Predicting future dynamics on networks is challenging, especially when the complete and accurate network topology is difficult to obtain in real-world scenarios. Moreover, the higher-order interactions among nodes, which have been found in a wide range of systems in recent years, such as the nets connecting multiple modules in circuits, further complicate accurate prediction of dynamics on hypergraphs. In this work, we proposed a two-step method called the topology-agnostic higher-order dynamics prediction (TaHiP) algorithm. The observations of nodal states of the target hypergraph are used to train a surrogate matrix, which is then employed in the dynamical equation to predict future nodal states in the same hypergraph, given the initial nodal states. TaHiP outperforms three latest Transformer-based prediction models in different real-world hypergraphs. Furthermore, experiments in synthetic and real-world hypergraphs show that the prediction error of the TaHiP algorithm increases with mean hyperedge size of the hypergraph, and could be reduced if the hyperedge size distribution of the hypergraph is known.

In the contemporary landscape of information dissemination, it is imperative to not only promote positive information but also to effectively manage the spread of negative content to enhance public comprehension of diseases. This study introduces an epidemiological model that integrates both positive and negative information dynamics within heterogeneous networks. Initially, we employ an activity-driven network to simulate a realistic information network, complemented by a two-layer spreading model. This model utilizes a node-central unaware-aware-unaware-susceptible-infected-susceptible (UAU-SIS) framework to delineate the microscopic diffusion dynamics. Subsequently, we propose a combined strategy that aims to inhibit the dissemination of negative information while simultaneously promoting positive messages. This approach is designed to elevate public awareness and self-protection against diseases, thereby reducing the number of infected individuals and controlling associated costs. Moreover, we employ a neural ordinary differential equation control (NODEC) mechanism to determine the optimal control signal. This method enables the learning of optimal control performance and enhances the efficiency of the solution process. The efficacy of the proposed optimal control strategy is validated through numerical simulations. The results indicate that the neural ordinary differential equation (ODE) control can effectively approximate the optimal control signals, and the strategy can significantly mitigate the final scale of infection and reduce control costs.

The spread of epidemics is a complex process influenced by multiple social relations, including families, schools, and companies. Group interactions represented by a simplicial complex have been shown to impact the dynamics of epidemic transmission significantly. However, the previously proposed higher order contagion models only consider the propagation process within the higher order structure but lack a description of the consistency of the node states in the structure. To address this problem, we provide solutions to create a solvable network model that shows network clusters and node state consistency tendencies, capable of capturing the coexistence of interacting groups. We analyze the bistable region and epidemic threshold of the higher order propagation dynamics and explore the effect of the state update of the nodes on the model in: 1) random regular simplicial networks; 2) triangular simplex-lattice networks; 3) star-simplicial networks; and 4) heterogeneous simplicial networks. Our theoretical analysis, complemented by Monte Carlo numerical simulations, demonstrates that the proposed model accurately captures phase transitions and the bistable region created by higher order interactions. In addition, we find that the incorporation of intersimplex interaction mechanisms greatly reduces the epidemic threshold of the system and produces a larger bistable region, in which the dynamics of the system are heavily affected by the density and location of the initially infected nodes. Our findings contribute to a better understanding of higher order interactions in complex networked systems.

The prediction of future dynamics on networks is a challenge. Unfortunately, due to the noise in the sampling process, or the low resolution of observational data, it is hardly feasible to get the complete topology of real-world networks. Moreover, the higher-order interactions among nodes add the difficulty to accurate prediction of dynamics on networks. In this work, we proposed a two-step approach for higher-order dynamics prediction without network topology. First, the observations of nodal dynamics of a specific dynamical model on unknown hypergraphs are collected to solve an optimization problem by Ridge regression, to obtain a surrogate incidence matrix. Second, the prediction is obtained by iterating the equation of the dynamical model with the surrogate incidence matrix. We define the average relative prediction error to evaluate the performance of our prediction method, and a wide range of hypergraph dynamics with different parameters are predicted. The prediction accuracy is positively correlated with the number of hyperedges the hypergraph contains and the contact rate in the dynamical model.

Previous studies have focused on the assumption that individual attitudes toward epidemics, epidemic-related information, and vaccination are identical with much less effort attempting to consider psychological factors and individual heterogeneity. In this article, we employ a two-layer susceptible–infected–recovered–vaccinated/unaware–aware–unaware (SIRV-UAU) coupled network to depict the interplay between epidemic spreading and information diffusion. We propose an imperfect vaccination evolutionary game model integrating prospect theory (PT) to explore the effect of individual subjective perception and social difference on epidemic spreading and vaccination equilibrium. We find that the individual social difference in the epidemic spreading layer has a more significant impact on the epidemic threshold than that in the information diffusion layer. Individual psychological perception and vaccination behavior decision-making determine the vaccination equilibrium: under the PT, vaccination equilibrium increases with the decrease of the rationality coefficient when the vaccination cost is small. Furthermore, we analyze the effect of social differences on individual vaccination behavior decision-making and find that vaccination equilibrium increases with the increase of social reinforcement strength and primary protective probability. Finally, we discuss the effect of infection cost on vaccination equilibrium when the infection cost of vaccinated individuals is less than that of unvaccinated individuals and observe that a small infection cost helps improve the vaccination equilibrium.

Privacy concerns have long been a nuisance for users when using online social networks (OSNs), and most OSNs require users to configure the privacy settings themselves. Researchers are paying attention to the recommendation of privacy settings for OSN users, as some studies have found it difficult for users to understand and to use. This paper addresses the problem of recommending user-level privacy settings by leveraging the user-generated content (UGC) sequences and account information. We first analyze the differences of users with different privacy settings using the crawled Twitter dataset. Further, we formalize the privacy settings recommendation problem as a multi-label classification task and propose a deep learning-based privacy settings recommendation approach, PrivacyRec. It adopts Recurrent Neural Networks (RNNs) for the UGC sequences modeling to extract deep representations of users' semantic preferences and interaction behavior. In addition, it fuses the above representations with processed account portrait feature through attention mechanism and infers the privacy settings. We evaluate PrivacyRec using two real-world datasets and compare it with several baseline methods. The experimental results demonstrate the effectiveness of PrivacyRec's ability to recommend privacy settings.

The integration of traditional Chinese medicine (TCM) and modern medicine (MM) focuses on the wellness and healing of the whole person, and promises an improved treatment. Yet it is not well known what the correlation between TCM symptoms and MM symptoms is, and how such correlation changes in a certain disease. In this paper, we construct the symptom networks and compare the topology of the networks between the fatty liver disease (FLD) patients group and the healthy controls group, with the physical and TCM examination data. Specifically, we explore the correlation between TCM and MM symptoms. Furthermore, the centrality metrics such as eigenvector centrality, h-degree and c-index, are used to identify the significant symptoms. Moreover, to validate the effectiveness of the identification of significant symptoms, we quantify the ability of these symptoms as biomarkers to distinguish FLD patients and healthy controls. Our results demonstrate that the construction of symptom networks and centrality metrics shed a new light on the analysis of the correlation among symptoms and the recognition of significant symptoms.

As the access to high resolution datasets from real physical proximity becomes more and more convenient, plenty of temporal network models have been proposed to model human interaction patterns. However, less work has been devoted to investigating that to what degree the contact network expresses human memory, which is much crucial to forming a realistic network structure. Here we analyze three empirical datasets which reflect temporal human face-to-face interactions. We first quantify the strength of contact memory on the agent level and network level, respectively. We find that human interactions have the short-term memory. Then, we study the difference between the duration of the consecutive interaction events of the same two agents. We find that the contact memory exists in the duration of the individual interaction events.

Zero-determinant strategies provide a novel perspective for revealing the relation between a game strategy and the cost, and play an important role in effectively controlling and optimizing the costs against virus spreading in vaccination games. We explore in this paper how the social cost can be optimized when a self-interested individual adopts the newly proposed admin-cost-based zero-determinant strategy (AZDS) to unilaterally control his own cost. In contrast to the traditional belief of the conflicts between personal interest and public (social) interest, we find that the AZDS is a win-win mechanism which presents the advantage in optimizing both the individual cost and social cost simultaneously. Taking into account the differences between the individuals (nodes) in a network, we investigate the topological influence on the performance of the AZDS implemented by the administrator, and conclude that the control capability of the administrator depends on not only one's degree but also the network average degree, and the AZDS implemented by the administrators with different degrees have different sensitivities to the change of economic incentive. Some real social networks with community structure illustrate the effectiveness of the AZDS by introducing the coarse graining method.

The location prediction issue impacts a wide range of practical areas ranging from urban planning to epidemic controlling. Numerous previous studies regarding location prediction have been carried out based on both individual historical trajectories and traces of the dense social ties of individuals such as friend and in-role. However, a kind of the so-called Familiar Stranger social tie has been discovered and identified recently, which was neglected previously and may improve the location prediction. In this paper, we propose a novel location prediction method which first takes the trajectories of the familiar stranger of individuals into account. We validate our method to achieve better performance with multiple social ties to predict locations of users with three empirical human traces datasets.

The vulnerability of a network may suffer tremendous economic losses. From the perspective of network nodes, network security depends not only on the behavior of a node but also on that of its neighbours. We propose a non-cooperative networking vaccination game with economic incentive mechanism under a susceptible-infected-susceptible (SIS) epidemic process. In the game each node individually decides on whether or not to invest in anti-virus protection strategies to get vaccinated such as install an anti-virus software or a firewall. On account of the competition of interests among nodes, we present a zero-determinant (ZD) strategy for a node as the administrator who takes the security of overall network into account and plays a major role in minimizing the social cost. Finally, we evaluate the performance of ZD strategies and compare the proposed method with other strategies.

Human brain determines the ability of perceiving and behaving. Previous works have introduced network science and control theory to stimulate the brain states and functions. In this work, network controllability theory is utilized to study the structural controllability of both temporal and static functional brain networks. We find that only a few nodes are the required driver nodes to structurally control the static functional brain networks. Specially, some regions of interest (ROIs) in cerebellums with low connectivity play important roles in controlling functional brain networks. Structural controllability of temporal functional brain networks is more complicated. Control centrality of nodes which presents the ability to control other nodes does not only depend on the degree but also on the location of the nodes via studying the temporal functional brain network.