social links between nodes are an important concept in a social network. The new centrality measures are proposed for weighted networks, which relies on a time-ordered weighted graph model, generalized temporal degree and closeness centrality. Furthermore, two measures—Temporal Degree-Degree and Temporal Closeness-Closeness—are employed to better understand the significance of nodes in weighted dynamic networks. Our study is caried out according to real dynamic weighted networks dataset of a university-based karate club. Through extensive experiments and discussions of the proposed metrics, our analysis proves that there is an effectiveness on the impact of each node throughout social networks.

Fog radio access network optimization for 5G leveraging user mobility and traffic data

The surging data traffic and dynamic user mobility in 5G networks have posed significant demands for mobile operators to increase data processing capacity and ensure user handover quality. Specifically, a cost-effective and quality-aware radio access network (RAN) is in great necessity. With the emergence of fog-computing-based RAN architecture (Fog-RAN), the data processing units (BBUs) can be separated from base stations (RRHs) and hosted in distributed fog servers, where each server accommodates a community of RRHs to handle data traffic and user handover. The key problem in Fog-RAN optimization is how to cluster complementary RRHs into communities and allocate adequate numbers of BBUs for the fog servers, since real-world traffic and mobility patterns are highly dynamic to model, and it is not trivial to find an optimal RRH clustering and BBU allocation scheme from potentially enormous numbers of candidates. In this work, we propose a data-driven framework for cost-effective and quality-aware Fog-RAN optimization. In the RRH clustering phase, we build a weighted graph model to characterize user mobility patterns across RRHs, and propose a size-constrained community detection (SCUD) algorithm to cluster RRHs into communities with frequent internal handover events. In the BBU allocation phase, we formulate BBU allocation in each community fog server as a set partitioning problem, and propose a column-reduced integer programming (CLIP) algorithm to find optimal BBU allocation schemes that maximize BBU utilization rate. Evaluations using two large-scale real-world datasets collected from Ivory Coast and Senegal show that compared to the traditional RAN architecture, our framework effectively reduces the average handover overhead to 12.8% and 27.3%, and increases the average BBU utilization rate to 49.7% and 52.3% in both cities, respectively, which consistently outperforms the state-of-the-art baseline methods.

A Novel Voltage Collapse Path Analysis of Primary System Equivalent Model for Smart Grid

This paper proposes a novel equivalence model of the smart grid. The smart grid can be equivalent to a three-layer model, namely power supply, power distribution, and power layer. For equivalent model, the system voltage collapses path are analyzed, the characteristics of the N structure complex smart grid model are derived. The weighted graph model of the brittle relationship of complex smart grid systems is analyzed. The model has the characteristics of logical clarity, hierarchical representation, qualitative analysis. By finding the crash path through different equivalent modes, the crash state path between subsystems can be analyzed. The voltage collapse of one node in the operation of the smart grid system will be transmitted to the entire system through a chain reaction, which will cause the entire system to collapse. The maximum voltage collapse path for different equivalent modes is used to analyze the most likely collapse path. The method of predicting and controlling the brittle voltage collapse path of the smart grid can effectively optimize the performance of the entire system, which has important guiding significance for the design and control of the smart grid.

A New Similarity Method based on Weighted Graph Models for Matching Parallel Business Process Models

A New Similarity Method based on Weighted Graph Models for Matching Parallel Business Process Models

A Two-Tier Collection and Processing Scheme for Fog-Based Mobile Crowdsensing in the Internet of Vehicles

In view of the rapid development of the Internet of Vehicles (IoV) and wireless communication technology, intelligent transportation systems play an important role in improving urban road safety, promoting the behavioral interaction between users and networks, improving the service quality, and controlling the network cost. Based on the universality and real-time nature of the IoV, data collectors can cooperate with users to sense and collect relevant data within the driving range of vehicles. To achieve this goal, this article proposes a two-tier sensing scheme around the optimization of the sensing mechanism and data processing. In the routing layer, we build a weighted graph model based on vehicle fog, and we propose a new routing strategy to maximize each vehicle’s utilization. We consider that the sensing information collected by multiple vehicles could be repetitive, which would lead to many unnecessary communication flows in the network. Therefore, in the data processing layer, we consider resource consumption in the whole fog, assign different tasks to the sensing vehicles, and filter similar information on the relay nodes of the routing paths to reduce the waste of resources in IoV. Finally, we compare and analyze our two-tier scheme with related approaches, and the results show that our scheme has higher link utilization and lower resource consumption for a high-speed mobile network environment in IoV.

An Approximate Model for Event Detection From Twitter Data

The abundance and real-time availability of Twitter data have proved beneficial in detecting events in various domains such as emergency situations, crime detection, public health, place recommendations, etc. Nevertheless, two critical challenges occur while detecting events using social media data. First, the uncertainty in capturing the contextual relationship among tweets, which is the result of the limited availability of the contextual information due to the small length of tweets. Second, the high computation cost required in event detection due to massive data processing. Earlier research works, addressing these challenges, have tried to capture the contextual information by using the dense vector representations of texts leveraging deep neural word embedding generation models such as Word2Vec and GloVe. However, these models are trained on the Euclidean vector space which fails to amalgamate the directional information of the vectors with the semantic information in text, incurring high computational costs. To target both the problems simultaneously, we propose modeling Twitter data as a graph-of-sentences which retains the contextual relationships while maintaining lower computational cost. The proposed model captures contextual information using JoSE, a spherical vector representation leveraging the word-word and word-paragraph semantic co-occurrence statistics in a spherical generative model. Furthermore, the framework uses the weighted-graph model to capture all the relationships among the Twitter data efficiently. The graph is further pruned with the help of the graph component filtering approach. The graph clustering model, employed to detect the events, leverages the edge weights and the partial-k clustering approach maintaining low computation costs. The experimentation on the annotated benchmark Twitter data set and the real-world datasets show improved run-time performance up to 30% while maintaining the qualitative performance (F1-score) comparable to the state-of-the-art models.

A Two-Stagse Approach for Social Identity Linkage Based on an Enhanced Weighted Graph Model

Social identity linkage refers to identify the accounts belong to the same person across different social networks. This work can assist in building more complete social profiles, which is valuable for many social-powered applications. In this paper, we propose a two-stage approach to improve the efficiency and accuracy of large-scale social identity linkage. The first stage deals with the seed set enrichment problem and focuses on exploring a larger set of seeds with greater precision. The second stage deals with the global propagation problem and focuses on finding more matched pairs with lower computation. Moreover, we propose an enhanced weighted graph model to deeply investigate the structural characteristics. We also develop an attribute representation method to reduce the impact of missing attributes. Finally, we evaluate our method based on the datasets collected from two popular social networks in China. And the experimental results demonstrate that our method outperforms other state of the art algorithms.

Optimal transportation planning of classified domestic garbage based on map distance

Domestic garbage classification is required in many cities, however, optimal transportation of classified garbage has not been widely studied. Here, the optimal transportation of the classified domestic garbage from the waste transfer center to the garbage disposal station is considered. The locations of waste transfer centers, map distances between these waste transfer centers, different types of garbage disposal stations and the quantity of waste in different areas are used as key factors for constructing a weighted graph model. This study considers Nanshan District in Shenzhen, China as a case study. Solutions were obtained by applying the proposed model to the area using data from 2011. To achieve the decision goal of identifying feasible traveling distance and time for each trash truck, the number of routes considered for recyclable waste transportation was no less than 5; for non-recyclable waste transportation was no less than 2; and for harmful waste transportation was 4. The study provides an auxiliary management tool for optimal municipal solid waste disposal that improves the sustainability of the system.

Estimating Parameters of a Directed Weighted Graph Model with Beta-Distributed Edge-Weights

We introduce a directed, weighted random graph model, where the edge-weights are independent and beta distributed with parameters depending on their endpoints. We will show that the row- and column-sums of the transformed edge-weight matrix are sufficient statistics for the parameters, and use the theory of exponential families to prove that the ML estimate of the parameters exists and is unique. Then an algorithm to find this estimate is introduced together with convergence proof that uses properties of the digamma function. Simulation results and applications are also presented.

An Infrastructure-Free Indoor Localization Algorithm for Smartphones.

Accurate indoor positioning technology provides location-based service for a variety of applications. However, most existing indoor localization approaches (e.g., Wi-Fi and Bluetooth-based methods) rely heavily on positioning infrastructure, which prevents their large-scale deployment and limits the range at which they are applicable. Here, we proposed an infrastructure-free indoor positioning and tracking approach, termed LiMag, which used ubiquitous magnetic field and ambient lights (e.g., fluorescent, incandescent, and light-emitting diodes (LEDs)) without containing modulated information. We conducted an in-depth study on both the advantages and the challenges in leveraging magnetic field and ambient light intensity for indoor localization. Based on the insights from this study, we established a hybrid observation model that took full advantage of both the magnetic field and ambient light signals. To address the low discernibility of the hybrid observation model, LiMag first generated a single-step fingerprint model by vectorizing consecutive hybrid observations within each step. In order to accurately track users, a lightweight single-step tracking algorithm based on the single-step fingerprints and the particle filter framework was designed. LiMag leveraged the walking information of users and several single-step fingerprints to generate long trajectory fingerprints that exhibited much higher location differentiation ability than the single-step fingerprint. To accelerate particle convergence and eliminate the accumulative error of single-step tracking algorithm, a long trajectory calibration scheme based on long trajectory fingerprints was also introduced. An undirected weighted graph model was constructed to decrease the computational overhead resulting from this long trajectory matching. In addition to typical indoor scenarios including offices, shopping malls and parking lots, we also conducted experiments in more challenging scenarios, including large open-plan areas as well as environments characterized by strong sunlight. Our proposed algorithm achieved a 75th percentile localization accuracy of 1.8 m and 2.2 m, respectively, in the office and shopping mall tested. In conclusion, our LiMag algorithm provided location-based service of infrastructure-free with significantly improved localization accuracy and coverage, as well as satisfactory robustness inside complex indoor environments.

The global remittance network: an inflow and outflow analysis

ABSTRACTRemittances are flows of money between immigrants and their relatives. A scarcely addressed topic related to human migration is the application of Network Flow Analysis (NFA) in the study of remittances. This study provides both a theoretical and empirical macro-level study of worldwide remittance flows. Theoretically, a remittance model used by the World Bank is probabilistically analyzed and useful probability bounds are derived. Empirically, a weighted graph model built from World Bank data delivers important insight on the remittance generative process, the highest remittance flow values and inflow–outflow remittance variations. This manuscript concludes by postulating that remittances potentially result from a multiplicative process and that techniques based on NFA provide novel ways of approaching remittance dynamics problems.

An algebraic framework for minimum spanning tree problems

We formally prove that Prim's minimum spanning tree algorithm is correct for various optimisation problems with different aggregation functions. The original minimum weight spanning tree problem and the minimum bottleneck spanning tree problem are special cases, but the framework covers many other problems. To this end we work in new generalisations of relation algebras and Kleene algebras and in new algebraic structures that capture key operations used in Prim's algorithm and its specification. Weighted graphs form instances of these algebraic structures, where edge weights are taken from a linear order with a binary aggregation operation. Many existing results from relation algebras and Kleene algebras generalise from the relation model to the weighted-graph model with no or small changes. The overall structure of the proof uses Hoare logic. All results are formally verified in Isabelle/HOL heavily using its integrated automated theorem provers. This paper is an extended version of [36].