Network Flow Research Articles

Strong long ties facilitate epidemic containment on mobility networks

Abstract The analysis of connection strengths and distances in the mobility network is pivotal for delineating critical pathways, particularly in the context of epidemic propagation. Local connections that link proximate districts typically exhibit strong weights. However, ties that bridge distant regions with high levels of interaction intensity, termed strong long ties, warrant increased scrutiny due to their potential to foster satellite epidemic clusters and extend the duration of pandemics. In this study, strong long ties are identified as outliers on the joint distribution of distance and flow in the mobility network of Shanghai constructed from 1km×1km high-resolution mobility data. We propose a grid-joint isolation strategy alongside a reaction-diffusion transmission model to assess the impact of strong long ties on epidemic propagation. The findings indicate that regions connected by strong long ties exhibit a small spatial autocorrelation and display a temporal similarity pattern in disease transmission. Grid-joint isolation based on strong long ties reduces cumulative infections by an average of 17.1% compared to other types of ties. This work highlights the necessity of identifying and targeting potentially infected remote areas for spatially focused interventions, thereby enriching our comprehension and management of epidemic dynamics.

A New Multicommodity Network Flow Model and Branch and Cut for Optimal Quantum Boolean Circuit Synthesis

This study introduces a new optimization model and a branch-and-cut approach for synthesizing optimal quantum circuits for reversible Boolean functions, which are pivotal components in quantum algorithms. Although heuristic algorithms have been extensively explored for quantum circuit synthesis, research on exact counterparts remains relatively limited. However, the need to design quantum circuits with guaranteed optimality is increasing, especially for improving computational fidelity on noisy intermediate-scale quantum devices. This study presents mathematical optimization as a viable option for optimal synthesis, with the potential to accommodate practical considerations arising in fast-evolving quantum technologies. We set a demonstrative problem to implement reversible Boolean functions using high-level gates known as multiple control Toffoli gates while minimizing a technology-based proxy called quantum cost—the number of low-level gates used to realize each high-level gate. To address this problem, we propose a discrete optimization model based on a multicommodity network and discuss potential future variations at an abstract level to incorporate technical considerations. A customized branch and cut is then developed upon different aspects of our model, including polyhedron integrality, surrogate constraints, and variable prioritization. Our experiments demonstrate the robustness of the proposed approach in finding cost-optimal circuits for all benchmark instances within a two-hour time frame. Furthermore, we present interesting intuitions from these experiments and compare our computational results with relevant studies, highlighting newly discovered circuits with the lowest quantum costs reported in this paper. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Quantum Computing. Funding: This research was supported by the Ministry of Science and Information and Communication Technology, South Korea [Grants 2017R1E1A1A0307098814 and 2020R1A4A307986411]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0562 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0562 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Functional and vascular neuroimaging in maritime pilots with long-term sleep disruption.

The mechanism underlying the possible causal association between long-term sleep disruption and Alzheimer's disease remains unclear Musiek et al. 2015. A hypothesised pathway through increased brain amyloid load was not confirmed in previous work in our cohort of maritime pilots with long-term work-related sleep disruption Thomas et al. Alzheimer's Res Ther 2020;12:101. Here, using functional MRI, T2-FLAIR, and arterial spin labeling MRI scans, we explored alternative neuroimaging biomarkers related to both sleep disruption and AD: resting-state network co-activation and between-network connectivity of the default mode network (DMN), salience network (SAL) and frontoparietal network (FPN), vascular damage and cerebral blood flow (CBF). We acquired data of 16 maritime pilots (56 ± 2.3years old) with work-related long-term sleep disruption (23 ± 4.8 working years) and 16 healthy controls (59 ± 3.3years old), with normal sleep patterns (Pittsburgh Sleep Quality Index ≤ 5). Maritime pilots did not show altered co-activation in either the DMN, FPN, or SAL and no differences in between-network connectivity. We did not detect increased markers of vascular damage in maritime pilots, and additionally, maritime pilots did not show altered CBF-patterns compared to healthy controls. In summary, maritime pilots with long-term sleep disruption did not show neuroimaging markers indicative of preclinical AD compared to healthy controls. These findings do not resemble those of short-term sleep deprivation studies. This could be due to resiliency to sleep disruption or selection bias, as participants have already been exposed to and were able to deal with sleep disruption for multiple years, or to compensatory mechanisms Mentink et al. PLoS ONE. 2021;15(12):e0237622. This suggests the relationship between sleep disruption and AD is not as strong as previously implied in studies on short-term sleep deprivation, which would be beneficial for all shift workers suffering from work-related sleep disruptions.

Security constrained optimal power flow solution for practical transmission grid using hybrid use of generating plant and network restructuring

This paper presented a study on the security constrained optimal power flow (SCOPF) of a practical transmission utility grid network (TUGN). The objectives of reduction in the transmission losses of TUGN (TLGN) and to minimize the bus voltage deviations (BVD) are achieved by addition of thermal power plant (TPP) and network restructuring (NR). Identification of best fit node for generation injection is achieved using the method based on hybrid combination of analytic approach and genetic algorithm (GA). Efficacy of the proposed study is evaluated using the computation of energy equivalent to network loss saving, percentage derating of the load (PDOL), voltage profile, bus voltage deviations, and financial analysis (FA). Study is performed without and with contingency (N-1) for the base case network, TUGN with addition of TPP and TUGN with addition of TPP and NR. This is established that addition of TPP, addition of TPP with NR are effective to reduce the TLGN, improve the voltage profile and minimize the bus voltage deviations. This study is more efficient compared to various studies reported in literature.

A Monte Carlo-based algorithm for the quickest path flow network reliability problem

A Monte Carlo-based algorithm for the quickest path flow network reliability problem

Directional flow in perivascular networks: mixed finite elements for reduced-dimensional models on graphs

Flow of cerebrospinal fluid through perivascular pathways in and around the brain may play a crucial role in brain metabolite clearance. While the driving forces of such flows remain enigmatic, experiments have shown that pulsatility is central. In this work, we present a novel network model for simulating pulsatile fluid flow in perivascular networks, taking the form of a system of Stokes–Brinkman equations posed over a perivascular graph. We apply this model to study physiological questions concerning the mechanisms governing perivascular fluid flow in branching vascular networks. Notably, our findings reveal that even long wavelength arterial pulsations can induce directional flow in asymmetric, branching perivascular networks. In addition, we establish fundamental mathematical and numerical properties of these Stokes–Brinkman network models, with particular attention to increasing graph order and complexity. By introducing weighted norms, we show the well-posedness and stability of primal and dual variational formulations of these equations, and that of mixed finite element discretizations.

Mixed-integer programming models and heuristic algorithms for the maximum value dynamic network flow scheduling problem

Various applications in contested logistics and infrastructure restoration require dynamic flow solutions characterized by a schedule of network flows consecutively transmitted over a sequence of successive periods. For these schedules, we assume flows transmit via arcs during periods while flows reside at nodes from one period to the next. Within this context, we introduce the Maximum Value Dynamic Network Flow Problem (MVDFP) in which we seek to maximize the cumulative value of a non-simultaneous network flow schedule that accumulates node value whenever some minimum amount of flow resides at a node between periods. For solving the MVDFP, we first introduce a large mixed-integer program (MIP). As this MIP can become computationally-expensive for large networks, we present a trio of computationally-effective, easy to implement heuristic approaches that solve a series of smaller, more manageable MIPs. These heuristic approaches typically determine high-quality solutions significantly faster than the MIP obtains an optimal solution by dividing the full network flow schedule into a sequence of consecutive shorter network flow subschedules. In many cases, at least one of our heuristic approaches produces an optimal solution in a fraction of the MIP’s computational time. We present extensive computational results to highlight our heuristics’ efficacy, discuss for what instances each approach may be most applicable, and detail future research avenues.

Temporal asynchrony analysis for dynamic operation of hydraulic-thermal-electricity multiple energy networks based on holomorphic embedding method

Analyzing the operational states of multiple energy networks (MEN) in multi-energy systems is crucial for ensuring system stability. The dynamic operational characteristics of different energy flows pose challenges for computational analysis. Traditional steady-state methods are inadequate for addressing the dynamics of MEN, especially when dealing with temporal discrepancies between hydraulic and thermal flows in thermal networks (TN) and the heterogeneity between TN and electrical networks. Therefore, this paper proposes a novel holomorphic embedding method (HEM) based on multi-stage decomposition method. The developed HEM constructs a time coefficient matrix and utilize inner-outer loop recursion to handle the time lag between thermal flow and hydraulic flow in the TN. Additionally, we reconstruct a holomorphic matrix, integrating hydraulic flow to bridge thermal and electric power flows, thereby improving the operational heterogeneity among different networks. Real-case simulations show that when the Taylor expansion order in HEM is equal to 4, the proposed method achieves a mere 1% discrepancy from actual operational data, enhancing computational efficiency by 60% compared to the Newton-Raphson method. Moreover, in this real-case, the TN exhibits a maximum delay response time of 180s compared to electrical networks. Exploiting this delay time effectively increases renewable energy generation within multi-energy systems by 961.58kW per day.

Analyzing Single-Valued Neutrosophic Fuzzy Graphs Through Matroid Perspectives

We hope to present this paper on the emergence of a novel category of matroids derived from single-valued neutrosophic (SVN) fuzzy-graphs. The findings of this study make a substantial contribution to both matroid theory and the field of neutrosophic systems. In this study, we aim to introduce the concept of SVN-matroids and conduct a thorough investigation into their fundamental properties. Additionally, we explore the essential requirements for establishing duality within the realm of matroids formed from neutrosophic fuzzy-networks. This investigation encompasses co-weak isomorphism, weak isomorphism, and isomorphism between two matroids. Furthermore, our research proposes the expansion of other graph products and the creation of a SVN-graph to utilize the Greedy and Kruskal algorithms for determining optimal productivity in an information flow of a single-source network within a SVN-network. The application of neutronosophic graph theory offers a paradigm for addressing optimization problems in chemical graph theory, such as identifying the maximal independent set and a minimum spanning tree. When faced with uncertainty, neutrosophic optimization provides more reliable solutions by considering the uncertainties associated with the objective function or constraints.

CME Velocity Field Calculation Model Based on an Unsupervised Transformer Optical Flow Network

The optical flow algorithm (OF) is one the main methods for calculating image velocity field and has many applications in space weather. Most OF calculations are applied to the motion of labeled rigid objects and are not suitable for velocity detection of high-energy particles, such as in a coronal mass ejection (CME). Fluctuations in exposure time and the influence of space weather will lead to inconsistent brightness of the same feature point at different times. To address this problem, we propose an unsupervised multiscale optical flow network based on Vision Transformer, named UTFlowNet. The network comprises a multiscale feature extraction module and a coarse-to-fine global optical flow calculation module. The movement of high-energy particles emitted during a CME eruption follows certain physical rules. Therefore, we apply fluid motion–based loss functions to analyze the motion of high-energy particles more effectively, addressing the problem of CME motion field extraction. Our method can be applied to the real-time automatic extraction of a CME’s velocity field and performs well with inconsistent brightness, large-scale motion, and strong CME noise. Additionally, we can estimate subpixel level fine-grained velocity. Our model may be affected by overfitting during cross–data set inference, so we encourage performing a small amount of transfer learning on new data sets to mitigate this issue. In order to verify the accuracy of our method, we conducted experiments and verification on the Solar and Heliospheric Observatory LASCO C2 data and the High Altitude Observatory MLSO data. We constructed a large-scale displacement simulation data set based on LASCO C2 data and tested on it, achieving the best results.

Realization of Integrated Regional Ecological Management Based on Ecosystem Service Supply and Demand Flow Networks: An Example from a Dominant Mineral Resources Development Area

Understanding the flow processes and pattern optimization of ecosystem services (ESs) supply and demand is crucial for integrated regional ecological management. However, the understanding of the flow process of ESs at the 1 km grid scale is still limited, especially in areas dominated by mineral resource development. The landscape in these areas has undergone significant changes due to mining activities. It is urgent to construct a regional management model that integrates the flow of ecosystem services and mine restoration. This study developed a framework that links ecosystem service flows (ESFs) and ecological security patterns (ESP) based on multi-source ecological monitoring data, constructed an ES supply-demand flow network through the flow properties, and determined the sequence and optimization strategies for mine rehabilitation to achieve integrated regional management. The results show that, except for food production (FP), other services were in surplus overall, mostly in synergistic relationships, but the spatial distribution of their supply and demand was not coordinated. Surplus areas were located mainly in the eastern woodlands, and deficit areas were located in the northwestern production agglomeration centers, suggesting that areas of supply-demand imbalance can be mitigated through ecological integration. Among these, water yield (WY) had a small number of sources and sinks and is limited in area range. Habitat quality (HQ) sources and sinks had the largest area coverage and the highest number. The distribution of ESF corridors, influenced by factors such as the number of sources and sinks, flow characteristics, and spatial resistance, varied significantly. HQ exhibited a more uniform distribution range, while WY had a longer average length of flow path. Overlaying ecological and mining factors, we identified ecological strategic spots, important supply areas, beneficiary areas, and mine priority restoration areas to further optimize the overall layout and rationally allocate the intrinsic structure of the patches based on ES supply and demand.

A survey on feature extraction and learning techniques for link prediction in homogeneous and heterogeneous complex networks

Complex networks are commonly observed in several real-world areas, such as social, biological, and technical systems, where they exhibit complicated patterns of connectedness and organised clusters. These networks have intricate topological characteristics that frequently elude conventional characterization. Link prediction in complex networks, like data flow in telecommunications networks, protein interactions in biological systems, and social media interactions on platforms like Facebook, etc., is an essential element of network analytics and presents fresh research challenges. Consequently, there is a growing emphasis in research on creating new link prediction methods for different network applications. This survey investigates several strategies related to link prediction, ranging from feature extraction based to feature learning based techniques, with a specific focus on their utilisation in dynamic and developing network topologies. Furthermore, this paper emphasises on a wide variety of feature learning techniques that go beyond basic feature extraction and matrix factorization. It includes advanced learning-based algorithms and neural network techniques specifically designed for link prediction. The study also presents evaluation results of different link prediction techniques on homogeneous and heterogeneous network datasets, and provides a thorough examination of existing methods and potential areas for further investigation.

Rethinking max-min planning on energy-efficient software-defined networking for 5G networks.

Energy efficiency plays an important role in intelligent networking for 5G networks, which concerns environmental, financial, and performance aspects of intelligent networking for 5G networks. To this end, network designers propose energy-efficient approaches to reduce energy consumption of networks and to raise network performance by switching off the links/nodes with low loads or at idle status. The existing energy-efficient approaches can be formulated as a max-min optimal problem, namely maximizing network/node/port throughput via minimum energy consumption. The max-min planning investigates energy efficiency only from the links/nodes perspective. The max-min planning for energy-efficient networking, if not carefully designed from the network-wide standpoint, can lead to lower energy efficiency for the whole network due to lack of global planning, which in turn results in the degraded performance due to network un-connectivity after closing the nodes/links. In this paper we rethink the max-min planning framework on energy-efficient software-defined networking for intelligent networking of 5G networks, which takes in account combining network connectivity and maximum network flow with minimum energy consumption. Our framework aims at how to deliver dynamic end-to-end traffic demands with the appropriate network topology by building data forwarding plane with maximum network flow and control plane with network connectivity. We discuss the associated challenges and implementation issues. A dynamic max-min planning framework depending on dynamic end-to-end traffic demands is presented to achieve network-wide energy efficiency. Numerical results show the improved energy efficiency performance for the whole network.

Dynamic multi-scale spatial-temporal graph convolutional network for traffic flow prediction

This paper proposes a dynamic multi-scale spatial-temporal graph convolutional network (DS-STGCN) for traffic flow prediction. The network aims to comprehensively extract global and local dependencies in dynamic spatial-temporal data by inputting traffic network flow data to construct node feature graphs, topology graphs, and time slot feature graphs, capturing the complexity and dynamics of traffic flow. DS-STGCN interprets feature information of the traffic network from both spatial and temporal dimensions through dynamic multi-scale graph convolutional blocks. In the spatial dimension, these blocks use constraints at different levels to balance fine-grained local features and extensive global features, revealing the intrinsic structure of traffic flow data. In the temporal dimension, these blocks jointly learn with temporal convolutional blocks to capture multi-frequency time patterns and handle long sequence data, effectively extracting potential dependencies of time series. Furthermore, DS-STGCN effectively models the changing spatial-temporal relationships in road network flow by constructing dynamically adaptive updated adjacency tensors, generating dynamic graph structures to address the challenge of changing spatial-temporal relationships in the transportation system. Experimental results show that our method significantly outperforms other competing methods on five real traffic datasets (PEMS03, PEMS04, PEMS07, PEMS08 and METR-LA).

Distributed network flows generate localized category selectivity in human visual cortex.

A central goal of neuroscience is to understand how function-relevant brain activations are generated. Here we test the hypothesis that function-relevant brain activations are generated primarily by distributed network flows. We focused on visual processing in human cortex, given the long-standing literature supporting the functional relevance of brain activations in visual cortex regions exhibiting visual category selectivity. We began by using fMRI data from N = 352 human participants to identify category-specific responses in visual cortex for images of faces, places, body parts, and tools. We then systematically tested the hypothesis that distributed network flows can generate these localized visual category selective responses. This was accomplished using a recently developed approach for simulating - in a highly empirically constrained manner - the generation of task-evoked brain activations by modeling activity flowing over intrinsic brain connections. We next tested refinements to our hypothesis, focusing on how stimulus-driven network interactions initialized in V1 generate downstream visual category selectivity. We found evidence that network flows directly from V1 were sufficient for generating visual category selectivity, but that additional, globally distributed (whole-cortex) network flows increased category selectivity further. Using null network architectures we also found that each region's unique intrinsic "connectivity fingerprint" was key to the generation of category selectivity. These results generalized across regions associated with all four visual categories tested (bodies, faces, places, and tools), and provide evidence that the human brain's intrinsic network organization plays a prominent role in the generation of functionally relevant, localized responses.

Spatio-temporal instabilities of blood flow in a model capillary network

Spatio-temporal instabilities of blood flow in a model capillary network

Network analysis of aggregated money flows in stock markets

We introduce the formation of a network of money flows between assets in stock markets, which captures directed relations between assets in terms of how investors have re-allocated money in the stock exchange. Our approach is based on identifying a directed link, or money flow, that occurs when an investor funds a purchase of an asset by selling another asset(s). We extract investor-level money flow networks on daily basis from shareholder registration data, which are then aggregated for both financial institutional and retail investors. Overall, we have a time series of 877 daily networks from 2006 to 2009, which is exceptionally long data on temporal networks. Through our analysis of non-reciprocated triadic patterns in the aggregated money-flow networks, we find that these patterns are both recurrent and significant. However, they are not related to the 2008 financial crisis. Additionally, we observe that the counts on different triadic motifs exhibit not only an autoregressive process but are also interconnected contemporaneously and dynamically. These findings suggest the need for further research using sophisticated network models to provide a comprehensive representation of money flows in stock markets.

Network analysis of correlations between suicide exposure, depression, and anxiety symptoms in adolescents.

Adolescents with a history of suicide exposure, defined as experiencing the suicide death of a family member, friend, or other acquaintances, are more likely to experience mental health issues such as depression or anxiety. This study aimed to explore prevalence rates and the network of adolescents' suicide exposure, depression, and anxiety symptoms, and to clarify the correlations between suicide exposure and symptoms of depression and anxiety. A total of 8,957 adolescents were included in this cross-sectional study. Data regarding general information, symptoms of depression and anxiety, and suicide exposure were collected from mid-September to early October 2021. Network analysis was employed to assess relationships between suicide exposure and individual symptoms of both depression and anxiety. Central symptoms were identified by strength; the flow network was visualized to identify symptoms directly related to suicide exposure. The prevalence rates of suicide exposure, depression, and anxiety were 5.28%, 12.87%, and 10.48%. Results indicated that suicide exposure was associated with both depression and anxiety, and had the strongest positive association with suicidal ideation. Central symptoms of the network were sad mood, nervousness, fatigue, irritability, and uncontrollable worry. Bridge symptoms were suicidal ideation and irritability. Appetite changes, suicidal ideation, uncontrollable worry, sleep difficulties, and irritability were symptoms directly related to suicide exposure. There were significant inter-symptom associations between suicide exposure, depression, and anxiety in adolescents. It is recommended that future studies explore whether targeted interventions and long-term monitoring concerning these inter-symptom associations can protect adolescents with suicide exposure.

SPDepth: Enhancing Self-Supervised Indoor Monocular Depth Estimation via Self-Propagation

Due to the existence of low-textured areas in indoor scenes, some self-supervised depth estimation methods have specifically designed sparse photometric consistency losses and geometry-based losses. However, some of the loss terms cannot supervise all the pixels, which limits the performance of these methods. Some approaches introduce an additional optical flow network to provide dense correspondences supervision, but overload the loss function. In this paper, we propose to perform depth self-propagation based on feature self-similarities, where high-accuracy depths are propagated from supervised pixels to unsupervised ones. The enhanced self-supervised indoor monocular depth estimation network is called SPDepth. Since depth self-similarities are significant in a local range, a local window self-attention module is embedded at the end of the network to propagate depths in a window. The depth of a pixel is weighted using the feature correlation scores with other pixels in the same window. The effectiveness of self-propagation mechanism is demonstrated in the experiments on the NYU Depth V2 dataset. The root-mean-squared error of SPDepth is 0.585 and the δ1 accuracy is 77.6%. Zero-shot generalization studies are also conducted on the 7-Scenes dataset and provide a more comprehensive analysis about the application characteristics of SPDepth.

Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learning

Transitions between free-surface and pressurized flows, known as transient mixed flows, have posed significant challenges in urban drainage systems (UDS), e.g., pipe bursts, road collapses, and geysers. However, traditional mechanistic modeling for mixed flows is challenged by the difficult integration of multi-source data, complex equation forms for the discovery of dynamic processes, and high computational demands. In response, we proposed a data-driven model, TMF-PINN, which utilizes a Physics-Informed Neural Network (PINN) to simulate and invert Transient Mixed Flow (TMF) in sewer networks. This model integrates experimental data, simulation results and Partial Differential Equations (PDEs) into its loss function, leveraging the extensive data available in smart urban water systems. A status factor (α) has been introduced to seamlessly link open channel and pressurized flow dynamics, facilitating rapid adjustments in wave speed. On this basis, Fourier feature extraction and quadratic neural networks have been employed to capture complex dynamic processes featuring high-frequency. Validation through three classical cases using the Storm Water Management Model (SWMM) and comparisons with finite volume Harten-Lax-van Leer (HLL) solver reveal that the proposed model circumvents the constraints of spatiotemporal resolution, yielding accurate flow field predictions.