Influence maximization (IM) aims to select a small set of seed nodes whose activation triggers a maximal cascade. Existing methods typically assume access to the network topology or a reliable surrogate, which is often unavailable in practice due to noisy, privacy-protected, or partially observed links. These settings pose two challenges: (1) the lack of explicit topology removes a key inductive constraint for diffusion modeling, undermining influence estimation, and (2) nonlinear, temporally dependent node interactions yield complex multivariate time series that hinder topology inference. We propose DynaFLUX, an end-to-end generative framework for IM under hidden topology. DynaFLUX learns a compact surrogate of latent dynamics directly from observed time series, and jointly optimizes a seed-selection policy via reinforcement learning. A self-attention pointer network captures long-range dependencies for seed generation, while an influence-prediction module infers a surrogate topology and uses Monte Carlo diffusion to provide policy-gradient rewards. Experiments show that DynaFLUX accurately identifies influential spreaders and consistently outperforms state-of-the-art baselines in unseen topology scenarios.

Evaluating node influence is fundamental for identifying key nodes in complex networks. Existing methods typically rely on generic indicators to rank node influence across diverse networks, thereby ignoring the individualized features of each network itself. Actually, node influence stems not only from general features but also multiscale individualized information encompassing specific network structure and task. Here, we design an active learning (AL) architecture to predict node influence quantitatively and precisely, which samples representative nodes based on graph entropy correlation matrix integrating multiscale individualized information. This brings two intuitive advantages: 1) discovering potential high-influence but weak-connected nodes and 2) improving the influence maximization (IM) strategy by deducing influence interference. Significantly, our architecture demonstrates exceptional transfer learning capabilities across multiple types of networks, which can identify those key nodes with large disputation across different existing methods. In addition, our approach, combined with a simple greedy algorithm, exhibits dominant performance in solving the IM problem (IMP). This architecture holds great potential for applications in graph mining and prediction tasks.

Reliability evaluation of complex network is one of main topics in complex engineering systems, especially for Internet of Things (IoT). The reliability of IoT partially depends on its large-scale network. Especially, the lifetime distribution of large-scale network is critical for its health management. However, the large scale of the network usually leads to an expensive simulation time cost. Instead of direct simulation, we propose a method to infer the large-scale network lifetime using small-scale networks with the universal scaling function. We first find the scaling relationships between network lifetime and network size in a network model with failure coupling for two-dimensional square lattice network and Cayley tree network. Network lifetime with different size can be described by one universal scaling function. Then we perform theoretical analysis to derive the scaling relationships. Finally we apply these scaling relationships to wireless sensor network with coupled failures in more realistic situation as case study. From the simulation results of smaller-scale networks, we can infer the lifetime distribution of large-scale networks based on universal scaling function. The computation time and accuracy are compared with standard Monte Carlo simulation which shows that our method is faster and accurate. Our research shows that the proposed method using universal scaling functions can help us to infer the lifetime properties of large-scale networks with low computational cost. Our method can help fast reliability evaluations of large-scale complex networks with high accuracy.

Gestational Diabetes Mellitus (GDM), a serious complication during pregnancy which is defined by abnormal glucose regulation, is commonly treated by diabetic diet and lifestyle changes. While recent findings place the microbiome as a natural mediator between diet interventions and diverse disease states, its role in GDM is still unknown. Here, based on observation data from healthy pregnant control group and GDM patients, we developed a new network approach using patterns of co-abundance of microorganism to construct microbial networks that represent human-specific information about gut microbiota in different groups. By calculating network similarity in different groups, we analyze the gut microbiome from 27 GDM subjects collected before and after two weeks of diet therapy compared with 30 control subjects to identify the health condition of microbial community balance in GDM subjects. Although the microbial communities remain similar after the diet phase, we find that the structure of their inter-species co-abundance network is significantly altered, which is reflected in that the ecological balance of GDM patients was not "healthier" after the diet intervention. In addition, we devised a method for individualized network analysis of the microbiome, thereby a pattern is found that GDM individuals whose microbial networks are with large deviations from the GDM group are usually accompanied by their abnormal glucose regulation. This approach may help the development of individualized diagnosis strategies and microbiome-based therapies in the future.

While a number of network systems are running under certain stress with a limited lifetime, it is still unknown how to predict the lifetime–stress relation of complex systems. We develop a percolation-based approach to build an operational lifetime–stress model for complex networks, which captures the spatial and temporal reliability characteristics of the system. In this article, the general analytical expression for the entire operational lifetime–stress relation has been presented, which suggests that the load stress and the number of nodes in the network impact the operational lifetime in the same manner. The size effect found here in the lifetime–stress relation is observed for the first time, to our best knowledge. For 2-D lattices, we show that the lifetime–stress relation can be regarded as the combination of two parts—an approximately linear region for small stress, and nonlinear region for large stress. We also analyze the lifetime–stress function of Beijing road network and the western United States power grid. Our article might help to develop acceleration testing methods, which will facilitate better design of reliable complex systems.

Operating systems are critical infrastructures for the information systems. Malfunction of certain function component can induce unexpected risks and countless damage for the computing service based on the operating systems. While it is critical for understanding the failure mechanism of operating system, it remains unclear how the function components interact with each other. Here we study these interactions in the kernel of Android OS by modeling the operating system as a complex network. In this network, each node represents a function and links are various call relationship between them. With community analysis, we find three different relations between the topological statistics and the community size. To reveal the organization vulnerability in different scale, we also perform the percolation analysis and identify the critical structures of this software networks. Our findings may help to understand the system complexity and design corresponding software testing methods.