Networks Of Different Sizes Research Articles

A network sensor location problem for link flow observability and estimation

Effective traffic sensor systems can collect precise and reliable real-time traffic information to improve the performance of traffic guidance and traffic flow control. In particular, link flow data are among the most intuitive sources of information for reflecting road congestion and can be further used to estimate origin-destination (OD) and path demands. To efficiently obtain link flow information, much effort has been devoted to the network sensor location problem for link flow observability and estimation. The objective of the observability problem is to minimize the number of installed sensors while ensuring full link observability, whereas the objective of the link flow estimation problem is to maximize the information gain or minimize the estimation deviations given a limited number of sensors. To the best of our knowledge, our study is the first to integrate link flow observability, flow estimation and the impact of prior link flow uncertainty into the sensor location problem. Accordingly, we propose two new models. In detail, we first present a new mixed-integer optimization model to achieve full link flow observability. Since only topological information is used, this is a generalized version of the existing models as well as a basic representation of other models for the flow estimation problem in our study. The second model integrates linear regression into the traffic sensor location problem. The results of this model provide both location selection and estimation methods for the decision maker. Furthermore, to address the problems of data loss and low computational efficiency in large-scale networks, we exploit the concepts of ambiguity sets and linear decision rules in robust optimization to generate a tractable approximate counterpart to the above sample-based link estimation model. Sample information is further integrated into this tractable approximate model to enhance the simulation performance without incurring a loss in computational efficiency. Numerical experiments are conducted for networks of different sizes. The results show significant improvement in surveillance performance, with a significant reduction in the number of required sensors and good estimation performance. Our work also verifies that when the number of samples is small, compared with the sample-based model, our enhanced tractable approximate model has several advantages, including (i) better out-of-sample performance, (ii) a similar noninferred link distribution, (iii) less dependence on sample size, and (iv) lower computation time requirements for large-scale problems.

Complex networks of interacting stochastic tipping elements: Cooperativity of phase separation in the large-system limit.

Tipping elements in the Earth system have received increased scientific attention over recent years due to their nonlinear behavior and the risks of abrupt state changes. While being stable over a large range of parameters, a tipping element undergoes a drastic shift in its state upon an additional small parameter change when close to its tipping point. Recently, the focus of research broadened towards emergent behavior in networks of tipping elements, like global tipping cascades triggered by local perturbations. Here, we analyze the response to the perturbation of a single node in a system that initially resides in an unstable equilibrium. The evolution is described in terms of coupled nonlinear equations for the cumulants of the distribution of the elements. We show that drift terms acting on individual elements and offsets in the coupling strength are subdominant in the limit of large networks, and we derive an analytical prediction for the evolution of the expectation (i.e., the first cumulant). It behaves like a single aggregated tipping element characterized by a dimensionless parameter that accounts for the network size, its overall connectivity, and the average coupling strength. The resulting predictions are in excellent agreement with numerical data for Erdös-Rényi, Barabási-Albert, and Watts-Strogatz networks of different size and with different coupling parameters.

Node2vec with weak supervision on community structures

• A weakly supervised node embedding algorithm. • Communities detected by combinatorial approaches used as weak labels for learning the embedding objective. • Reduces distances between node pair vectors that are likely to belong to the same community over the ones that do not. • Demonstrated to be effective on both arti cial and real-world networks. Detecting communities or the modular structure of real-life networks (e.g. a social network or a product purchase network) is an important task because the way a network functions is often determined by its communities. Traditional approaches to community detection involve modularity-based algorithms, which generally speaking, construct partitions based on heuristics that seek to maximize the ratio of the edges within the partitions to those between them. On the other hand, node embedding approaches represent each node in a graph as a real-valued vector and is thereby able to transform the problem of community detection in a graph to that of clustering a set of vectors. Existing node embedding approaches are primarily based on, first, initiating random walks from each node to construct a context of a node, and then make the vector representation of a node close to its context. However, standard node embedding approaches do not directly take into account the community structure of a network while constructing the context around each node. To alleviate this, we propose a community structure aware node embedding approach, where we incorporate an initial combinatorial approach-based partition information into the objective function of node embedding. We demonstrate that our proposed combination of the combinatorial and the embedding approaches for community detection outperforms a number of combinatorial-based baselines on a wide range of real-life and synthetic networks of different sizes and densities.

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

TemporalRI: subgraph isomorphism in temporal networks with multiple contacts

Temporal networks are graphs where each edge is associated with a timestamp denoting when two nodes interact. Temporal Subgraph Isomorphism (TSI) aims at retrieving all the subgraphs of a temporal network (called target) matching a smaller temporal network (called query), such that matched target edges appear in the same chronological order of corresponding query edges. Few algorithms have been proposed to solve the TSI problem (or variants of it) and most of them are applicable only to small or specific queries. In this paper we present TemporalRI, a new subgraph isomorphism algorithm for temporal networks with multiple contacts between nodes, which is inspired by RI algorithm. TemporalRI introduces the notion of temporal flows and uses them to filter the search space of candidate nodes for the matching. Our algorithm can handle queries of any size and any topology. Experiments on real networks of different sizes show that TemporalRI is very efficient compared to the state-of-the-art, especially for large queries and targets.

Modeling and Prediction of Daily Traffic Patterns—WASK and SIX Case Study

The paper studies efficient modeling and prediction of daily traffic patterns in transport telecommunication networks. The investigation is carried out using two historical datasets, namely WASK and SIX, which collect flows from edge nodes of two networks of different size. WASK is a novel dataset introduced and analyzed for the first time in this paper, while SIX is a well-known source of network flows. For the considered datasets, the paper proposes traffic modeling and prediction methods. For traffic modeling, the Fourier Transform is applied. For traffic prediction, two approaches are proposed—modeling-based (the forecasting model is generated based on historical traffic models) and machine learning-based (network traffic is handled as a data stream where chunk-based regression methods are applied for forecasting). Then, extensive simulations are performed to verify efficiency of the approaches and their comparison. The proposed modeling method revealed high efficiency especially for the SIX dataset, where the average error was lower than 0.1%. The efficiency of two forecasting approaches differs with datasets–modeling-based methods achieved lower errors for SIX while machine learning-based for WASK. The average prediction error for SIX reached 3.36% while forecasting for WASK turned out extremely challenging.

LA-Trickle: A novel algorithm to reduce the convergence time of the wireless sensor networks

Wireless sensor networks (WSNs) are very important to realize the Internet of Things by connecting physical objects to the Internet. RPL (Routing Protocol for Low Power and Lossy Networks) has been provided by the IETF (Internet Engineering Task Force) for routing on wireless sensor networks. RPL uses the Trickle algorithm to schedule the transmission of control messages. The environment of WSNs is often highly variable and the environmental conditions for nodes are different. If network nodes are equipped with a learning automaton, network convergence time can be reduced. The use of learning automata allows each network node to adjust its parameters to environmental conditions by receiving environmental feedback to perform better. The main aim of this article is to reduce the network convergence time. We equipped the Trickle algorithm with a learning automaton, which determines how many times the algorithm is repeated with the minimum interval to resolve the inconsistency, based on environmental conditions. Since repeating the Trickle algorithm with the minimum interval increases the local repair speed and reduces the network convergence time. We simulated a large number of networks of different sizes and densities. According to the simulation results, we observed that in the proposed method, the network convergence time was reduced compared with the other methods. Also, due to fewer changes in network topology, energy consumption for network reconstruction was reduced.

Protected area networks do not represent unseen biodiversity

Most existing protected area networks are biased to protect charismatic species or landscapes. We hypothesized that conservation networks designed to include unseen biodiversity—species rich groups that consist of inconspicuous taxa, or groups affected by knowledge gaps—are more efficient than networks that ignore these groups. To test this hypothesis, we generated species distribution models for 3006 arthropod species to determine which were represented in three networks of different sizes and biogeographic origin. We assessed the efficiency of each network using spatial prioritization to measure its completeness, the increment needed to achieve conservation targets, and its specificity, the extent to which proposed priority areas to maximize unseen biodiversity overlap with existing networks. We found that the representativeness of unseen biodiversity in the studied protected areas, or extrinsic representativeness, is low, with ~ 40% of the analyzed unseen biodiversity species being unprotected. We also found that existing networks should be expanded ~ 26% to 46% of their current area to complete targets, and that existing networks do not efficiently conserve the unseen biodiversity given their low specificity (as low as 8.8%) unseen biodiversity. We conclude that information on unseen biodiversity must be included in systematic conservation planning approaches to design more efficient and ecologically representative protected areas.

Fictitious Component Free - Pressure Deficient Network Algorithm for Water Distribution Network with Variable Minimum and Required Pressure-Heads

The pressure-driven analysis is essential for modelling the pressure deficient condition of water distribution networks. Owing to the complexity, the development of pressure-driven analysis algorithms remains a grey area over the past few decades. As a milestone, EPANET 2.0 got upgraded to EPANET 2.2 with the inclusion of pressure-driven analysis. However, EPANET 2.2 has a shortcoming of using only a single value for the parameters, minimum pressure-head and required pressure-head of the demand nodes. Ironically, when a water distribution network serves for a wide area or during a fire-fighting period, the demand nodes have variable values for each of these parameters. To address this, a new method named Fictitious Component Free - Pressure Deficient Network Algorithm (FCF-PDNA) is proposed in this paper. The FCF-PDNA does not require any additional fictitious components. It can be used for both steady-state and extended period simulations. The proposed method is applied to water distribution networks of different sizes and types and confirmed that the FCF-PDNA could deal with variable values of minimum and required pressure-head. The suitability of this method for different scenarios that will lead to pressure deficient conditions is also analysed and verified. Moreover, the computational time taken by the FCF-PDNA and the EPANET 2.2 pressure-driven analysis is similar.

QoSA-ICN: An information-centric approach to QoS in vehicular environments

Heterogeneous content-based traffic distribution motivates Information-Centric Networking (ICN), where content delivery is of primary interest as a prominent solution. However, current work does not address Quality of Service (QoS) provisioning for prioritized traffic, which is required for different applications and content types. This paper extends ICN with data delivery deadline awareness and shapes the forwarding decisions to ensure prioritized packet treatment. The proposed QoS Aware-ICN (QoSA-ICN) classifies requests' priority with their QoS requirements by codifying a QoSInfo object in interest/data packets. QoSA-ICN also extends the existing NDN transmission mode to a converged best route with multi-hop multi-route forwarding, to avoid the overhead of pre-building routes in rapidly changing and dynamic environments. Furthermore, this work expands the network traffic control mechanism to optimally manage the potential impact of QoS on network congestion control. Extensive simulations results show a 3.5x and 1.2x improvement in data delivery time; and 13.5x and 4.6x reduction in packet loss when compared to DRSC-based native IP and NDN approaches, in networks of different sizes. Through simulations, we also found that QoSA-ICN reduces overall content retrieval time and packet loss while achieving a high ratio of successfully-received priority packets as compared to the existing techniques T-Move, MAP-Me and pNDN.

Compatible Influence Maximization in Online Social Networks

Influence maximization, which aims to find a small number of influencers in a social network to maximize the influence spread under a certain propagation model, has attracted substantial attention due to its widespread applications, such as viral marketing and social advertising. However, most of the former studies focus primarily on maximizing the influence spread of a single product, which is not very common in actual marketing campaigns. In this article, we study a novel compatible influence maximization problem for two considered products, which involves more complex product adoption decisions of users in many realistic settings. The problem is NP-hard, and the objective function no longer exhibits monotonicity and submodularity. We propose an adapted greedy algorithm to solve the problem effectively. Due to its poor computational efficiency in the seed selection, we further propose a fast greedy algorithm that integrates several effective optimization strategies without compromising the accuracy and devise an efficient heuristic algorithm to approximate the influence spread calculation. Extensive experiments over real-world social networks of different sizes demonstrate the effectiveness and efficiency of the proposed methods.

Modelling community structure and temporal spreading on complex networks

We present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

A Markov model for performance evaluation of channel bonding in IEEE 802.11

The ever-growing popularity of Wireless Local Area Networks (WLANs) in home, public, and work environments is fueling the need for WLANs that can accommodate more stations, each with higher throughput. This typically results in WLANs containing a larger number of heterogeneous devices, making the prediction of the network’s behavior and its efficient configuration an even more elaborate problem. In this paper, we propose a Markovian model that predicts the throughput achieved by each Access Point (AP) of the WLAN as a function of the network’s topology and the AP’s throughput demands. By means of simulation, we show that our model achieves mean relative errors of around 10% for networks of different sizes and with diverse node configurations. The model is adapted to the specification of IEEE 802.11 standards that implement channel bonding, namely 802.11n/ac/ax, and as such it can be used to provide insight into issues of channel assignment when using channel bonding. We derive guidelines on the best practice in static channel bonding given a performance metric and for different node characteristics such as the Modulation and Coding Scheme (MCS) indexes, frame aggregation rates, saturation levels, and network topologies. We then put our findings to the test by identifying the optimal channel bonding combination in an 802.11ac WLAN containing nodes with diverse characteristics. We conclude that the optimal solution is highly dependent on the particular network configuration. However, we find that, in general, larger channels are better suited for throughput maximization and smaller (and separate) channels render higher fairness.

A scalable random walk with restart on heterogeneous networks with Apache Spark for ranking disease-related genes through type-II fuzzy data fusion

One of the effective missions of biology and medical science is to find disease-related genes. Recent research uses gene/protein networks to find such genes. Due to false positive interactions in these networks, the results often are not accurate and reliable. Integrating multiple gene/protein networks could overcome this drawback, causing a network with fewer false positive interactions. The integration method plays a crucial role in the quality of the constructed network. In this paper, we integrate several sources to build a reliable heterogeneous network, i.e., a network that includes nodes of different types. Due to the different gene/protein sources, four gene-gene similarity networks are constructed first and integrated by applying the type-II fuzzy voter scheme. The resulting gene-gene network is linked to a disease-disease similarity network (as the outcome of integrating four sources) through a two-part disease-gene network. We propose a novel algorithm, namely random walk with restart on the heterogeneous network method with fuzzy fusion (RWRHN-FF). Through running RWRHN-FF over the heterogeneous network, disease-related genes are determined. Experimental results using the leave-one-out cross-validation indicate that RWRHN-FF outperforms existing methods. The proposed algorithm can be applied to find new genes for prostate, breast, gastric, and colon cancers. Since the RWRHN-FF algorithm converges slowly on large heterogeneous networks, we propose a parallel implementation of the RWRHN-FF algorithm on the Apache Spark platform for high-throughput and reliable network inference. Experiments run on heterogeneous networks of different sizes indicate faster convergence compared to other non-distributed modes of implementation.

A New Algorithm Based on Dijkstra for Vehicle Path Planning Considering Intersection Attribute

Vehicle path planning is a key issue for car navigation systems. When path planning, considering the time spent at intersections is more in line with the actual situation, so it is of practical significance to study the path planning problem take account intersection attributes. In this article, we study the problem in a deterministic network, taking the minimization of travel time from the origin to the destination as the optimization goal. For this purpose, we construct a mathematical model for the problem. This paper proposes a reverse labeling Dijkstra algorithm (RLDA) based on traditional Dijkstra algorithm to solve the problem, it is proved that the correctness of the RLDA algorithm theoretically, and analyze that the RLDA algorithm has a lower polynomial time complexity. Finally, we selected the actual road network as the simulation experiment object to verify the effectiveness of the algorithm searching for the optimal path. And select 10 groups of networks of different sizes and conduct extensive experiments to compare the convergence efficiency and calculation speed between RLDA and PSO, GA, ACO, NNA, OPABRL. The statistical results show that the convergence rate of the RLDA algorithm is better than that of ACO, NNA, and GA. When the number of network nodes is less than 350, the algorithm has the smallest running time.

An MCDM integrated adaptive simulated annealing approach for influence maximization in social networks

The Influence Maximization (IM) problem aims to identify a small subset of nodes that have the most influence spread in a network. Although it is an NP-hard problem, the continuous increasing size of the social networks leads to a substantially higher computational complexity, and therefore, has motivated numerous researchers to explore better approaches. This paper proposes a multi-criteria decision making (MCDM) based meta-heuristic approach to solve the IM problem in social networks. An MCDM approach is utilized to select candidate nodes by eliminating less influential ones at the preliminary phase which decreases the computation cost. Thereafter, to find the optimal solution, a modified version of Simulated Annealing (SA) with an enhanced search strategy is proposed. The performance of this proposed approach is tested and verified by solving the IM problem on eight real-life social networks of different sizes and types and comparing the results with six benchmark algorithms. The experimental results indicate that the proposed algorithm has a better trade-off between the solution quality and computational run time than the other algorithms.

Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools.

The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.

Traffic flow prediction over muti-sensor data correlation with graph convolution network

Accurate and real-time traffic flow prediction plays an important role in improving the traffic planning capability of intelligent traffic systems. However, traffic flow prediction is a very challenging problem because the spatial–temporal correlation among roads is complex and changeable. Most of the existing methods do not reasonably analyze the dynamic spatial–temporal correlation caused by the changing relationship of traffic patterns among roads, thus cannot get satisfactory results in the medium and long-term traffic prediction. To address these issues, a novel Multisensor Data Correlation Graph Convolution Network model, named MDCGCN, is proposed in this paper. The MDCGCN model consists of three parts: recent, daily period and weekly period components, and each of which consists of two parts: 1) benchmark adaptive mechanism and 2) multisensor data correlation convolution block. The first part can eliminate the differences among the periodic data and effectively improve the quality of data input. The second part can effectively capture the dynamic temporal and spatial correlation caused by the changing relationship of traffic patterns among roads. Through substantial experiments conducted on two real data sets, results indicate that the proposed MDCGCN model can significantly improve the medium and long-term prediction accuracy for traffic networks of different sizes, and is superior to existing prediction methods.

Evolutionary Algorithm for the Electric Vehicle Routing Problem with Battery Degradation and Capacitated Charging Stations

As CO2 emission regulations increase, fleet owners increasingly consider the adoption of Electric Vehicle (EV) fleets in their business. The conventional Vehicle Routing Problem (VRP) aims to find a set of routes to reduce operational costs. However, route planning of EVs poses different challenges than that of Internal Combustion Engine Vehicles (ICEV). The Electric Vehicle Routing Problem (E-VRP) must take into consideration EV limitations such as short driving range, high charging time, poor charging infrastructure, and battery degradation. In this work, the E-VRP is formulated as a Prognostic Decision-Making problem. It considers customer time windows, partial midtour recharging operations, non-linear charging functions, and limited Charge Station (CS) capacities. Besides, battery State of Health (SOH) policies are included in the E-VRP to prevent early degradation of EV batteries. An optimization problem is formulated with the above considerations, when each EV has a set of costumers assigned, which is solved by a Genetic Algorithm (GA) approach. This GA has been suitably designed to decide the order of customers to visit, when and how much to recharge, and when to begin the operation. A simulation study is conducted to test GA performance with fleets and networks of different sizes. Results show that E-VRP effectively enables operation of the fleet, satisfying all operational constraints.

Inverse Percolation to Quantify Robustness in Multiplex Networks

Inverse percolation is known as the problem of finding the minimum set of nodes whose elimination of their links causes the rupture of the network. Inverse percolation has been widely used in various studies of single-layer networks. However, the use and generalization of multiplex networks have been little considered. In this work, we propose a methodology based on inverse percolation to quantify the robustness of multiplex networks. Specifically, we present a modified version of the mathematical model for the multiplex-vertex separator problem (m-VSP). By solving the m-VSP, we can find nodes that cause the rupture of the mutually connected giant component (MCGC) and the large viable cluster (LVC) when their links are removed from the network. The methodology presented in this work was tested in a set of benchmark networks, and as case study, we present an analysis using a set of multiplex social networks modeled with information about the main characteristics of the best universities in the world and the universities in Mexico. The results show that the methodology presented in this work can work in different models and types of 2- and 3-layer multiplex networks without dividing the entire multiplex network into single-layer as some techniques described in the specific literature. Furthermore, thanks to the fact that the technique does not require the calculation of some structural measure or centrality metric, and it is easy to scale for networks of different sizes.