The advent of online social networks has facilitated fast and wide spread of information. However, some users, especially members of minority groups, may be less likely to receive information spreading on the network, due to their disadvantaged network position. We study the optimization problem of adding new connections to a network to enhance fairness in information access among different demographic groups. We provide a concrete formulation of this problem where information access is measured in terms of resistance distance, {offering a new perspective that emphasizes global network structure and multi-path connectivity.} The problem is shown to be NP-hard. We propose a simple greedy algorithm which turns out to output accurate solutions, but its run time is cubic, which makes it undesirable for large networks. As our main technical contribution, we reduce its time complexity to linear, leveraging several novel approximation techniques. In addition to our theoretical findings, we also conduct an extensive set of experiments using both real-world and synthetic datasets. We demonstrate that our linear-time algorithm can produce accurate solutions for networks with millions of nodes.

Random walk centrality is a fundamental metric in graph mining for quantifying node importance and influence, defined as the weighted average of hitting times to a node from all other nodes. Despite its ability to capture rich graph structural information and its wide range of applications, computing this measure for large networks remains impractical due to the computational demands of existing methods. In this paper, we present a novel formulation of random walk centrality, underpinning two scalable algorithms: one leveraging approximate Cholesky factorization and sparse inverse estimation, while the other sampling rooted spanning trees. Both algorithms operate in near-linear time and provide strong approximation guarantees. Extensive experiments on large real-world networks, including one with over 10 million nodes, demonstrate the efficiency and approximation quality of the proposed algorithms.

The passive intermodulation (PIM) interference in simultaneous transmit-receive system is difficult to deal with because the process of generating PIM signal is both nonlinear and with memory. To cancel such PIM interference digitally, this paper proposes the symmetrized basis function approximation network. Firstly, we recognize and justify one methodology underlying the current cascaded model, by which the two physically coupled causes, nonlinearity and memory, are mathematically decoupled. After decoupling, the nonlinear block in the system model only needs to be a static nonlinear mapping without memory. This recognition to the role of nonlinear block, along with the concept of basis transformation in linear space, leads to the idea of basis function approximation network (BFAN). Secondly, we point out that the PIM generating process is an odd mapping. Building this prior information into system modelling, we propose further the symmetrized BFAN, which greatly improves cancellation performance and reduces computational complexity. Lastly, we believe that the number of the nonlinear functions used in the system model should correspond to the number of the PIM sources. Building this belief into system modeling, we let different nonlinear units share the same weights so that they represent the same nonlinear function corresponding to the same one PIM source. This turns out to be tremendously effective in PIM interference cancellation for the experiment data. Additionally, the critical point phenomenon is explained with experiment and data analysis by virtue of our simple but rational way to deal with nonlinearity in PIM.

The control of the grid-forming (GFM) inverter is crucial, as it provides stable frequency and voltage support to the system, allowing for the integration of more renewable energy sources. This article aims to develop a novel primary control strategy that combines risk-sensitive control and residual compensation control, enhancing flexibility in strategy design to improve GFM inverter performance under disturbances, noise, and parameter uncertainties. The presented risk-sensitive residual generator-based compensation-control strategy includes two risk-sensitive factors$-$one for unbiased state estimation and residual generation, and another for improving closed-loop system performance. These two risk-sensitive factors increase the design freedom of the primary control strategy for the GFM inverter. First, this article establishes a link between risk-sensitive state estimation and residual generation, using the relationship between the risk-sensitive factor and$\tau$-divergence to design a risk-sensitive residual generator. Then, this article introduces a novel connection between risk-sensitive control and residual compensation control. By incorporating a risk-sensitive factor and its relationship with system stability margin, this article solves for the residual compensation controller with only two Riccati equations. Finally, experimental results on the StarSim platform demonstrate the effectiveness of the presented risk-sensitive control strategy for the GFM inverter in managing disturbances, noise, and parameter uncertainties.

Thebiharmonic distance (BD) is a fundamental metric that measures the distance of two nodes in a graph. It has found applications in network coherence, machine learning, and computational graphics, among others. In spite of BD's importance, efficient algorithms for the exact computation or approximation of this metric on large graphs remain notably absent. In this work, we provide several algorithms to estimate BD, building on a novel formulation of this metric. These algorithms enjoy locality property (that is, they only read a small portion of the input graph) and at the same time possess provable performance guarantees. In particular, our main algorithms approximate the BD between any node pair with an arbitrarily small additive error ε in time O(1/ε2 poly(log n/ε)). Furthermore, we perform an extensive empirical study on several benchmark networks, validating the performance and accuracy of our algorithms.

Abstract Maximizing influences in complex networks is a practically important but computationally challenging task for social network analysis, due to its nondeterministic polynomial time (NP)-hard nature. Most current approximation or heuristic methods either require tremendous human design efforts or achieve unsatisfying balances between effectiveness and efficiency. Recent machine learning attempts only focus on speed but lack performance enhancement. In this paper, different from previous attempts, we propose an effective deep reinforcement learning model that achieves superior performances over traditional best influence maximization algorithms. Specifically, we design an end-to-end learning framework that combines graph neural network as the encoder and reinforcement learning as the decoder, named DREIM. Through extensive training on small synthetic graphs, DREIM outperforms the state-of-the-art baseline methods on very large synthetic and real-world networks on solution quality, and we also empirically show its linear scalability with regard to the network size, which demonstrates its superiority in solving this problem.

Grid-forming (GFM) inverters with inductor–capacitor (LC) filters are used to interface distributed generation with the grid. However, the GFM inverter systems are prone to incipient multiplicative faults due to the inherent parameter drift phenomenon of theLCfilter. Motivated by this scenario, this article presents a primary active fault-tolerant control (PAFTC) strategy based on the robust-residual-observer-based-digital-twin model (RRODTM) to achieve the fault-tolerant control for the incipient multiplicative faults. The RRODTM is composed of a robust residual observer and a residual-driven compensation controller. Specially, the residual-driven compensation controller is designed by the data-driven method based on the multiplicative uncertainty caused by the incipient multiplicative faults. At the same time, this article constructs a data-driven-event-trigger mechanism based on stability margin to achieve the plug-and-play (PnP) function of the residual-driven compensation controller. Based on these, the PAFTC strategy improves the control performance online according to the degree of the incipient multiplicative fault. Comprehensive experiments show that compared to well-known control schemes in the literature, the PAFTC strategy improves the power distribution accuracy and transient response speed as well as enhances the robustness of the system.

Critical nodes in networks are extremely vulnerable to malicious attacks to trigger negative cascading events such as the spread of misinformation and diseases. Therefore, effective moderation of critical nodes is very vital for mitigating the potential damages caused by such malicious diffusions. The current moderation methods are computationally expensive. Furthermore, they disregard the fundamental metric of information centrality, which measures the dissemination power of nodes. We investigate the problem of removing$k$edges from a network to minimize the information centrality of a target node$v$while preserving the network's connectivity. We prove that this problem is computationally challenging: it is NP-complete and its objective function is not supermodular. However, we propose three approximation greedy algorithms using novel techniques such as random walk-based Schur complement approximation and fast sum estimation. One of our algorithms runs in nearly linear time in the number of edges. To complement our theoretical analysis, we conduct a comprehensive set of experiments on synthetic and real networks with over one million nodes. Across various settings, the experimental results illustrate the effectiveness and efficiency of our proposed algorithms.

This paper presents a primary control strategy that can recover control performance online for the voltage source inverter (VSI) with inductor-capacitor (LC) filter. The control strategy contains a droop-based feedforward controller, an LQR-based state feedback controller and a data-driven residual compensation controller (FFR). An event-triggered mechanism based on stability margin is used to implement the plug-and-play (PnP) function of the residual compensation controller. Simulations show that FFR can realize uncertainty control and disturbance suppression and improve the dynamic performance, robustness and power distribution ability of the VSI system.

Information of crop phenology is essential for evaluating crop productivity and crop management. Synthetic Aperture Radar (SAR), with the advantage of all-weather, day-night imaging and clouds penetrability, is an effective way for rice growth monitoring. In this study, we developed a method for remotely determining phenological stages of paddy rice with sixteen polarimetric SAR images. The method consists of three procedures: (I) Classified the transplanted rice (T-R) and the direct-sown rice (D-R) field; (II) Sensitivity analysis of polarimetric parameters versus rice phenology; (III) Reconstructing the time-series polarimetric parameters profiles of rice by time-frequency analysis; (IV) Specifying the phenological stages by detecting the maximum point, minimal point and inflection point from the smoothed polarimetric parameters time profile. Three keys phenological periods of rice were detected with the the accuracy of about 84%.