Search Results (148)

This paper proposes an adaptive single-layer distributed neurodynamic optimization approach with the penalty method to address a non-smooth pseudoconvex optimization problem with affine equality and inequality constraints in multi-agent systems, where the global objective function for the agents is pseudoconvex but not required to be differentiable. The target of this approach is to optimize the global objective while ensuring compliance with various constraints. The approach avoids the use of additional auxiliary variables, thereby reducing communication bandwidth and computational complexity. Under mild assumptions, the solution of the designed model is bounded for any initial conditions, to enter their respective feasible domains in finite time, and remain within these domains indefinitely. To achieve finite-time consensus in undirected, connected networks for multi-agent systems, a novel consensus mechanism is introduced to ensure that all agents synchronize their states within finite time. By exploiting the unique pseudoconvexity of the global objective function, the solution trajectory converges to the optimal state of the original problem. Furthermore, the effectiveness of the proposed approach is verified through two simulation experiments, and comparisons with four existing algorithms are conducted to demonstrate its superiority in convergence performance. Finally, an economic dispatch problem in power systems is provided as an engineering application to illustrate the practical applicability of the proposed algorithm. Full article

Uneven regional socioeconomic growth presents a fundamental economic challenge, as traditional budget allocation models frequently fall short by neglecting spatial topology and spillover effects. By striking a statistically correct balance between social equality and economic efficiency, this study seeks to maximize regional public funds. We present a new hybrid approach that uses Graph Attention Networks (GAT) in conjunction with multi-criteria optimization to model administrative districts as undirected graphs. By combining regularizations for temporal stability and spatial smoothness, the composite loss function scalarizes a logarithmic utility function for efficiency and a differentiable Gini index for equity. The GNN-based model removed major historical funding imbalances, reducing the highest regional funding gap from an 80-fold difference to a 1.08 ratio and reducing per capita funding variance by 92%, according to empirical testing conducted in Kazakhstan’s Almaty region. The system automatically reorganized 208.5 billion KZT (~463 million USD) at the ideal Pareto frontier (α = 0.5), allocating 96.4% of investments to the digitalization sector, which was given priority. According to the results, incorporating spatial topology, which explains 58.7% of choice variability, offers a highly scalable, empirically supported method for improving strategic public administration and reducing institutional stagnation. Full article

In the existing research on information propagation modeling in social networks, hypergraphs have been widely applied to characterize the high-order interaction relationships involving multiple nodes. However, most models are still based on the assumption of undirected connections, which leads to certain limitations in depicting the information flow direction and the structural characteristics of propagation chains. To address the above problems, a social hypernetwork propagation model with directional constraints is constructed in this paper by introducing the directed hypergraph structure and combining it with the improved SEIR model. The strength of social relationships is measured by intimacy in the model, and a comprehensive characterization of the information propagation process is achieved by integrating the threshold mechanism of the directed hypergraphs with the attenuation function of information timeliness. In addition, the effectiveness of the proposed model is verified by taking the event of “imposing additional tariffs” as an example, and the evolutionary characteristics of propagation in different network structures, as well as the impacts of user confidence and information timeliness, are analyzed using simulation experiments. The results indicate that the model is applicable to characterizing the information propagation trends and dynamic characteristics in real social networks, and can provide theoretical references and methodological support for the prediction and regulation of network public opinion. Full article

Multi-agent and distributed dynamical systems can exhibit coordinated behavior that is difficult to summarize in terms of independent parts. Integrated Information Theory (IIT) provides one influential notion of system-level irreducibility, but exact computation of causal $\mathrm{\Phi }$ remains intractable except in very small systems. In this work, we introduce ${\mathrm{\Phi }}_{\mathrm{spectral}}$, a scalable observer-relative statistic defined on pairwise mutual information networks extracted from multivariate time-series data. A normalized graph Laplacian and its Fiedler vector identify a bipartition of the mutual information graph, and ${\mathrm{\Phi }}_{\mathrm{spectral}}$ reports the normalized weight of informational coupling crossing that cut. The measure is inspired by IIT’s concern with irreducibility but is not equivalent to intrinsic causal $\mathrm{\Phi }$: it is pairwise, undirected, and functional/statistical rather than intervention-based. We evaluate it on four exploratory simulation regimes: random oscillators, a transitional Kuramoto-like synchronization regime, a perfectly synchronized regime, and a combinatorial threshold-linear network (CTLN). Across these cases, ${\mathrm{\Phi }}_{\mathrm{spectral}}$ is most useful as a measure of observer-relative integration under second-order dependencies, separating redundancy-dominated from transiently differentiated regimes. The current results should be read as a proof-of-concept rather than as a formal validation against exact IIT. We discuss relations to weak IIT, Integrated World Modeling Theory (IWMT), and the perturbational complexity index (PCI), and we outline the stationary benchmarking and small-system validation needed for stronger causal claims. Full article

The stability of solar-dependent energy systems is vital for urban sustainability, but it is increasingly threatened by compound energy risks (CERs), events where low photovoltaic generation coincides with high electricity demand. This study addresses a critical knowledge gap by disentangling the co-evolving structural and magnitude drivers of these events to identify their propagation pathways and the most vulnerable districts. To achieve this, a novel hybrid framework was developed to provide a high-resolution, spatiotemporal assessment of both risk dimensions across Singapore’s 41 districts. Structural risk was mapped by integrating an undirected co-occurrence network, quantified using Mutual Information (MI), with a directed influence network derived from Bayesian Network Theory (BNT). Concurrently, magnitude risk was assessed through a copula-based analysis of joint probabilities for historical and future climate conditions, using Singapore’s new V3 dataset under multiple Shared Socioeconomic Pathways (SSPs). The results reveal a significant shift in the compound energy risk landscape. Structurally, the network of risk propagation evolves from a historically diffuse configuration to a consolidated system dominated by clusters of 8 to 9 highly interconnected districts under the SSP245 scenario. Under the high-diffusion SSP585 scenario, this evolution is expanded by the addition of 4 more districts. At the same time, the magnitude of risk intensifies across identified hotspot districts. This synthesis uncovers a critical feedback dynamic: districts such as 29, 36, and 40 not only serve as key structural hubs but also experience sharp increases in event probability, with their return periods for extreme compound events collapsing from over 50 years historically to the 10–20-year range. This forms a self-reinforcing loop of systemic vulnerability. These findings indicate that Singapore’s energy security will become increasingly exposed to climate-driven risks that propagate through this consolidated network, requiring targeted spatial adaptation to ensure long-term grid sustainability. Full article

Cookbooks serve as structured records of both ingredient repertoires and the underlying processing logics that define a culture’s culinary identity. By modeling five Transylvanian ethnic traditions—Hungarian, Romanian, Transylvanian Saxon, Jewish, and Armenian—as weighted, undirected co-occurrence networks, we found that interethnic connectivity is driven primarily by technological processes rather than simple ingredient presence. Using purposive sampling, we compiled a harmonized corpus of 1409 recipes and applied explicit ingredient normalization (retention, aggregation, and deconstruction) and a 14-class functional taxonomy. We computed density, clustering, modularity, and centrality measures and compared cuisines with a binary Jaccard index, both at the category level and within four course types. Category networks reveal an exceptionally tight Hungarian–Romanian–Armenian triangle (J > 0.95), whereas course-level results show that main dishes exhibit the strongest divergence (J < 0.28). These results support a layered identity model of Transylvanian gastronomy: while shared confectionery frameworks in desserts dissolve ethnic boundaries (M < 0.17), main dishes actively guard cultural boundaries through distinct technological signatures. Full article

The increasing complexity of modern gate-level circuits significantly degrades the efficiency of existing Hardware Trojan detection methods. Community partitioning is an efficient structural decomposition technique to address efficiency and scalability issues, yet current community-based detection schemes rely primarily on undirected graph modeling. To address these issues, we propose an improved structure-aware community detection method for gate-level netlists, aiming to enhance the detection and localization capabilities of small-scale Hardware Trojans. First, an expanded dataset with structural diversity of clean and Trojan-inserted circuits is constructed by extending Trust-Hub benchmark circuits. Then, a directed and resolution-adaptive Louvain community detection algorithm is proposed—by introducing directed modularity, resolution parameters, and logic-gate semantic weighting, fine-grained community partitioning is achieved. On this basis, topological, functional, and anomaly features are extracted from community subgraphs, and a detection framework is built by combining graph neural networks and traditional detection models. All experiments are conducted on a unified platform equipped with an Intel (R) Core (TM) i7-10750H processor and an NVIDIA GeForce RTX 2060 GPU. Experimental results show that compared with configurations using the original Louvain partitioning and traditional features, the proposed method achieves significant improvements in both detection accuracy and localization capability. After introducing the improved community partitioning and feature design, the optimal model (CommunityGAT) yields a 3.3% increase in TPR and a 10.8% increase in ALC, verifying the method’s effectiveness in detecting small-scale concealed Trojans. Full article

Truck platooning enabled by V2X and cooperative driving can reduce aerodynamic drag and consequently decrease fuel consumption and CO₂ emissions. Meanwhile, hub-and-spoke courier networks require strategic decisions on hub locations, allocation, and line-haul routing. This paper introduces an integrated Hub Location-Platoon Routing Problem (HLPRP) that jointly optimizes (i) hub selection and single allocation of spokes; (ii) the departure hubs where platoons are formed; (iii) line-haul (inter-hub) service design and route selection; and (iv) demand routing, while internalizing monetized carbon benefits from platooning. A variable neighborhood search-based simulated annealing solution framework is developed to eliminate duplicated hub pair representations induced by network symmetry. Computational experiments on benchmark and large-scale North China instances demonstrate that the proposed approach consistently produces high-quality solutions within practical runtimes. The results indicate that the optimal network structure is primarily driven by transportation cost trade-offs and is further shaped by platoon-enabling investment and the associated carbon benefit, which concentrates on a subset of high-volume inter-hub corridors. Overall, the proposed framework provides a decision support approach for designing low-carbon courier line-haul networks. Full article

YouTube is a de facto learning environment for athletes seeking fast, actionable nutritional guidance, yet platform dynamics may favor simplified or testimonial narratives over evidence-aligned messages. This study maps Spanish-language sports-nutrition videos to clarify who is most visible, how advice is framed, and what users encounter first. We conducted a cross-sectional, mixed-methods study of 558 YouTube videos on pre/post-exercise nutrition and supplementation. Data was coded for video types (divulgation/testimonial), claim presence, evidence links, and creator status (professional/non-professional). Exposure-adjusted metrics (View Ratio, Viewer Interaction) and nonparametric tests summarized distributions. An undirected network generated centrality rankings to select qualitative samples. Thematic analysis of titles and descriptions identified recurring rhetorical patterns and discourse modes. Divulgation videos predominated (97.3%). Evidence links were rare (0.2%). Exposure and interaction were right-skewed, indicating concentrated visibility. Non-professionals produced most videos, with older uploads and higher daily view accrual; however, interaction per view was similar across groups. Qualitative synthesis revealed two dominant discourse modes, scientific–cautious and experience–testimonial. Oversimplification and motivational cues clustered in testimonial/non-professional items; instructional language and scarce evidence links concentrated in professional/divulgation items. In Spanish sports-nutrition content, visibility is concentrated, and creator identity shapes advice framing. Evidence-aligned messages can compete when expressed with clear athletic framing, explicit caveats, and links to trustworthy sources. Full article

To overcome the limitations of current link prediction methods in effectively leveraging topological information and node importance, this paper introduces a new model called AMPS (Adaptive Multi-scale Potential-enhanced Path Similarity). The model is built on a hierarchical structure that captures both global network topology and local interaction patterns, with full compatibility for directed and undirected networks. This is achieved through a process that quantifies node potential fields, enhances multi-scale similarity, and fuses information across scales. Specifically, we define three types of potential field models, global, local, and k-hop, to flexibly measure node importance. We also introduce two complementary prediction modules: an enhanced common neighbor matrix (PCN), which uses potential fields to refine local structural details, and a feature-weighted generalized path similarity (GLP), which integrates node importance into path evaluation. The final similarity score is obtained by adaptively combining the outputs of PCN and GLP. Experiments on 12 undirected datasets and 9 directed datasets demonstrate that AMPS significantly outperforms other mainstream algorithms in terms of the AUC metric. It also exhibits strong robustness under varying training set ratios, maintaining stable advantages in both directed and undirected scenarios. This framework provides a physically intuitive, topology-aware, and high-precision solution for link prediction across various types of networks. Full article

For the first time, this paper introduces Nabla fractional calculus into the distributed Nash equilibrium (NE) seeking problem of aggregative games (AGs) with partial decision information in undirected communication networks, and proposes two novel fractional-order distributed algorithms. In the considered setting, each agent can access to only local information and collaboratively estimates the global aggregate through communication with its neighbors. Both algorithms adopt a backward-difference scheme followed by an implicit fractional-order gradient descent step. One updates local aggregate estimates via fractional-order dynamic tracking and the other uses fractional-order average dynamic consensus protocols. Under standard assumptions, convergence of both algorithms to the NE is rigorously proved using nabla fractional-order Lyapunov stability theory, achieving a Mittag-Leffler convergence rate. The feasibility of the developed schemes is verified via numerical experiments applied to a Nash-Cournot game and the coordination control of flexible robotic arms. Full article

We introduce the Onco-Hem Connectome (OHC), a patient similarity network (PSN) designed to organize real-world hemato-oncology inpatients by exploratory phenotypes with potential clinical utility. Background: Polypharmacy and drug–drug interactions (DDIs) are pervasive in hemato-oncology and vary with comorbidity and treatment intensity. Methods: We retrospectively analyzed a 2023 single-center cohort of 298 patients (1158 hospital episodes). Standardized feature vectors combined demographics, comorbidity (Charlson, Elixhauser), comorbidity polypharmacy score (CPS), aggregate DDI severity score (ADSS), diagnoses, and drug exposures. Cosine similarity defined edges (threshold ≥ 0.6) to build an undirected PSN; communities were detected with modularity-based clustering and profiled by drugs, diagnosis codes, and canonical chemotherapy regimens. Results: The OHC comprised 295 nodes and 4179 edges (density 0.096, modularity Q = 0.433), yielding five communities. Communities differed in comorbidity burden (Kruskal–Wallis ${\epsilon }^2$: Charlson 0.428, Elixhauser 0.650, age 0.125, all FDR-adjusted p < 0.001) but not in utilization (LOS, episodes) after FDR (${\epsilon }^2$ ≈ 0.006–0.010). Drug enrichment (e.g., enoxaparin $\Delta$ = +0.13 in Community 2; vinblastine $\Delta$ = +0.09 in Community 3) and principal diagnoses (e.g., C90.0 23%, C91.1 15%, C83.3 15% in Community 1) supported distinct clinical phenotypes. Robustness analyses showed block-equalized features preserved communities (ARI 0.946; NMI 0.941). Community drug signatures and regimen signals aligned with diagnosis patterns, reflecting the integration of resource-use variables in the feature design. Conclusions: The Onco-Hem Connectome yields interpretable, phenotype-level insights that can inform supportive care bundles, DDI-aware prescribing, and stewardship, and it provides a foundation for phenotype-specific risk models (e.g., prolonged stay, infection, high-DDI episodes) in hemato-oncology. Full article

Machine learning (ML) algorithms are reshaping academic research. However, there is a lack of systematic impact analysis in specific domains. We propose a framework for evaluating the knowledge landscape of domain-specific ML research. It consists of three key components: LDA (Latent Dirichlet Allocation) for topic identification, co-occurrence network construction, and influential algorithm scoring using centrality metrics. In a case study on COVID-19 research, we analyze 30,664 ML-related papers. We identify 13 research topics. We construct a symmetric undirected network to quantify algorithm influence. This analysis employs six centrality metrics: mention frequency, weighted degree, degree centrality, eigenvector centrality, closeness centrality, and betweenness centrality. Results were obtained following linear normalisation. The framework highlights the top ten most influential algorithms for each topic. It reveals the evolving impact of algorithms in COVID-19 research. The methodology is adaptable to other domains. It provides a systematic approach to understanding ML domain-specific impact. Full article

We study the process of opinion formation in an Ising social network of scientific collaborations. The network is undirected. An Ising spin is associated with each network node being oriented up (red) or down (blue). Certain nodes carry fixed, opposite opinions whose influence propagates over the other spins, which are flipped according to the majority-influence opinion of neighbors of a given spin during the asynchronous Monte Carlo process. The amplitude influence of each spin is self-consistently adapted, and a flip occurs only if this majority influence exceeds a certain conviction threshold. All non-fixed spins are initially randomly distributed, with half of them oriented up and half down. Such a system can be viewed as a model of elite influence, coming from the fixed spins, on the opinions of the crowd of non-fixed spins. We show that a phase transition occurs as the amplitude influence of the crowd spins increases: the dominant opinion shifts from that of the elite nodes to a phase in which the crowd spins’ opinion becomes dominant and the elite can no longer impose their views. Full article

In various real-world scenarios, directed graphs can express the nature of relationships more clearly than undirected ones. Meanwhile, decentralized networks have attracted increasing attention in recent years. In decentralized directed networks (e.g., the Bitcoin Network), each user maintains only their own local view of the network. To provide better services, third-party providers often need to construct a global graph based on the local views uploaded by users for downstream graph-analysis tasks. However, directly collecting users’ local views poses significant privacy risks. For directed graphs, symmetry plays an important role in restoring data balance and preserving structural integrity. In this paper, we propose DGG-LDP, a directed graph generation algorithm based on local edge differential privacy, tailored for decentralized directed graphs. The core idea of the algorithm is to balance coarse-grained and fine-grained structural information so as to preserve geometric symmetry: we first synthesize an initial graph by collecting one round of community degree vectors, and then—guided by symmetry principles—we iteratively refine the graph using a second round of noisy community degree vectors, removing redundant asymmetric edges in the community vectors to better approximate the original graph. Additionally, the algorithm incorporates graph structure learning and graph embedding techniques to mitigate the impact of noise. Experiments on four real-world datasets demonstrate the effectiveness of the proposed method. Full article