Explore the vast range of titles available.

In order to understand the online international dissemination of Chinese culture through online games, this paper constructs a computational model of cultural dissemination based on a hypergraph. The model constructs a network topology based on hypergraph theory, divides the states of individuals spreading Chinese cultural information in online social networks, and calculates the transfer probability of individual states based on the rules of cultural information spreading. The analysis of the algorithms reveals that the maximum infection ratio of the model in this paper decreases slowly, and the coverage rate of immune nodes reaches 0.9, indicating good stability and adaptation performance. The model accurately simulates the process of Chinese culture diffusion in the Minecraft community. The mean value of the evaluation of the six entries on learning motivation and attitude is 3.73 points, which indicates that the use of Minecraft helps improve students’ motivation for learning about Chinese culture.

The study of citation networks is of interest to the scientific community. However, the underlying mechanism driving individual citation behavior remains imperfectly understood, despite the recent proliferation of quantitative research methods. Traditional network models normally use graph theory to consider articles as nodes and citations as pairwise relationships between them. In this paper, we propose an alternative evolutionary model based on hypergraph theory in which one hyperedge can have an arbitrary number of nodes, combined with an aging effect to reflect the temporal dynamics of scientific citation behavior. Both theoretical approximate solution and simulation analysis of the model are developed and validated using two benchmark datasets from different disciplines, i.e. publications of the American Physical Society (APS) and the Digital Bibliography & Library Project (DBLP). Further analysis indicates that the attraction of early publications will decay exponentially. Moreover, the experimental results show that the aging effect indeed has a significant influence on the description of collective citation patterns. Shedding light on the complex dynamics driving these mechanisms facilitates the understanding of the laws governing scientific evolution and the quantitative evaluation of scientific outputs.

The purpose of this work is to advance in the computational study of connectome graphs from a topological point of view. Specifically, starting from a sequence of hypergraphs associated to a brain graph (obtained using the Boundary Scale model, BS2), we analyze the resulting scale-space representation using classical topological features, such as Betti numbers and average node and edge degrees. In this way, the topological information that can be extracted from the original graph is substantially enriched, thus providing an insightful description of the graph from a clinical perspective. To assess the qualitative and quantitative topological information gain of the BS2 model, we carried out an empirical analysis of neuroimaging data using a dataset that contains the connectomes of 96 healthy subjects, 52 women and 44 men, generated from MRI scans in the Human Connectome Project. The results obtained shed light on the differences between these two classes of subjects in terms of neural connectivity.

The birth of fog computing has given rise to many security threats. Distributed denial of service (DDoS) attacks by intruders on fog nodes will cause system resources to be illegally appropriate. Intrusion detection system (IDS) is a powerful technology that can be used to resist DDoS attacks. In our previous research, we have proposed a fog computing intrusion detection system (FC-IDS) framework. In this paper, we mainly analyze and model the DDoS attacks under the framework of FC-IDS. We propose a hypergraph clustering model based on Apriori algorithm. This model can effectively describe the association between fog nodes which are suffering from the threat of DDoS. Through simulation, we verify that the resource utilization rate of the system can be effectively promoted through the DDoS association analysis.

A multi-target robust observation method for satellite constellations based on hypergraph algebraic connectivity and observation precision theory is proposed to address the challenges posed by the surge in space targets and system failures. First, a precision metric framework is constructed based on nonlinear batch least squares estimation theory, deriving the theoretical precision covariance through cumulative observation matrices to provide a theoretical foundation for tracking accuracy evaluation. Second, multi-satellite collaborative observation is modeled as an edge-dependent vertex-weighted hypergraph, enhancing system robustness by maximizing algebraic connectivity. A constrained simulated annealing (CSA) algorithm is designed, employing a precision-guided perturbation strategy to efficiently solve the optimization problem. Simulation experiments are conducted using 24 Walker constellation satellites tracking 50 targets, comparing the proposed method with greedy algorithm, CBBA, and CSA-bipartite Graph methods across three scenarios: baseline, maneuvering, and failure. Results demonstrate that the CSA-hypergraph method achieves 0.089 km steady-state precision in the baseline scenario, representing a 41.4% improvement over traditional methods; in maneuvering scenarios, detection delay is reduced by 34.3% and re-achievement time is decreased by 47.4%; with a 30% satellite failure rate, performance degradation is only 9.8%, significantly outperforming other methods.

We study both H and E / Z -eigenvalues of the adjacency tensor of a uniform multi-hypergraph and give conditions for which the largest positive H or Z -eigenvalue corresponds to a strictly positive eigenvector. We also investigate when the E -spectrum of the adjacency tensor is symmetric.

The modeling of the graphical representation of business processes (BP) or workflows in enterprise information systems (IS) is often to represent various activities, entities, relations, functions, and communicate between them in an enterprise to achieve the major goal of operational support. In this work, we decided to use graph representation approaches, especially hypergraphs to depict the complex relationships that exist among the artifacts and constituents of BP for more efficient and accurate manipulations. We used bipartite and further hypergraph formats for storing and curating data. We have investigated the various descriptive languages and representation models of BP as process modeling, workflow and process integration, and object-oriented (OO) languages. We have carried out experiments using different approach combinations, but for observing quiltedrepresentation, we focused on the main consistencies of “DBP”. As the final approach, we used the “DBP” stream and data schemes that are defined by us to proceed with using pure Python for manually generating data and external Python libraries to store, curate, and visualize “DBP”.

We present a method to extract temporal hypergraphs from sequences of 2-dimensional functions obtained as solutions to Optimal Transport problems. We investigate optimality principles exhibited by these solutions from the point of view of hypergraph structures. Discrete properties follow patterns that differ from those characterizing their continuous counterparts. Analyzing these patterns can bring new insights into the studied transportation principles. We also compare these higher-order structures to their network counterparts in terms of standard graph properties. We give evidence that some transportation schemes might benefit from hypernetwork representations. We demonstrate our method on real data by analyzing the properties of hypernetworks extracted from images of real systems.

Graph partitioning and graph clustering are ubiquitous subtasks in many applications where graphs play an important role. Generally speaking, both techniques aim at the identification of vertex subsets with many internal and few external edges. To name only a few, problems addressed by graph partitioning and graph clustering algorithms are: li>What are the communities within an (online) social network?How do I speed up a numerical simulation by mapping it efficiently onto a parallel computer?How must components be organised on a computer chip such that they can communicate efficiently with each other?What are the segments of a digital image?Which functions are certain genes (most likely) responsible for?The 10th DIMACS Implementation Challenge Workshop was devoted to determining realistic performance of algorithms where worst case analysis is overly pessimistic and probabilistic models are too unrealistic. Articles in the volume describe and analyse various experimental data with the goal of getting insight into realistic algorithm performance in situations where analysis fails. This book is published in cooperation with the Center for Discrete Mathematics and Theoretical Computer Science.