Search Results (201)

This paper establishes fundamental results for partial total domination in hypergraphs. We present tight bounds for the partial total domination number in k-uniform hypergraphs, demonstrate relationships with classical domination parameters, and provide constructive proofs using hypergraph transformation techniques. Applications in sensor networks and biological systems are discussed with supporting examples. Key results include a general upper bound of kk−1γ(H) for k-uniform hypergraphs without isolated vertices, verified through both analytic methods and computational examples. Full article

Session-based recommendation (SBR) aims to predict a user’s next item of interest by analyzing their anonymous browsing patterns. While previous studies have demonstrated considerable efficacy, they may fall short when confronted with exceedingly sparse interaction data. This paper presents a novel approach, cross-session graph and hypergraph co-guided session-based recommendation (CGH-SBR), which adeptly forecasts subsequent items while upholding efficiency and precision. First, we construct a directed graph that captures sequential dependencies by modeling cross-session item transitions, alongside building a hypergraph that encapsulates higher-order relationships between items within sessions. Subsequently, we employ two distinct graph neural networks (GNNs) to learn item representations on these two graphs separately. Further, we innovate by integrating a symmetry-aware co-guided learning framework. This framework promotes the integration of diverse perspectives and facilitates mutual learning, leveraging the data’s symmetric properties to enhance the model’s pattern recognition capabilities. Comprehensive experimentation conducted on two public datasets showcases the outstanding performance and potential of the recommendation system presented by CGH-SBR. Full article

Parallel computing techniques have been adopted in geospatial cellular automata (CA) models to improve computational efficiency, enabling large-scale complex simulations of land use and land cover (LULC) changes at fine scales. However, the spatial distribution of computational intensity often changes along with the spatiotemporal dynamics of LULC during the simulation, leading to an increase in load imbalance among computing units and degradation of the computational performance of a parallel CA. This paper presents a dynamic load balancing method based on hypergraph partitioning for multi-process parallel geospatial CA models. During the simulation, the sub-domains are dynamically reassigned to computing processes through hypergraph partitioning according to the spatial variation in computational workloads to restore load balance. In addition, a novel mechanism called Migrated-SubCellspaces-First (MSCF) is proposed to reduce the cost of workload migration by employing a non-blocking communication technique to further improve computational performance. To demonstrate and evaluate the effectiveness of our method, a parallel geospatial CA model with hypergraph-based dynamic load balancing is developed. Experiments using a dataset from California showed that the proposed dynamic load balancing method achieved a computational performance enhancement of 62.59% by using 16 processes compared with a parallel CA with static load balancing. Full article

To be successful in a circular economy, it is important to keep the cost of operationalizing remanufacturing processes low in order to retain as much value of the product as possible. Optimizing operations for disassembly, as a key process step, is therefore an important prerequisite for economically viable circular manufacturing. The generation of fit-to-resource disassembly instructions is labor-intensive and challenging because (digital) product information is often lacking at End-of-Life. With upcoming EU regulations for Eco-design for Sustainable Products in mind, including the future use of Digital Product Passports, it is time to think about standardized methods to capture disassembly information for products. First requirements from small and medium-sized remanufacturing companies have been collected and compared with available frameworks for modeling product topology, parameters, and (dis)assembly process rationale. Based on this, the disassembly hypergraph is presented as a concept for recording ‘resource-agnostic disassembly guides’ in (machine-readable) product models to determine required disassembly actions and tools ‘smartly’. The concept builds upon existing models. Additionally, suitable methods for the collection of disassembly information are explored, resulting in preliminary insights from disassembly data collection workshops. Although the approach is promising, future work is needed to expand the concept of the disassembly hypergraph with both guidelines for setting up disassembly ontologies and further systematic disassembly knowledge extraction in order to apply this as a useful means for companies to rationalize their disassembly operations. Full article

Scientific dispute and scholarly debate have traditionally served as mechanisms for arbitrating between competing scientific categorizations. However, current AI technologies lack both the ethical framework and technical capabilities to handle the adversarial reasoning inherent in scientific discourse effectively. This creates a ‘categorization conundrum’ where new knowledge emerges from opaque black-box systems while simultaneously introducing unresolved vulnerabilities to errors and adversarial attacks. Our research addresses this challenge by examining how to preserve and enhance human dispute’s vital role in the creation, development, and resolution of knowledge categorization, supported by traceable AI assistance. Building on our previous work, which introduced GRAPHYP—a multiverse hypergraph representation of adversarial opinion profiles derived from multimodal web-based documentary traces—we present three key findings. First, we demonstrate that standardizing concepts and methods through ‘Dispute Learning’ not only expands the range of adversarial pathways in scientific categorization but also enables the identification of GRAPHYP model extensions. These extensions accommodate additional forms of human reasoning in adversarial contexts, guided by novel philosophical and methodological frameworks. Second, GRAPHYP’s support for human reasoning through graph-based visualization provides access to a broad spectrum of practical applications in decidable challenging categorizations, which we illustrate through selected case studies. Third, we introduce a hybrid analytical approach combining probabilistic and possibilistic methods, applicable to diverse classical research data types. We identify analytical by-products of GRAPHYP and examine their epistemological implications. Our discussion of standardized representations of documented adversarial uses highlights the enhanced value that structured dispute brings to elicit differential categorizations in the scientific discourse. Full article

In this paper, we consider the minimal vertex cover and minimal dominating sets with capacity and/or connectivity constraint enumeration problems. We develop polynomial-delay enumeration algorithms for these problems on bounded-degree graphs. For the case of minimal connected vertex covers, our algorithms run in polynomial delay, even on the class of d-claw free graphs. This result is extended for bounded-degree graphs and outputs in quasi-polynomial time on general graphs. To complement these algorithmic results, we show that the minimal connected vertex cover, minimal connected dominating set, and minimal capacitated vertex cover enumeration problems in 2-degenerated bipartite graphs are at least as hard as enumerating minimal transversals in hypergraphs. Full article

The resource scheduling optimization problem in predictive maintenance is a complex operational research challenge involving reasoning about stochastic failure models and the dynamic allocation of repair resources. In recent years, resource scheduling methods based on deep learning have been increasingly applied in this field, demonstrating promising performances. Among these, resource scheduling algorithms based on heterogeneous graphs have shown exceptional results in multi-objective optimization tasks. However, conventional graph neural networks primarily operate on binary relational graphs, which struggle to effectively utilize data in multi-relational settings, thereby limiting the scheduler’s performance. To address this limitation, this paper proposes a heterogeneous hypergraph-based resource scheduling algorithm for aircraft maintenance tasks to tackle the challenges of higher-order and many-to-many relationship processing inherent in traditional graph neural networks. Specifically, the proposed algorithm defines aircraft nodes and maintenance personnel nodes while introducing decision nodes and state nodes to construct hyperedges. It employs hypergraph convolution with a multi-head attention mechanism to learn the long-term value of decisions, followed by policy selection based on a Markov decision process. This method offers a lightweight, non-parametric dynamic scheduling solution capable of robust learning in highly stochastic environments. Comparative experiments conducted on three datasets of varying scales demonstrate that the proposed method outperforms both heuristic algorithms and existing deep learning methods in terms of its optimization performance on M1 and M2 metrics. Furthermore, it surpasses resource scheduling algorithms based on heterogeneous graph neural networks across multiple metrics. Full article

Many important plants’ agronomic traits, such as crop yield, stress tolerance, and other traits, are controlled by multiple genes and exhibit complex inheritance patterns. Traditional breeding methods often encounter difficulties in dealing with these traits due to their complexity. However, genomic selection (GS), which utilizes high-density molecular markers across the entire genome to facilitate selection in breeding programs, excels in capturing the genetic variation associated with these traits. This enables more accurate and efficient selection in breeding. The traditional crop genome selection model, based on statistical methods or machine learning models, often treats samples as independent entities while neglecting the abundance latent relational information among them. Consequently, this limitation hampers their predictive performance. In this study, we proposed a novel crop genome selection model based on hypergraph attention networks for genomic prediction (HGATGS). This model incorporates dynamic hyperedges that are designed based on sample similarity to validate the efficacy of high-order relationships between samples for phenotypic prediction. By introducing an attention mechanism, it assigns weights to different hyperedges and nodes, thereby enhancing the ability to capture kinship relationships among samples. Additionally, residual connections are incorporated between hypergraph convolutional layers to further improve model stability and performance. The model was validated on datasets for multiple crops, including wheat, corn, and rice. The results showed that HGATGS significantly outperformed traditional statistical methods and machine learning models on the Wheat 599, Rice 299, and G2F 2017 datasets. On Wheat 599, HGATGS achieved a correlation coefficient of 0.54, a 14.9% improvement over methods like R-BLUP and BayesA (0.47). On Rice 299, HGATGS reached 0.45, a 66.7% increase compared to other models like R-BLUP and SVR (0.27). On G2F 2017, HGATGS attained 0.88, slightly surpassing other models like R-BLUP and BayesA (0.87). We conducted ablation experiments to compare the model’s performance across three datasets, and found that the model integrating hypergraph attention and residual connections performed optimally. Subsequent comparisons of the model’s prediction performance with dynamically selected different k values revealed optimal performance when K = (3,4). The model’s prediction performance was also compared across different single nucleotide polymorphisms (SNPs) and sample sizes in various datasets, with HGATGS consistently outperforming the comparison models. Finally, visualizations of the constructed hypergraph structures showed that certain nodes have high connection densities with hyperedges. These nodes often represent varieties or genotypes with significant impacts on traits. During feature aggregation, these high-connectivity nodes contribute significantly to the prediction results and demonstrate better prediction performance across multiple traits in multiple crops. This demonstrates that the method of constructing hypergraphs through correlation relationships for prediction is highly effective. Full article

In vehicular ad hoc networks (VANETs), the presence of untrustworthy nodes poses a significant threat, impacting the network’s reliability. This has led to the emergence of node-centric misbehavior detection as a crucial aspect of VANET security, focusing on the behavior of vehicles rather than the content of their interactions. While the trust model is a popular approach, the computational complexity of trust computations and management in VANETs is attributed to the intricate relationships among vehicles and the dynamic autonomous movement of nodes. To tackle these challenges, we developed a hybrid trust model scheme for node-centric misbehavior detection. Our method represents complex vehicular relationships using a hyper-graph within a dynamic behavior-homogeneous cluster. The model incorporates direct and indirect trust in a multi-layered hybrid trust framework, enabling accurate computation of the aggregate trust level for each cluster member vehicle. Experimental results demonstrate the effectiveness of our scheme, particularly in high-density vehicle cooperation scenarios, highlighting its promising ability to detect misbehaving nodes. Full article

With the proliferation of smart devices and the emergence of high-bandwidth applications, Unmanned Aerial Vehicle (UAV)-assisted Device-to-Device (D2D) communications and Non-Orthogonal Multiple Access (NOMA) technologies are increasingly becoming important means of coping with the scarcity of the spectrum and with high data demand in future wireless networks. However, the efficient coordination of these techniques in complex and changing 3D environments still faces many challenges. To this end, this paper proposes a NOMA-based multi-UAV-assisted D2D communication model in which multiple UAVs are deployed in 3D space to act as airborne base stations to serve ground-based cellular users with D2D clusters. In order to maximize the system throughput, this study constructs an optimization problem of joint channel assignment, trajectory design, and power control, and on the basis of these points, this study proposes a joint dynamic hypergraph Multi-Agent Deep Q Network (DH-MDQN) algorithm. The dynamic hypergraph method is first used to construct dynamic simple edges and hyperedges and to transform them into directed graphs for efficient dynamic coloring to optimize the channel allocation process; subsequently, in terms of trajectory design and power control, the problem is modeled as a multi-agent Markov Decision Process (MDP), and the Multi-Agent Deep Q Network (MDQN) algorithm is used to collaboratively determine the trajectory design and power control of the UAVs. Simulation results show the following: (1) the proposed algorithm can achieve higher system throughput than several other benchmark algorithms with different numbers of D2D clusters, different D2D cluster communication spacing, and different UAV sizes; (2) the proposed algorithm designs UAV trajectory optimization with a 27% improvement in system throughput compared to the 2D trajectory; and (3) in the NOMA scenario, compared to the case of no decoding order constraints, the system throughput shows on average a 34% improvement. Full article

In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to generate synthetic retail transaction data, specifically focusing on a novel system architecture that combines consumer behavior modeling with stock-keeping unit (SKU) availability constraints to address real-world assortment optimization challenges. We diverge from conventional methodologies by integrating SKU data into our GAN architecture and using more sophisticated embedding methods (e.g., hyper-graphs). This design choice enables our system to generate not only simulated consumer purchase behaviors but also reflects the dynamic interplay between consumer behavior and SKU availability—an aspect often overlooked, among others, because of data scarcity in legacy retail simulation models. Our GAN model generates transactions under stock constraints, pioneering a resourceful experimental system with practical implications for real-world retail operation and strategy. Preliminary results demonstrate enhanced realism in simulated transactions measured by comparing generated items with real ones using methods employed earlier in related studies. This underscores the potential for more accurate predictive modeling. Full article

Quantum contextuality plays a significant role in supporting quantum computation and quantum information theory. The key tools for this are the Kochen–Specker and non-Kochen–Specker contextual sets. Traditionally, their representation has been predominantly operator-based, mainly focusing on specific constructs in dimensions ranging from three to eight. However, nearly all of these constructs can be represented as low-dimensional hypergraphs. This study demonstrates how to generate contextual hypergraphs in any dimension using various methods, particularly those that do not scale in complexity with increasing dimensions. Furthermore, we introduce innovative examples of hypergraphs extending to dimension 32. Our methodology reveals the intricate structural properties of hypergraphs, enabling precise quantifications of contextuality. Additionally, we investigate several promising applications of hypergraphs in quantum communication and quantum computation, paving the way for future breakthroughs in the field. Full article

Persistent topological Laplacians constitute a new class of tools in topological data analysis (TDA). They are motivated by the necessity to address challenges encountered in persistent homology when handling complex data. These Laplacians combine multiscale analysis with topological techniques to characterize the topological and geometrical features of functions and data. Their kernels fully retrieve the topological invariants of corresponding persistent homology, while their non-harmonic spectra provide supplementary information. Persistent topological Laplacians have demonstrated superior performance over persistent homology in the analysis of large-scale protein engineering datasets. In this survey, we offer a pedagogical review of persistent topological Laplacians formulated in various mathematical settings, including simplicial complexes, path complexes, flag complexes, digraphs, hypergraphs, hyperdigraphs, cellular sheaves, and N-chain complexes. Full article

In recent years, it has become a hot topic to combine graph neural networks with contrastive learning, a method which has been applied not only in recommendation tasks but has also achieved impressive results in many fields, such as text processing and spatio-temporal modeling. However, existing methods are still constrained by several issues: (1) Most graph learning methods do not explore the imbalance of node and edge type distribution caused by different user–item interactions. (2) The randomness of data expansion and sampling strategies in contrastive learning may lead to confusion about the importance of key items for users. To overcome the problems, in this paper, we propose an explanation-guided contrastive recommendation model based on interactive message propagation and dual-hypergraph convolution (ECR-ID). Specifically, we designed two different interactive propagation mechanisms for the user–item dual-hypergraph sets to promote comprehensive dynamic information propagation and exchange, which further mitigates the imbalance problem of hyperedges and nodes in the hypergraph convolution, as well as the propagation loss of synergistic information between nodes. In addition, we developed an explanation-guided contrastive learning framework, which highlights the important items in user–item interactions through an explanation-based approach and guided the training of the contrastive learning framework based on the differences in the importance scores of the items, thus generating accurate positive and negative views and improving the contrastive learning performance. Finally, we integrated the contrastive learning framework with the dual-hypergraph networks based on joint training to further improve the recommendation performance. Extensive experimental evaluations on real datasets show that ECR-ID outperforms state-of-the-art recommendation algorithms. In the future, we will conduct in-depth tests based on a wider range of real-world datasets to alleviate the limitation that the existing experimental datasets all comprise data from single business services like Alibaba and Amazon, thus validating the effectiveness of our model more comprehensively. Full article

The objective of cross-city recommendation is to suggest points-of-interest (POI) in the target city that may be of interest to users, based on their check-in records from their source city. Although significant progress has been made in studying user preference transfers, there is a lack of research focusing on personalized user preference transfers. Furthermore, the mining of user preferences from the source city is impacted by errors and missing information. To address these challenges, this paper proposes a Check-In Heterogeneous Hypergraph and Personalized Preference Transfers for Cross-City POI Recommendation Method (CHHPPT). Firstly, a check-in heterogeneous hypergraph network is introduced in the user source city preference-mining module. This network, through Heterogeneous Hypergraph Embeddings (HHE), captures user preferences in the source city, thereby mitigating the impact of errors and missing information on user preference. Subsequently, in the user-personalized preference transfer module, a user’s transferable features are obtained through a POI aggregation network. These features are then combined with a meta-network and transfer networks to achieve user-personalized preference transfer. Finally, in the target city point-of-interest recommendation module, a POI-geographical graph is constructed using the geographical information of POI. This graph, in conjunction with category information, yields a joint embedding representation. The final recommendation is achieved by integrating the user-personalized preference transfer embeddings with the target city’s POI embeddings. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of CHHPPT in cross-city recommendation tasks. Full article