Weighted Graph Representation Research Articles

Multi-agent distributed control of integrated process networks using an adaptive community detection approach

This paper focuses on developing an adaptive system decomposition approach for multi-agent distributed model predictive control (DMPC) of integrated process networks. The proposed system decomposition employs a refined spectral community detection method to construct an optimal distributed control framework based on the weighted graph representation of the state space process model. The resulting distributed architecture assigns controlled outputs and manipulated inputs to controller agents and delineates their interactions. The decomposition evolves as the process network undergoes various operating conditions, enabling adjustments in the distributed architecture and DMPC design. This adaptive architecture enhances the closed-loop performance and robustness of DMPC systems. The effectiveness of the multi-agent distributed control approach is investigated for a benchmark benzene alkylation process under two distinct operating conditions characterized by medium and low recycle ratios. Simulation results demonstrate that adaptive decompositions derived through spectral community detection, utilizing weighted graph representations, outperform the commonly employed unweighted hierarchical community detection-based system decompositions in terms of closed-loop performance and computational efficiency.

An adaptive distributed architecture for multi-agent state estimation and control of complex process systems

A multi-agent integrated distributed moving horizon estimation (DMHE) and model predictive control (DMPC) framework is developed for complex process networks. This framework utilizes an adaptive spectral community detection-based decomposition approach for a weighted graph representation of the state space model of the system to identify the optimal communities for distributed estimation and control. As the operating conditions of the process network change, the system decomposition adjusts, and the estimation and control agents are reassigned accordingly. These adjustments enable optimizing the integrated DMHE and DMPC architecture, enhancing robustness and closed-loop system performance. The effectiveness of the proposed adaptive distributed multi-agent estimation and control framework is demonstrated through a benchmark benzene alkylation process under various operating conditions. Simulation results show that the proposed multi-agent approach enhances closed-loop performance and computational efficiency compared to traditional system decomposition methods using unweighted hierarchical community detection.

A crashworthiness optimization method of subway underframe structures based on the differential evolution of the weighted graph representation

A crashworthiness optimization method of subway underframe structures based on the differential evolution of the weighted graph representation

Multimodal weighted graph representation for information extraction from visually rich documents

This paper introduces a novel system for information extraction from visually rich documents (VRD) using a weighted graph representation. The proposed method aims to improve the performance of the information extraction task by capturing the relationships between various VRD components. The VRD is modeled as a weighted graph, in which visual, textual, and spatial features of text regions are encoded in nodes and edges representing the relationships between neighboring text regions. The information extraction task from VRD is performed as a node classification task through the use of a graph convolutional networks, where the VRD graphs are fed into the network. The approach is evaluated across diverse documents, encompassing invoices and receipts, revealing achievement levels equal to or surpassing robust baselines.

Network analysis uncovers the communication structure of SARS-CoV-2 spike protein identifying sites for immunogen design

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has triggered myriad efforts to understand the structure and dynamics of this complex pathogen. The spike glycoprotein of SARS-CoV-2 is a significant target for immunogens as it is the means by which the virus enters human cells, while simultaneously sporting mutations responsible for immune escape. These functional and escape processes are regulated by complex molecular-level interactions. Our study presents quantitative insights on domain and residue contributions to allosteric communication, immune evasion, and local- and global-level control of functions through the derivation of a weighted graph representation from all-atom MD simulations. Focusing on the ancestral form and the D614G-variant, we provide evidence of the utility of our approach by guiding the selection of a mutation that alters the spike's stability. Taken together, the network approach serves as a valuable tool to evaluate communication "hot-spots" in proteins to guide design of stable immunogens.

Application of a weighted graph representation method to the crashworthiness optimization of subway collision frame structures

The collision frame structure is the most important subway structure for energy absorption and impact load transfer during foreign object collision. To improve the crashworthiness of the collision frame structure, a parameter optimization method based on a weighted graph representation method was proposed. Based on weighted graph theory, an equivalent mathematical model of the collision frame structure was established. Combined with the Belytschko beam element method, which is a large deformation calculation technique, a parametric finite element model (FEM) considering plastic mechanical properties was constructed, which was validated by a detailed finite element model and a quasi-static crush test. Based on the practical requirements, the crush displacement, the intrusion space and the mean crush force (MCF) were determined as constraints. The structural total mass was considered as the objective. The optimized structural parameters and key performance indicators were obtained and compared by the genetic algorithm (GA), Colliding bodies optimization (CBO), Enhanced colliding bodies optimization (ECBO). The results showed that the GA algorithm had the best optimization objective. The comparison results showed that the optimized collision frame structure could meet the crashworthiness requirements, while the structural mass was reduced from the initial 168.92 kg to 97.33 kg, thus giving a reduction rate of 42.38 %.

Systematic Partitioning of Proteins for Quantum-Chemical Fragmentation Methods Using Graph Algorithms

Quantum-chemical fragmentation methods offer an efficient approach for the treatment of large proteins, in particular if local target quantities such as protein-ligand interaction energies, enzymatic reaction energies, or spectroscopic properties of embedded chromophores are sought. However, the accuracy that is achievable for such local target quantities intricately depends on how the protein is partitioned into smaller fragments. While the commonly employed naı̈ve approach of using fragments with a fixed size is widely used, it can result in large and unpredictable errors when varying the fragment size. Here, we present a systematic partitioning scheme that aims at minimizing the fragmentation error of a local target quantity for a given maximum fragment size. To this end, we construct a weighted graph representation of the protein, in which the amino acids constitute the nodes. These nodes are connected by edges weighted with an estimate for the fragmentation error that is expected when cutting this edge. This allows us to employ graph partitioning algorithms provided by computer science to determine near-optimal partitions of the protein. We apply this scheme to a test set of six proteins representing various prototypical applications of quantum-chemical fragmentation methods using a simplified molecular fractionation with conjugate caps (MFCC) approach with hydrogen caps. We show that our graph-based scheme consistently improves upon the naı̈ve approach.

A novel weighted graph representation-based method for structural topology optimization

This paper proposes a novel weighted graph representation for structural topology optimization. Based on the graph theory, a weighted adjacency matrix is first introduced to collect the connectivity information and the corresponding width value of the edges. Accordingly, each edge with different width is symbolized as a rectangle to represent the mapped topology for a regular meshed design domain. To reduce the computational cost, an improved differential evolution (DE) process with a dual self-adaptive mutation operator which is named as the DSADE is proposed to utilize as an optimizer. Finally, three classical numerical tests are carried out. The results indicate that the present method can effectively deal with a series of structural topology optimization problem with different boundary constraints. In addition, by comparing with the related methods in literatures, it is found that the present method can achieve an optimized solution without complex initial definitions.

A method of low voltage residential micro-grids management using AMI/GIS systems and its application benefits

Most of the countries in the world have a futuristic vision of generating at least 33% of total installed generation capacity from the renewable sources like wind, solar etc. by 2020. As part of the vision, one of the key steps that are being taken is to integrate residential micro grids in large scale at low voltage distribution level. In this paper, we propose a novel and unique algorithm to effectively monitor and control the residential micro grids (MG) connected to low voltage radial distribution systems. An Advanced Metering Infrastructure (AMI) & Geographical Information System (GIS) based near real-time load flow method is developed for this purpose. Proposed method along with weighted graph representation of each 1-phase network connected to a 3-phase distribution transformer (DT) helps in real-time monitoring & control of residential micro grids at individual DT level. A case study is performed on standard IEEE 33 bus radial system demonstrating the working method of proposed algorithm and its major applications for distribution system automation & control are discussed.

Integrating network science and public transport accessibility analysis for comparative assessment

Network science offers powerful concepts and methods for studying complex systems, such as public transport networks. However, many existing studies on complex network analysis of public transport networks were primarily motivated to test network science concepts using real-life networks. Consequently, important properties related to public transport services in these studies have been overlooked. This has led to claims made by transport researchers that these works do not necessarily improve the understanding of system properties and performance. To bridge this gap, this study integrates network science and public transport accessibility analysis for comparative assessment across multiple public transport networks. The primary contribution of this study pertains to developing a method based on network science for computing public transport accessibility measured as the average travel impedance. The travel impedance metric is defined based on the generalized travel cost between stop pairs, containing initial and transfer waiting times, in-vehicle travel times and time-equivalent transfer penalty costs. To perform efficient computation, we propose a new type of weighted graph representation of public transport networks, which explicitly incorporates travel costs that are derived from general transit feed specification (GTFS) data. Besides the methodological contribution, the secondary one of this study consists in performing a comparative assessment of worldwide tram networks' accessibility. The analysis shows insights into how different travel components (e.g., in-vehicle travel times and waiting and transfer times) specifically account for the variance in accessibility across different networks. Additional references for improving the designing and planning of public transport networks can be obtained from such comparative assessments.

A prediction-Based VM consolidation approach in IaaS Cloud Data Centers

Recent years have witnessed a rapid growth in exploiting Cloud environments to host and deliver various types of virtualized resources as on-demand services. In order to optimally use Cloud resources, the arrangement of virtual machines (VMs) in physical machines (PMs) must be performed strategically, because the placement of VMs in accordance with the available resources can reduce energy consumption, improve resource utilization and, consequently, can increase companies benefits. However, VMs could have time varying workloads, which leads to degradation of performance and power consumption. Thus, re-configuring the VMs placement is essential. Virtual machine consolidation aims to optimally use the available resources by allocating several virtual machines on a set of physical ones (PMs). To determine the PMs capacities to reallocate VMs, it is important to predict their states based on resource utilization history within each VM, and the past VMs migration traffic. However, a common limitation between existing VM consolidation approaches is the lack of information about the history of (and the future) VM migration traffic. Through this paper, we aim to propose a virtual machine consolidation approach based on the estimation of requested resources and the future VM migration traffic. We exploit the strength of Kernel Density Estimation technique (KDE) as a powerful mean to forecast the future resource usage of each VM, and AKKA toolkit as an actor-based model that allows exchanging useful information about the host’s states. We adopt a weighted-graph representation to model the history of migration traffic between PMs and to design the actor-based topology of the data center. The obtained results show the effectiveness of our approach in terms of total number of migrations and energy consumption.

Robust system size reduction of discrete fracture networks: a multi-fidelity method that preserves transport characteristics

We propose a multi-fidelity system reduction technique that uses weighted graphs paired with three-dimensional discrete fracture network (DFN) modelling for efficient simulation of subsurface flow and transport in fractured media. DFN models are used to simulate flow and transport in subsurface fractured rock with low-permeability. One method to alleviate the heavy computational overhead associated with these simulations is to reduce the size of the DFN using a graph representation of it to identify the primary flow sub-network and only simulate flow and transport thereon. The first of these methods used unweighted graphs constructed solely on DFN topology and could be used for accurate predictions of first-passage times. However, these techniques perform poorly when predicting later stages of the mass breakthrough. We utilize a weighted-graph representation of the DFN where edge weights are based on hydrological parameters in the DFN that allows us to exploit the kinematic quantities derivable a posteriori from the flow solution obtained on the graph representation of the DFN to perform system reduction and predict the later stages of the breakthrough curve with high fidelity. We also propose and demonstrate the use of an adaptive pruning algorithm with error control that produces a pruned DFN sub-network whose predicted mass breakthrough agrees with the original DFN within a user-specified tolerance. The method allows for the level of accuracy to be a user-controlled parameter.

Dispersion and Mixing in Three‐Dimensional Discrete Fracture Networks: Nonlinear Interplay Between Structural and Hydraulic Heterogeneity

AbstractWe investigate the relative impact of topological, geometric, and hydraulic heterogeneity on transport processes in three‐dimensional fracture networks. Focusing on the two largest scales of heterogeneity in these systems, individual fracture and network structure, we compare transport through analogous structured and disordered three‐dimensional fracture networks with varying degrees of hydraulic heterogeneity. For the moderate levels of hydraulic heterogeneity we consider, network structure is the dominant control of transport through the networks. Less dispersion, both longitudinal and transverse, is observed in structured networks than in disordered networks, due in part to the higher connectivity in the former, independent of the level of hydraulic heterogeneity. However, increases in dispersion with higher hydraulic heterogeneity are larger in the disordered networks than in the structured networks, thereby indicating that the interplay between structural and hydraulic heterogeneity is nonlinear. We propose a measure of disorder in fracture networks by computing the Shannon entropy of the spectrum of the Laplacian of a weighted graph representation of the networks, where the weights are given by a combination of topological, geometric, and hydraulic properties. This metric, as a relative indicator by comparison between two networks, is a first approach to the dispersion potential and “mixing capacity” of a fracture network.

Enabling time-dependent uncertain eco-weights for road networks

Reduction of greenhouse gas (GHG) emissions from transportation is an essential part of the efforts to prevent global warming and climate change. Eco-routing, which enables drivers to use the most environmentally friendly routes, is able to substantially reduce GHG emissions from vehicular transportation. The foundation of eco-routing is a weighted-graph representation of a road network in which road segments, or edges, are associated with eco-weights that capture the GHG emissions caused by traversing the edges. Due to the dynamics of traffic, the eco-weights are best modeled as being time dependent and uncertain. We formalize the problem of assigning a time-dependent, uncertain eco-weight to each edge in a road network based on historical GPS records. In particular, a sequence of histograms is employed to describe the uncertain eco-weight of an edge at different time intervals. Compression techniques, including histogram merging and buckets reduction, are proposed to maintain compact histograms while retaining their accuracy. In addition, to better model real traffic conditions, virtual edges and extended virtual edges are proposed in order to represent adjacent edges with highly dependent travel costs. Based on the techniques above, different histogram aggregation methods are proposed to accurately estimate time-dependent GHG emissions for routes. Based on a 200-million GPS record data set collected from 150 vehicles in Denmark over two years, a comprehensive empirical study is conducted in order to gain insight into the effectiveness and efficiency of the proposed approach.

An embedded sectioning scheme for multiprocessor topology-aware mapping of irregular applications

We consider the problem of mapping irregular applications to multiprocessor architectures whose interconnect topologies affect the latencies of data movement across processor nodes. The starting point for solutions to this problem concerns suitable weighted graph representations of an irregular application and a processor topology. Prior results for this problem have demonstrated that graph partitioning approaches can provide high-quality solutions. Additionally, when coordinate information is available for the weighted graph of the application, the geometric mapping schemes can also provide high-quality solutions. We develop and present a scheme that we call ‘embedded sectioning’ that directly computes a locality enhancing embedding of the weighted graph representation which is then mapped to the processor topology using recursive coordinate bisection. Our scheme is specifically directed at gaining high-quality mappings for highly irregular applications where the amount of communication can vary greatly. We evaluate the quality of mappings produced by embedded sectioning for mesh-based processor topologies using well-accepted measures including congestion, dilation and their product, referred to as the communication volume. For a test suite of unit-weight graphs mapped to a 32 × 32 mesh of processors, our method improves congestion by 26%, dilation by 52% and communication volume by 64% relative to the best values of these measures from nine other schemes. Additionally, we observe that these improvements increase with an increase in the skewness of communication in applications. For a test suite with a skewness of two the corresponding improvements for congestion, dilation and communication volume are 72%, 52% and 87%, respectively.

An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery

We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure yielding compact and separated clusters. Each agent operates independently by performing a Markovian random walk on a suitable weighted graph representation of the input dataset. Such a weighted graph representation is induced by the specific parameter configuration of the dissimilarity measure adopted by the agent, which searches and takes decisions autonomously for one cluster at a time. Results show that the algorithm is able to discover parameter configurations that yield a consistent and interpretable collection of clusters. Moreover, we demonstrate that our algorithm shows comparable performances with other similar state-of-the-art algorithms when facing specific clustering problems.

Mining Semantics Structures from Syntactic Structures in Web Document Corpora

The Web is making possible many advanced text-mining applications, such as news summarization, essay grading, question answering, semantic search and structured queries on corpora of Web documents. For many of such applications, statistical text-mining techniques are of limited effectiveness since they do not utilize the morphological structure of the text. On the other hand, many approaches use NLP-based techniques that parse the text into parse trees, and then use patterns to mine and analyze parse trees which are often unnecessarily complex. To reduce this complexity and ease the entire process of text mining, we propose a weighted-graph representation of text, called TextGraphs, which captures the grammatical and semantic relations between words and terms in the text. TextGraphs are generated using a new text mining framework which is the main focus of this paper. Our framework, SemScape, uses a statistical parser to generate few of the most probable parse trees for each sentence and employs a novel two-step pattern-based technique to extract from parse trees candidate terms and their grammatical relations. Moreover, SemScape resolves coreferences by a novel technique, generates domain-specific TextGraphs by consulting ontologies, and provides a SPARQL-like query language and an optimized engine for semantically querying and mining TextGraphs.

Spectral Graph Analysis for Process Monitoring

Process monitoring is a fundamental task to support operator decisions under abnormal situations. In this article, spectral graph analysis monitoring (SGAM) is introduced. The approach is based on the spectral graph analysis theory. First, a weighted graph representation of process measurements is developed. Second, the process behavior is parametrized by means of graph spectral features, in particular, the algebraic connectivity and the spectral energy. The developed methodology is illustrated in autocorrelated and nonlinear synthetic cases and applied to the well-known Tennessee Eastman process benchmark with promising results.

A graph-based technique for semi-supervised segmentation of 3D surfaces

A wide range of cheap and simple to use 3D scanning devices has recently been introduced in the market. These tools are no longer addressed to research labs and highly skilled professionals, but rather, they are mostly designed to allow inexperienced users to acquire surfaces and whole objects easily and independently. In this scenario, the demand for automatic or semi-automatic algorithms for 3D data processing is increasing. In this paper we address the task of segmenting the acquired surfaces into perceptually relevant parts. Such a problem is well known to be ill-defined both for 2D images and 3D objects, as even with a perfect understanding of the scene, many different and incompatible semantic or syntactic segmentations can exist together. For this reason recent years have seen a great research effort into semi-supervised approaches, that can make use of small bits of information provided by the user to attain better accuracy. We propose a semi-supervised procedure that exploits an initial set of seeds selected by the user. In our framework segmentation happens by propagating part labels over a weighted graph representation of the surface directly derived from its triangulated mesh. The assignment of each element is driven by a greedy approach that accounts for the curvature between adjacent triangles. The proposed technique does not require to perform edge detection or to fit parametrized surfaces and its implementation is very straightforward. Still, despite its simplicity, tests made on a standard database of scanned 3D objects show its effectiveness even with moderate user supervision.

Ontology integration to identify protein complex in protein interaction networks

BackgroundProtein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms.MethodsWe have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes.ResultsThe algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches.