Edge Removal Research Articles

Investigation of the risk influential factors of maritime accidents: A novel topology and robustness analytical framework

This study aims to develop a novel and fully data-driven approach to analyse the maritime accidents risk influential factors (RIFs) by integrating Association Rule Mining (ARM) and Complex Network (CN) modelling. Firstly, a comprehensive dataset comprising 21,206 maritime accident records from Marine Accident Investigation Branch and Transportation Safety Board is collected and processed to serve as the foundational data source supporting the development of the new approach. Secondly, a novel Combined Association Rule Mining method is proposed to extract the interconnections among RIFs, with the mined results mapped into a CN framework. Finally, two importance ranking algorithms, namely the PageRank-Information-Entropy algorithm and edge betweenness centrality, are applied to identify the key RIFs and their information transmission paths. By simulating deliberate and random attacks within this network, a robustness analysis is conducted to further explore the evolution of RIFs. The findings reveal that ship-related factors demonstrate greater centrality and connectivity, exerting a more substantial influence on information propagation within the network structure. The robustness analysis illustrates that strategic node and edge removals are effective in preventing risk propagation. It therefore makes contributions to the development of a theoretical basis for stakeholders to develop cost-effective preventive measures against specific RIFs, ultimately enhancing maritime safety.

Quantifying the reciprocal impacts of capital and logistics networks in the supply chains: A cyber–physical system approach

Capital flow and logistics are closely linked in the supply chain. Current research primarily assesses their independent resilience during disruptions, but this study reveals an iterative bidirectional impact. The interconnection of shareholding, mutual guarantees, and borrowing within the supply chain has created financial relationships that were previously nonexistent. However, the evolution of logistics has not aligned with these financial developments, leading to a growing heterogeneity between capital flow and logistics. While the heterogeneity between capital flow and logistics within supply chains is crucial, previous studies, especially those rooted in complex networks, often overlook its impact. Drawing inspiration from the cyber–physical system, this paper abstracts logistics and capital flow into respective networks. A model of mutual dependence quantifies reciprocal impacts between capital and logistics networks, exploring the consequences of heterogeneity on the supply chain. Simulation results are compared with traditional single-layer network models. Finally, an innovative edge removal method is proposed to enhance supply chain robustness. Our simulation results indicate that the influence of the capital network on the logistics network and their relationship to heterogeneity is nonlinear. The edge removal method enhances supply chain network robustness in a nonlinear manner, with optimal effectiveness achieved at p_l=0.2.

Mitigating critical nodes in brain simulations via edge removal

Brain simulation holds promise for advancing our comprehension of brain mechanisms, brain-inspired intelligence, and addressing brain-related disorders. However, during brain simulations on high-performance computing platforms, the sparse and irregular communication patterns within the brain can lead to the emergence of critical nodes in the simulated network, which in turn become bottlenecks for inter-process communication. Therefore, effective moderation of critical nodes is crucial for the smooth conducting of brain simulation. In this paper, we formulate the routing communication problem commonly encountered in brain simulation networks running on supercomputers. To address this issue, we firstly propose the Node-Edge Centrality Addressing Algorithm (NCA) for identifying critical nodes and edges, based on an enhanced closeness centrality metric. Furthermore, drawing on the homology of spikes observed in biological brains, we develop the Edge Removal Transit Algorithm (ERT) to reorganize sparse and unbalanced inter-process communication in brain simulation, thereby diminishing the information centrality of critical nodes. Through extensive simulation experiments, we evaluate the performance of the proposed communication scheme and find that the algorithm accurately identifies critical nodes with a high accuracy. Our simulation experiments on 1600 GPU cards demonstrate that our approach can reduce communication latency by up to 25.4%, significantly shortening simulation time in large-scale brain simulations.

Resilience of code compliant reinforced concrete buildings to progressive collapse: A numerical analysis investigation

This study investigates the resilience of dual-system reinforced concrete (RC) structures impacted by progressive collapse where the local failure of a primary structural component leads to the sequential collapse of adjoining elements. The study is conducted on buildings designed in the United Arab Emirates in the context of extreme events. Nonlinear dynamic analysis of twenty-seven different building models is performed to evaluate the impact of critical variables, building height, and column spacing on progressive collapse potential under the Unified Facilities Criteria (UFC) regulations. The research methodology aims to study impacts associated with sudden removal of critical columns (corner, edge, and internal) that replicate severe accident scenarios in buildings. The structural responses were assessed based on the ASCE 41 performance design criteria. The formation of plastic hinges was analyzed to determine the ductility and resilience of the structures. The study finds that buildings with removed corner columns exhibit more significant vertical displacements than those with edge or internal column removals. The results also indicate that columns adjacent to the removed ones generally remained elastic and within their structural capacities, showing structural resilience to localized damage. However, the flexural strength of RC flat slabs near removed or adjacent columns demonstrates insufficient flexural capacities. This study underscores the imperative for enhanced design strategies and emphasizes the critical importance of bolstering structural resilience against extreme events. Moreover, it intends to draw the attention of policymakers locally to the need to consider progressive collapse when designing reinforced concrete structures.

Quantifying the role of supernatural entities and the effect of missing data in Irish sagas

For over a decade, complex networks have been applied to mythological texts in order to quantitatively compare them. This has allowed us to identify similarities between texts in different cultures, as well as to quantify the significance of some heroic characters. Analysing a full mythology of a culture requires gathering data from many individual myths which is time consuming and often impractical. In this work, we attempt to bypass this by analysing the network of characters in a dictionary of mythological characters. We show that the top characters identified by different centrality measures are consistent with central figures in the Irish sagas. Although much of Irish mythology has been lost, we demonstrate that these most central characters are highly robust to a large random removal of edges.

Automated confidence estimation in deep learning auto-segmentation for brain organs at risk on MRI for radiotherapy.

We have built a novel AI-driven QA method called AutoConfidence (ACo), to estimate segmentation confidence on a per-voxel basis without gold standard segmentations, enabling robust, efficient review of automated segmentation (AS). We have demonstrated this method in brain OAR AS on MRI, using internal and external (third-party) AS models. Thirty-two retrospectives, MRI planned, glioma cases were randomly selected from a local clinical cohort for ACo training. A generator was trained adversarialy to produce internal autosegmentations (IAS) with a discriminator to estimate voxel-wise IAS uncertainty, given the input MRI. Confidence maps for each proposed segmentation were produced for operator use in AS editing and were compared with "difference to gold-standard" error maps. Nine cases were used for testing ACo performance on IAS and validation with two external deep learning segmentation model predictions [external model with low-quality AS (EM-LQ) and external model with high-quality AS (EM-HQ)]. Matthew's correlation coefficient (MCC), false-positive rate (FPR), false-negative rate (FNR), and visual assessment were used for evaluation. Edge removal and geometric distance corrections were applied to achieve more useful and clinically relevant confidence maps and performance metrics. ACo showed generally excellent performance on both internal and external segmentations, across all OARs (except lenses). MCC was higher on IAS and low-quality external segmentations (EM-LQ) than high-quality ones (EM-HQ). On IAS and EM-LQ, average MCC (excluding lenses) varied from 0.6 to 0.9, while average FPR and FNR were ≤0.13 and ≤0.21, respectively. For EM-HQ, average MCC varied from 0.4 to 0.8, while average FPR and FNR were ≤0.37 and ≤0.22, respectively. ACo was a reliable predictor of uncertainty and errors on AS generated both internally and externally, demonstrating its potential as an independent, reference-free QA tool, which could help operators deliver robust, efficient autosegmentation in the radiotherapy clinic.

Type-II Apollonian network: More robust and more efficient Apollonian network

The family of planar graphs is a particularly important family and models many networks including the layout of printed circuits. The widely-known Apollonian packing process has been used as guideline to create the typical Apollonian network with planarity. In this paper, we propose a new principled framework based on the Apollonian packing process to generate model as complex network, and obtain a family of new networks called Type-II Apollonian network At. While our network and the typical Apollonian network are maximal planar, the former turns out to be Hamiltonian and Eulerian, however, the latter is not. Then, we in-depth study some fundamental structural properties on network At, and verify that network At is sparse, has scale-free feature and small-world property, and exhibits disassortative mixing structure. Next, we derive the asymptotic solution of the spanning tree entropy of network At by designing an effective algorithm, which suggests that Type-II Apollonian network is more robust to a random removal of edges than the typical Apollonian network. Additionally, we study trapping problem on network At, and use average trapping time as metric to show that Type-II Apollonian network At has more efficient underlying structure for fast information diffusion than the typical Apollonian network.

Evaluation and design of a bite force measuring load cell

The measurement of bite force can be essential for assessing oral health and the functioning of the stomatognathic system. However, its use in dental offices is limited due to operational complexities and costs. This study focused on evaluating the mechanical strength of the user-friendly and low manufacturing cost load cell under development within LABBIO – Bioengineering Laboratory at UFMG, the gnathodynamometer, tailored for children. 3D modeling in SolidWorks and Finite Element Method (FEM) structural analysis were employed to ensure its ability to withstand the desired 1000 N load. Initially, the device did not meet the desired load, resulting in iterations to modify its geometry. After iterations including the replacement of the initial material with a more suitable alternative, removal of edges, and other adjustments, a final prototype meeting mechanical strength and functionality requirements was developed.

Robustness study of hybrid hypergraphs

Recently, researchers have used the pairwise networks (lower-order) or hypergraphs (higher-order) to separately model different real systems. However, some real systems may simultaneously exist lower-order and higher-order relationships, for example, in scientist collaboration networks, there exist lower-order relationships, representing whether scientists know each other, as well as higher-order relationships, indicating shared research tasks among multiple scientists, such as co-authors of papers. In light of this, a hybrid hypergraph model that considers both lower-order and higher-order relationships is firstly considered in this work. And then, we focus on studying the robustness of hybrid hypergraphs owing to the importance of this issue. Specifically, we first introduce a collaboration efficiency (CE) metric to measure the robustness of hybrid hypergraphs. We further propose a novel edge removal strategy, termed EIHED (Edge-Included Hyperedge Efficiency Discrepancy), to study its impact on the robustness of hybrid hypergraphs. Finally, we further validate the effectiveness of our proposed strategy from both node attacking strategy and edge protection strategy.

Optimization of surface filtration to enhance wafer uniformity by removing large particle in ceria slurry

Owing to the limitations of scaling down, the development of complex structures for chip design is progressing in the semiconductor industry. As a result, the chemical mechanical polishing (CMP) process has become essential for the precise control of micro-level steps and for maintaining surface uniformity between layers during the stacking process. Structural management and improved performance of the consumables used in CMP processes are required. In particular, controlling the abrasive particles in the slurry that directly contact the wafer surface is crucial. Large particles within the slurry can occur scratches and cause non-uniformity in the polishing process, thus the particle size distribution of the slurry must be regulated. In this study, two types of filtration systems were constructed to determine the optimal conditions for CMP of ceria slurry. Depth filtration effectively removes particles through a cake layer formation mechanism but has limitations in achieving a consistent pore size within the fiber layer. Consequently, separation efficiency for particle sizes is relatively low. In contrast, surface filtration consisting of a single membrane demonstrates a consistent correlation between pore size and the size of the filtered particles, resulting in high particle removal efficiency. The pore size capable of removing large particles without active particle loss was 200 nm. After evaluating slurry productivity and filter-induced agglomeration, 0.8 L per minute (LPM) was identified as the optimal flow rate. Additionally, after filtration at 0.8 LPM, the maximum reduction in removal rates was relatively small at 272.54 Å/min due to decreased edge removal rates. However, by removing large particles to implement a monodisperse state, an improvement in uniformity of 17.11 % was achieved. Therefore, the CMP performance can be enhanced using surface filtration to control the particle size distribution (PSD).

Focus on the disruption of networks and system dynamics.

Networks are designed to ensure proper functioning and sustained operability of the underlying systems. However, disruptions are generally unavoidable. Internal interactions and external environmental effects can lead to the removal of nodes or edges, resulting in unexpected collective behavior. For instance, a single failing node or removed edge may trigger a cascading failure in an electric power grid. This Focus Issue delves into recent advances in understanding the impacts of disruptions on networks and their system dynamics. The central theme is the disruption of networks and their dynamics from the perspectives of both data-driven analysis as well as modeling. Topics covered include disruptions in the dynamics of empirical systems such as nuclear reaction networks, infrastructure networks, social networks, epidemics, brain dynamics, and physiology. Emphasis is placed on various phenomena in collective behavior, including critical phase transitions, irregular collective dynamics, complex patterns of synchrony and asynchrony, chimera states, and anomalous oscillations. The tools used for these studies include control theory, diffusion processes, stochastic processes, and network theory. This collection offers an exciting addition to the evolving landscape of network disruption research.

CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks

Electronic Health Records (EHRs) play a crucial role in shaping predictive are models, yet they encounter challenges such as significant data gaps and class imbalances. Traditional Graph Neural Network (GNN) approaches have limitations in fully leveraging neighbourhood data or demanding intensive computational requirements for regularisation. To address this challenge, we introduce CliqueFluxNet, a novel framework that innovatively constructs a patient similarity graph to maximise cliques, thereby highlighting strong inter-patient connections. At the heart of CliqueFluxNet lies its stochastic edge fluxing strategy — a dynamic process involving random edge addition and removal during training. This strategy aims to enhance the model’s generalisability and mitigate overfitting. Our empirical analysis, conducted on MIMIC-III and eICU datasets, focuses on the tasks of mortality and readmission prediction. It demonstrates significant progress in representation learning, particularly in scenarios with limited data availability. Qualitative assessments further underscore CliqueFluxNet’s effectiveness in extracting meaningful EHR representations, solidifying its potential for advancing GNN applications in healthcare analytics.

A novel approach to identify the microclimatic edge effect width of urban green spaces at the landscape level: A case study of Changsha, China

Microclimate (e.g., temperature and humidity) in urban green space (UGS) patches changes nonlinearly from the edge to the interior, displaying a microclimatic edge effect (MEE). Reducing the MEE width is an effective and practical strategy for UGS planning and management to enhance climatic benefits (e.g., cooling). However, cost-effective approaches are not available to quantify the MEE width, let alone explore its driving factors. This study proposes a novel and cost-effective method to quantify the MEE width, based on the relationship between climatic variables (e.g., temperature and humidity) and the percentage of remaining UGS after successive edge removals. The method was tested in the subtropical city of Changsha, China, considering air temperature (AT) and humidity. Its effectiveness was also tested using widely available land surface temperature (LST) datasets while considering different spatial resolutions of the UGS map (i.e., 1–10 m) and different analytical units (i.e., 1 km2 grid and township census tract). Finally, the generalizability of this method was validated with data on six other representative Chinese cities. The results show that: (1) The estimated MEE widths for temperature and humidity in Changsha are about 8 m and 6 m, respectively. (2) The approach obtains consistent MEE widths irrespective of temperature types and analytical units. (3) A high spatial resolution UGS map (i.e., 1 m) is recommended for higher accuracy. (4) This approach effectively identifies MEE width in six other representative Chinese cities (Beijing, Chengdu, Shanghai, Shenyang, Wuhan, Xi’an), demonstrating its generalizability. This novel approach provides an easy and fast method to identify the MEE width at the landscape scale, which can help (1) better understand the relationship between UGS fragmentation and the urban microclimate, and (2) better plan and manage UGS to improve the urban microclimate.

Graph Theoretical Models for Enhancing Highway Connectivity and Safety in Vehicular Networks

Vehicular networks play a crucial role in modern transportation systems, significantly impacting connectivity and safety on highways. This paper explores the application of graph theoretical models to enhance both connectivity and safety in vehicular networks. Graph theory, a branch of discrete mathematics, provides a robust framework for modeling and analyzing complex networks, including those formed by vehicles on highways. Our study begins by defining the vehicular network as a graph where nodes represent vehicles, and edges denote communication links between them. We employ various graph theoretical concepts such as connectivity, centrality, and network flow to evaluate and improve the network's performance. Key metrics, including the degree of nodes, clustering coefficients, and shortest path lengths, are utilized to quantify network connectivity and identify critical nodes and edges that influence overall network efficiency. One of the primary objectives is to ensure uninterrupted connectivity in the presence of dynamic and often unpredictable vehicular movement. To this end, we analyze the network's resilience to node failures and propose strategies to enhance robustness using redundancy and alternative routing paths. By incorporating concepts like k-connectivity and network diameter, we develop models that maintain high levels of connectivity despite the removal or failure of multiple nodes or edges. Safety is addressed through the lens of network stability and reliability. We investigate the impact of vehicular density, speed, and communication range on the network's ability to sustain reliable communication channels. Techniques such as dynamic topology management and adaptive power control are proposed to mitigate the risks associated with network fragmentation and communication delays. Furthermore, we introduce optimization algorithms that leverage graph partitioning and community detection to improve the management of vehicular clusters, facilitating efficient data dissemination and reducing the likelihood of congestion-related incidents. The proposed models are validated through simulations that mimic real-world highway conditions, demonstrating significant improvements in both connectivity and safety metrics. In conclusion, the application of graph theoretical models offers a promising approach to enhancing highway connectivity and safety in vehicular networks

Effects of edge addition or removal on the nullity of a graph

Lights Out is a game which can be played on any graph G. Initially we have a configuration which assigns one of the two states on or off to each vertex. The aim of the game is to turn all vertices to off state for an initial configuration by activating some vertices where each activation switches the state of the vertex and all of its neighbors. If the aim of the game can be accomplished for all initial configurations then G is called always solvable. We call the dimension of the kernel of the closed neighborhood matrix of the graph over the field Z2, nullity of G. It turns out that G is always solvable if and only if its nullity is zero. Moreover, the number of solutions of a given configuration is also determined by the nullity. We investigate the problem of how nullity changes when an edge is added to or removed from a graph. As a result we show that for every graph with positive nullity there exists an edge whose removal decreases the nullity. Conversely, we show that for every always solvable graph which is not an even graph with odd order, there exists an edge whose addition increases the nullity. We also show that if an always solvable graph is not even, then there is an edge whose removal increases the nullity.

Distributed Pinning Control: Stabilizing Large Boolean Networks Subjected to Perturbations.

Stability maintenance in systems refers to the capacity to preserve inherent stability characteristics. In this article, stability maintenance of large boolean networks (BNs) subjected to perturbations is investigated using a distributed pinning control (PC) strategy. The concept of edge removal as a form of perturbation is introduced, and several criteria for achieving global stability are established. Two forms of distributed PCs, one implemented before perturbation occurs and the other after, are introduced. It is noteworthy that the designs of the controllers are solely dependent on the system's in-neighbors. The proposed method significantly decreases the computational complexity, reducing it from O(22|V|) to O(|V|+ |E| + κ·2K) , where |V|, |E| denotes the cardinality of vertices and arcs of the adjacent graph of BN, κ is the number of the pinning nodes, and K represents the maximum in-degree of the network. In the worst-case scenario, the computational complexity is bounded by O(|V|+ |E| + κ·2|V|) . To validate the effectiveness of the proposed methods, results from multiple gene networks are presented, including a model representing the human rheumatoid arthritis synovial fibroblast, among which only 12 of the 359 nodes are deemed essential.

Machinability improvement in micro milling AlN after laser chemical milling

Processing microchannels inside laminated aluminum nitride high-temperature co-fired ceramics (AlN HTCC) packaging, a typical difficult-to-cut ceramic, can effectively solve the heat-dissipation problem of integrated chips used in smart skin. In order to improve the processing efficiency and quality of AlN, the machinability of AlN after laser chemical milling (LCM) was studied through the milling force, machined surface quality, surface defects, formation mechanism, and tool wear. This study established a milling force model that can predict the milling forces of AlN and analyses the reasons for the improvements in the milling force based on experimental data and predicted data. The results from the model and experiments demonstrated that the milling force of the laser chemical milling assisted micro milling (LCAMM) decreased by 85%–90% and 85%–95%, respectively, due to the amount of removal of a single edge was more uniform and the actual inclination angle increased during the cutting process in LCAMM. Moreover, the machined surface quality improved by 65%–76% after LCM because of less tool wear, fewer downward-propagating cracks generated during each feed, and the surface removal mode transformed from intergranular fracture to transgranular fracture, which effectively reducing tool wear and improving tool life. Finally, when feed per tooth and depth of cut were 0.4 μm/z and 5 μm, the optimal machined surface quality was obtained, with a roughness of 64.6 nm Therefore, milling after LCM can improve the machinability of AlN and providing a feasibility for the high-quality and efficient machining of microchannels.

Influence of network structure on spreading dynamics via tie range

Abstract There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of network structure on network spreading. Motifs, as fundamental structures within a network, play a significant role in spreading. Therefore, it is of interest to investigate the influence of the structural characteristics of basic network motifs on spreading dynamics. Considering the edges of the basic network motifs in an undirected network correspond to different tie ranges, two edge removal strategies are proposed, short ties priority removal strategy and long ties priority removal strategy. The tie range represents the second shortest path length between two connected nodes. The study focuses on analyzing how the proposed strategies impact network spreading and network structure, as well as examining the influence of network structure on network spreading. Our findings indicate that the long ties priority removal strategy is most effective in controlling network spreading, especially in terms of spread range and spread velocity. In terms of network structure, the clustering coefficient and the diameter of network also have an effect on the network spreading, and the triangular structure as an important motif structure effectively inhibits the spreading.

On Breaking Truss-based and Core-based Communities

We introduce the general problem of identifying a smallest edge subset of a given graph whose deletion makes the graph community-free. We consider this problem under two community notions that have attracted significant attention: k -truss and k -core. We also introduce a problem variant where the identified subset contains edges incident to a given set of nodes and ensures that these nodes are not contained in any community: k -truss or k -core, in our case. These problems are directly applicable in social networks: The identified edges can be hidden by users or sanitized from the output graph; or in communication networks: the identified edges correspond to vital network connections. We present a series of theoretical and practical results. On the theoretical side, we show through non-trivial reductions that the problems we introduce are NP-hard and, in fact, hard to approximate. For the k -truss-based problems, we also show exact exponential-time algorithms, as well as a non-trivial lower bound on the size of an optimal solution. On the practical side, we develop a series of heuristics that are sped up by efficient data structures that we propose for updating the truss or core decomposition under edge deletions. In addition, we develop an algorithm to compute the lower bound. Extensive experiments on 11 real-world and synthetic graphs show that our heuristics are effective, outperforming natural baselines, and also efficient (up to two orders of magnitude faster than a natural baseline), thanks to our data structures. Furthermore, we present a case study on a co-authorship network and experiments showing that the removal of edges identified by our heuristics does not substantially affect the clustering structure of the input graph. This work extends a KDD 2021 paper, providing new theoretical results as well as introducing core-based problems and algorithms.

The Course of the Trochlear Nerve Presented via a 3-Dimensional Photorealistic Anatomic Model

Several factors contribute to the anatomical complexity of the trochlear nerve, including small diameter, complex and longest intracranial course, deep location, and numerous neurovascular relationships. A 3-dimensional (3D) photorealistic model of the cranial nerves provides a detailed and immersive representation of the anatomy, enabling one to improve surgical planning, advanced surgical research, and training. The purpose of this work is to present a 3D photogrammetric study for a more intuitive and interactive way to explore and describe the entire course of trochlear nerve. Two injected-fixed head human specimens (4 sides) were examined. The dissection protocol was divided into the following steps: 1) brain hemisphere exposure; 2) hemispherectomy dissecting all cranial nerves and partial removal of the free edge of the tentorium; 3) middle fossa and lateral wall of cavernous sinus exposure; and 4) orbital exposure. A detailed 3D photogrammetric model was generated for each dissection step. Four main volumetric models were generated during a step-by-step layered dissection of the entire nerve pathway highlighting its different segments. Finally, a full and integrated model of the entire course of the nerve was created. The models are available for visualization on monoscopic display, virtual, and augmented reality environment. The present photogrammetric model provides a more comprehensive understanding of the nerve's anatomy in its different segments, allows for customizable views thus simulating different perspectives, and can be a valuable alternative to traditional dissections. It is an advanced tool for surgical planning and surgical simulation as well as virtual reality representation of the anatomy.