Node Topology Research Articles

An Attentional Graph Neural Network-Based Fault Point Positioning Model for Low-Voltage Distribution Networks

With the rapid development of the smart grid, the fast and accurate fault location of low-voltage distribution networks has become the key to ensuring the stability and reliability of the power supply. This paper aims to explore and construct a fault location model of low-voltage distribution network based on an attention diagram neural network. First, this paper analyzes the current situation and challenges of fault location in low-voltage distribution network, and points out that traditional methods have limitations when processing large-scale and high-dimensional power system data. Subsequently, a graph neural network (GNN) is introduced for processing graph-structured network data, and combined with attention mechanisms. Thus, an innovative attention-graph neural network model (named as A-GNN) is proposed for the purpose. The model can make full use of the topology structure and node feature information in the power grid, and dynamically adjust the information aggregation weight between different nodes through the attention mechanism. This is expected to achieve efficient and accurate fault location. In the experimental part, we trained and tested the A-GNN model based on the real low-voltage distribution network dataset, and compared it with several prediction models. The experimental results show that the A-GNN model has higher accuracy and recall rate in fault location tasks, especially in complex fault scenarios.

Boosting Cooperative NPC Effectiveness and Player Immersion through Behavior Tree Optimization in Gaming

The purpose of this research is to improve the adaptability and intelligence of cooperative non-player characters (NPCs) in dynamic gaming situations. Although behavior trees (BTs) are commonly used to simulate non-player character (NPC) behaviors, their rigid hierarchical design restricts NPC adaptability in complex settings. This study employs a variety of optimization techniques, including evolutionary algorithms and hybrid strategies that combine Artificial Neural Networks (ANN) and Monte Carlo Tree Search (MCTS), to improve NPC adaptability. The findings reveal that these strategies enable flexible behavior transitions, with evolutionary algorithms strengthening BT's flexibility in decision-making scenarios and ANN and MCTS significantly increasing NPC intelligence and response capabilities. Furthermore, we highlight fundamental drawbacks in typical BTs, such as static node topologies, which may impede dynamic adaptation. These discoveries lay the groundwork for improving immersive gaming experiences, expanding the knowledge repository for NPC AI research, and providing vital insights into the development of more intelligent cooperative NPCs.

Topology Control of Low-Connection UAV Laser Network with Virtual Nodes

Space laser communication technology has advantages such as high bandwidth and low interference, but the limitation of the load capacity of an unmanned aerial vehicle (UAV) makes it difficult for UAV clusters to have sufficient communication links to build a stable communication network. Aiming to address the problem of the low upper limit of the node degree of the self-organized network after the introduction of space laser communication technology for UAVs, a virtual node topology control (VNTC) algorithm is proposed for constructing an efficient and stable communication network structure. The algorithm is based on the unit disc graph model and achieves a lower-node-degree upper bound while maintaining connectivity through node pairing and virtual graph construction. The theoretical analysis and experimental results of the algorithm show that the algorithm can effectively deal with the topology control problem in UAV laser networks. In contrast to the XTC algorithm (exceptional topology control), the VNTC algorithm maintains the connectivity and most of the planarizability of the network, and, at the same time, it performs better in reducing the node degree and number of edges, thus providing an effective solution for low-connectivity topology control.

Designing Chiral Organometallic Nanosheets with Room-Temperature Multiferroicity and Topological Nodes.

Two-dimensional (2D) room-temperature chiral multiferroic and magnetic topological materials are essential for constructing functional spintronic devices, yet their number is extremely limited. Here, by using the chiral and polar HPP (HPP = 4-(3-hydroxypyridin-4-yl)pyridin-3-ol) as an organic linker and transition metals (TM = Cr, Mo, W) as nodes, we predict a class of 2D TM(HPP)2 organometallic nanosheets that incorporate homochirality, room-temperature magnetism, ferroelectricity, and topological nodes. The homochirality is introduced by chiral HPP linkers, and the change in structural chirality induces a topological phase transition of Weyl phonons. The room-temperature magnetism arises from the strong d-p spin coupling between TM cations and HPP doublet anions. The ferroelectricity is attributed to the breaking of spatial inversion symmetry in the lattice structure. Additionally, by adjusting the type of TMs, these nanosheets show rich and tunable band structures. Notably, all predicted materials are topologically nontrivial, featuring a quadratic nodal point around the Fermi level.

Construction of 3D Indoor Topological Models Based on Improved Face Sorting

Indoor location-based services and applications need to obtain information about the indoor spatial layouts and topological relationships of indoor spaces. The 3D city modeling data standard CityGML describes the indoor geometric and semantic information of buildings, but the surfaces composing a volume are discrete, leading to invalid volumes. Moreover, the topological adjacency relationships of adjacent indoor spaces have not yet been described, which makes it difficult to realize effective queries and analyses for indoor applications. In this paper, we present a 3D topological data model for indoor spaces that adopts five topological primitives, namely, node, edge, loop, face, and solid, to describe the topological relationships of indoor spaces. Then, by improving the existing face-sorting method according to vector products in 3D space, a method for constructing 3D topological relationships for indoor spaces is proposed, which successively constructs the topological hierarchical combination of volume and the topological adjacency relationships of adjacent volumes. The experimental results show that by using the improved face-sorting method proposed in this work, the relative positions of faces are directly determined to sort the faces set, which avoids relatively cumbersome calculations and improves the efficiency of constructing 3D topological relationships for indoor spaces.

A Multi-Attribute Decision-Making Approach for Critical Node Identification in Complex Networks.

Correctly identifying influential nodes in a complex network and implementing targeted protection measures can significantly enhance the overall security of the network. Currently, indicators such as degree centrality, closeness centrality, betweenness centrality, H-index, and K-shell are commonly used to measure node influence. Although these indicators can identify critical nodes to some extent, they often consider node attributes from a narrow perspective and have certain limitations. Therefore, evaluating the importance of nodes using most existing indicators remains incomplete. In this paper, we propose the multi-attribute CRITIC-TOPSIS network decision indicator, or MCTNDI, which integrates closeness centrality, betweenness centrality, H-index, and network constraint coefficients to identify critical nodes in a network. This indicator combines information from multiple perspectives, including local neighborhood importance, network topological location, path centrality, and node mutual information, thereby solving the issue of the one-sided perspective of single indicators and providing a more comprehensive measure of node importance. Additionally, MCTNDI is validated through the analysis of several real-world networks, including the Contiguous USA network, Dolphins network, USAir97 network, and Tech-routers-rf network. The validation is conducted from four aspects: the results of simulated network attacks, the distribution of node importance, the monotonicity of rankings, and the similarity of indicators, illustrating MCTNDI's effectiveness in real networks.

Resilience Analysis of Transportation System Based on Complex Network Theory

In order to ensure the important role of the transportation system in economic development, it is particularly important to evaluate the characteristics and resilience of the network. According to the complex network theory, the original mapping method is used to construct the complex network model, and the topological features node degree, average path length, clustering coefficient and intermediate are analyzed. The improved network efficiency and the maximum connectivity sub-graph size were selected as the resilience indicators, and the random attack and deliberate attack were used to simulate, and the changes of the resilience analysis index were calculated and compared, so as to complete the analysis of the resilience of the transportation system. The node degree, clustering coefficient and intermediate were selected as the evaluation indexes of key nodes, and the coefficient of variation method and TOPSIS algorithm were used to identify the key nodes, which were used as the basis for node selection in the simulation. Finally, taking Tianjin Shugang Highway as an example, an empirical analysis is carried out to verify the accuracy and applicability of the method.

Dynamic control, routing, and resource assignment in multi-granular optical node topologies combining wavelength, waveband, and spatial switching for 6G transport networks [Invited

Effective management of end-to-end 6G network services is crucial, with peak capacity requirements for 6G transport connections expected to exceed 1 Tb/s. As demand for high bandwidth rises, there is a growing necessity for high-capacity optical fiber links, including ultra-wideband (UWB) and multiple fiber links within the network. Scaling up to accommodate these demands, designing wavelength-selective switches (WSSs) for such networks significantly increases the port count. To tackle this issue, we propose various multi-granular optical node (MG-ON) architectures utilizing heterogeneous wavelength, waveband, and spatial switching. We evaluate these architectures’ performance against high-capacity wavelength division multiplexed (WDM) networks through various simulation parameters.

Evolution in thermal storage stability of SBS-polymer modified asphalt induced by differences in microscopic characteristics of base asphalt

In thermal storage process, the aggregation, segregation, and floating of styrene–butadiene-styrene block copolymer (SBS) modifier will affect the performances of SBS-modified asphalt (SBSMA). To investigate the significant impact of macro and micro characteristics of base asphalt (BA) on the storage stability of SBSMA, we prepared SBSMA samples with three different base asphalts (BA-A, BA-B, and BA-C). Fourier transform infrared spectroscopy (FTIR), gel permeation chromatography (GPC) and atomic force microscopy (AFM) were used to investigate the microscopic properties of BAs and SBSMAs and to establish topological network nodes for their microscopic morphology. The storage performance of SBSMAs was inspected by the segregation softening point difference, complex modulus difference, and phase angle difference. Finally, the correlation analysis of microscopic structure of BA samples and storage stability of SBSMA was conducted via grey correlation methodology. The experimental results indicate that BA-A with higher molecular weight has an uneven size with honeycomb like structure, while modified asphalt using BA-A exhibits serious phase separation. In contrast, modified asphalt using BA-B and BA-C has better storage stability due to their uniform honeycomb structure size, which is consistent with the pattern presented by the microstructure of asphalt. Grey correlation analysis shows that there is a good correlation between the roughness of BA samples and the storage stability of SBSMA. In addition, the segregation softening point and the difference in complex modulus can be effective indicators for evaluating the storage stability of SBSMA.

Observation of two-dimensional time-reversal broken non-Abelian topological states

Going beyond the conventional theory, non-Abelian band topology reveals the global quantum geometry of multiple Bloch bands and unveils a new paradigm for topological physics. However, to date, experimental studies on non-Abelian topological states beyond one dimension are still restricted to systems with time-reversal (T\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mathcal{T}}}$$\\end{document}) symmetry. Here, exploiting a designer gyromagnetic photonic crystal, we find rich T\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mathcal{T}}}$$\\end{document}-broken non-Abelian topological phases and their transitions with an unexpected connection to multigap antichiral edge states. By in-situ tuning the magnetic field in the gyromagnetic photonic crystal, we can create, braid, merge, and split the non-Abelian topological nodes in a unique way. Alongside this process, the multigap antichiral edge states can be tuned versatilely, giving rise to topological edge waveguiding with frequency-dependent directionality. These findings open a new avenue for non-Abelian topological physics and topological photonics.

Optimization model for identification of node topology relationships in an online education platform based on the IOELM-KSFTR algorithm

Optimization model for identification of node topology relationships in an online education platform based on the IOELM-KSFTR algorithm

Multi-bioinformatics revealed potential biomarkers and repurposed drugs for gastric adenocarcinoma-related gastric intestinal metaplasia

Biomarkers associated with the progression from gastric intestinal metaplasia (GIM) to gastric adenocarcinoma (GA), i.e., GA-related GIM, could provide valuable insights into identifying patients with increased risk for GA. The aim of this study was to utilize multi-bioinformatics to reveal potential biomarkers for the GA-related GIM and predict potential drug repurposing for GA prevention in patients. The multi-bioinformatics included gene expression matrix (GEM) by microarray gene expression (MGE), ScType (a fully automated and ultra-fast cell-type identification based solely on a given scRNA-seq data), Ingenuity Pathway Analysis, PageRank centrality, GO and MSigDB enrichments, Cytoscape, Human Protein Atlas and molecular docking analysis in combination with immunohistochemistry. To identify GA-related GIM, paired surgical biopsies were collected from 16 GIM-GA patients who underwent gastrectomy, yielding 64 samples (4 biopsies per stomach x 16 patients) for MGE. Co-analysis was performed by including scRNAseq and immunohistochemistry datasets of endoscopic biopsies of 37 patients. The results of the present study showed potential biomarkers for GA-related GIM, including GEM of individual patients, individual genes (such as RBP2 and CD44), signaling pathways, network of molecules, and network of signaling pathways with key topological nodes. Accordingly, potential treatment targets with repurposed drugs were identified including epidermal growth factor receptor, proto-oncogene tyrosine-protein kinase Src, paxillin, transcription factor Jun, breast cancer type 1 susceptibility protein, cellular tumor antigen p53, mouse double minute 2, and CD44.

Optimal configuration of adjustable capacity transformers considering OPF distribution

Abstract The configuration of adjustable capacity transformers will be affected by photovoltaic (PV) penetration. In this paper, we propose a method for optimizing the configuration of adjustable capacity transformers by analyzing the parameters and topology of the low-voltage distribution network, which considers both the operational safety of the grid and economic objectives under optimal power flow (OPF) distribution. We construct the objectives and constraints of the OPF-solving model in different PV penetration scenarios for two types of node topologies and utilize second-order cone (SOC) relaxation to solve the results of OPF distribution. In order to calculate the long-term power loss and comprehensively assess the transformer replacement cost, we analyze the low-voltage side load curves of the adjustable capacity transformers based on the OPF solutions. We design an optimization scheme for the capacity configuration of transformers under different PV penetration rates. Simulation results show that the proposed scheme can effectively reduce power loss and transformer operating costs.

TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image

Generative adversarial networks (GANs) have significantly advanced synthetic image generation, yet ensuring topological coherence remains a challenge. This paper introduces TopoSinGAN, a topology-aware extension of the SinGAN framework, designed to enhance the topological accuracy of generated images. TopoSinGAN incorporates a novel, differentiable topology loss function that minimizes terminal node counts along predicted segmentation boundaries, thereby addressing topological anomalies not captured by traditional losses. We evaluate TopoSinGAN using agricultural and dendrological case studies, demonstrating its capability to maintain boundary continuity and reduce undesired loop openness. A novel evaluation metric, Node Topology Clustering (NTC), is proposed to assess topological attributes independently of geometric variations. TopoSinGAN significantly improves topological accuracy, reducing NTC index values from 15.15 to 3.94 for agriculture and 14.55 to 2.44 for dendrology, compared to the baseline SinGAN. Modified FID evaluations also show improved realism, with lower FID scores: 0.1914 for agricultural fields compared to 0.2485 for SinGAN, and 0.0013 versus 0.0014 for dendrology. The topology loss enables end-to-end training with direct topological feedback. This new framework advances the generation of topologically accurate synthetic images, with applications in fields requiring precise structural representations, such as geographic information systems (GIS) and medical imaging.

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).

NUMA-aware parallel sparse LU factorization for SPICE-based circuit simulators on ARM multi-core processors

In circuit simulators that resemble the Simulation Program with Integrated Circuit Emphasis (SPICE), one of the most crucial steps is the solution of numerous sparse linear equations generated by frequency domain analysis or time domain analysis. The sparse direct solvers based on lower-upper (LU) factorization are extremely time-consuming, so their performance has become a significant bottleneck. Despite the existence of some parallel sparse direct solvers for circuit simulation problems, they remain challenging to adapt in terms of performance and scalability in the face of rapidly evolving parallel computers with multiple NUMA hardware based on ARM architecture. In this paper, we introduce a parallel sparse direct solver named HLU, which re-examines the performance of the parallel algorithm from the viewpoint of parallelism in pipeline mode and the computing efficiency of each task. To maximize task-level parallelism and further minimize the thread waiting time, HLU devises a fine-grained scheduling method based on an elimination tree in pipeline mode, which employs depth-first search (DFS-like) to iteratively search for parent tasks and then place dependent tasks in the same task queue. HLU also suggests two NUMA node affinity strategies: thread affinity optimization based on NUMA nodes topology to guarantee computational load balancing and data affinity optimization to enable effective memory placement when threads access data. The rationality and effectiveness of the sparse solver HLU are validated by the SuiteSparse Matrix Collection. In comparison with KLU and NICSLU, the experimental results and analysis show that HLU attains a speedup of up to 9.14× and 1.26x (geometric mean) on a Huawei Kunpeng 920 Server, respectively.

Link Stealing Attacks Against Inductive Graph Neural Networks

A graph neural network (GNN) is a type of neural network that is specifically designed to process graph-structured data. Typically, GNNs can be implemented in two settings, including the transductive setting and the inductive setting. In the transductive setting, the trained model can only predict the labels of nodes that were observed at the training time. In the inductive setting, the trained model can be generalized to new nodes/graphs. Due to its flexibility, the inductive setting is the most popular GNN setting at the moment. Previous work has shown that transductive GNNs are vulnerable to a series of privacy attacks. However, a comprehensive privacy analysis of inductive GNN models is still missing. This paper fills the gap by conducting a systematic privacy analysis of inductive GNNs through the lens of link stealing attacks. We propose two types of link stealing attacks, i.e., posterior-only attacks and combined attacks. We define threat models of the posterior-only attacks with respect to node topology and the combined attacks by considering combinations of posteriors, node attributes, and graph features. Extensive evaluation on six real-world datasets demonstrates that inductive GNNs leak rich information that enables link stealing attacks with advantageous properties. Even attacks with no knowledge about graph structures can be effective. We also show that our attacks are robust to different node similarities and different graph features. As a counterpart, we investigate two possible defenses and discover they are ineffective against our attacks, which calls for more effective defenses.

K-plex-based community detection with graph neural networks

Community detection is an effective way to determine the structure and characteristics of a complex network. With the expansion of the network scale, traditional community detection approaches such as modularity-based optimization models face new challenges related to representing and learning the topological structure and node attributes from a large scale complex network. Moreover, in many practical applications, there is little knowledge about label information or the number of communities, which greatly limits the performance of existing supervised or semi-supervised community detection approaches. To solve these problems, in this paper, we propose a graph neural network-based unsupervised community detection approach, which first applies the k-plex to generate the community seeds, then uses a node sampling algorithm to reduce the network complexity, and finally constructs a graph neural network model to learn the relationships of the network nodes and assign the nodes to different communities. Extensive empirical studies on various scale networks demonstrate both the effectiveness and efficiency of the proposed approach. Our codes are available at https://github.com/lol12854/KPGN.

Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-Disease Association Prediction.

Identifying new relevant long noncoding RNAs (lncRNAs) for various human diseases can facilitate the exploration of the causes and progression of these diseases. Recently, several graph inference methods have been proposed to predict disease-related lncRNAs by exploiting the topological structure and node attributes within graphs. However, these methods did not prioritize the target lncRNA and disease nodes over auxiliary nodes like miRNA nodes, potentially limiting their ability to fully utilize the features of the target nodes. We propose a new method, mask-guided target node feature learning and dynamic detailed feature enhancement for lncRNA-disease association prediction (MDLD), to enhance node feature learning for improved lncRNA-disease association prediction. First, we designed a heterogeneous graph masked transformer autoencoder to guide feature learning, focusing more on the features of target lncRNA (disease) nodes. The target nodes were increasingly masked as training progressed, which helps develop a more robust prediction model. Second, we developed a graph convolutional network with dynamic residuals (GCNDR) to learn and integrate the heterogeneous topology and features of all lncRNA, disease, and miRNA nodes. GCNDR employs an interlayer residual strategy and a residual evolution strategy to mitigate oversmoothing caused by multilayer graph convolution. The interlayer residual strategy estimates the importance of node features learned in the previous GCN encoding layer for nodes in the current encoding layer. Additionally, since there are dependencies in the importance of features of individual lncRNA (disease, miRNA) nodes across multiple encoding layers, a gated recurrent unit-based strategy is proposed to encode these dependencies. Finally, we designed a perspective-level attention mechanism to obtain more informative features of lncRNA and disease node pairs from the perspectives of mask-enhanced and dynamic-enhanced node features. Cross-validation experimental results demonstrated that MDLD outperformed 10 other state-of-the-art prediction methods. Ablation experiments and case studies on candidate lncRNAs for three diseases further proved the technical contributions of MDLD and its capability to discover disease-related lncRNAs.

Classification of high-ordered topological nodes towards Moiré flat bands in twisted bilayers

Classification of high-ordered topological nodes towards Moiré flat bands in twisted bilayers