arXiv subjects

Xiu-Xiu Zhan

Publications and source records attributed to Xiu-Xiu Zhan.

At least 19 recordsLinked to original sources

arXiv2026

Multiscale Reconstruction of Multiplex Networks with Higher-Order Interactions

Inferring network structure from dynamical observations is a fundamental inverse problem in complex systems. Existing approaches have largely focused on single-layer or pairwise interaction networks, leaving the reconstructability of multiplex systems with higher-order interactions poorly understood. Here, we show that structural identifiability in such systems is jointly governed by cross-layer spreading couplings and network heterogeneity. Building on coupled awareness-behavior dynamics, we develop a multiscale inference framework that disentangles interacting spreading processes and enables the simultaneous reconstruction of microscopic simplicial interactions and macroscopic metapopulation organization. Numerical experiments on synthetic and empirical networks demonstrate high reconstruction performance, particularly in non-absorbing dynamical regimes sustained by weak inhibitory and strong facilitatory couplings. Results reveal the mechanisms governing structural identifiability in higher-order multiplex networks and provide a general route for inverse inference in multiscale complex systems.

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arXiv2025

Entropy-Driven Sensor Deployment and Source Detection in Hypergraphs

Identifying the diffusion source in complex networks is critical for understanding and controlling epidemic spread. In realistic settings, full observation of node states is rarely available, making sensor-based source detection a practical alternative. However, existing sensor-based methods are often confined to simple networks, failing to capture the higher-order group dynamics of real-world spreading process. By deploying a limited number of sensors to monitor the diffusion process, one can infer the origin from partial observations. Yet, determining optimal sensor placement is challenging, i.e., poor deployment leads to redundant or noisy data, while optimal placement must balance coverage diversity and information value under limited resources. To address these challenges, we propose a dedicated framework termed Sensor-based Source Detection in Hypergraphs (SSDH). Specifically, we introduce a novel entropy-driven sensor deployment strategy that effectively captures critical early-stage diffusion signals by maximizing information gain under limited resources. Furthermore, we develop a source localization algorithm that quantifies propagation uncertainty through a newly defined path uncertainty-based score. By integrating this score with topological distance, SSDH enables accurate and robust source identification. Extensive experiments on both synthetic and empirical hypergraphs demonstrate that SSDH consistently outperforms competing algorithms by 5%--30% across different sensor ratios, final spreading ratios, and infection probabilities. These results validate the effectiveness of SSDH and highlight its superior capability to tackle source localization in complex systems characterized by higher-order interactions.

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arXiv2025

HIP: Model-Agnostic Hypergraph Influence Prediction via Distance-Centrality Fusion and Neural ODEs

Predicting user influence in social networks is a critical problem, and hypergraphs, as a prevalent higher-order modeling approach, provide new perspectives for this task. However, the absence of explicit cascade or infection probability data makes it particularly challenging to infer influence in hypergraphs. To address this, we introduce HIP, a unified and model-independent framework for influence prediction without knowing the underlying spreading model. HIP fuses multi-dimensional centrality indicators with a temporally reinterpreted distance matrix to effectively represent node-level diffusion capacity in the absence of observable spreading. These representations are further processed through a multi-hop Hypergraph Neural Network (HNN) to capture complex higher-order structural dependencies, while temporal correlations are modeled using a hybrid module that combines Long Short-Term Memory (LSTM) networks and Neural Ordinary Differential Equations (Neural ODEs). Notably, HIP is inherently modular: substituting the standard HGNN with the advanced DPHGNN, and the LSTM with xLSTM, yields similarly strong performance, showcasing its architectural generality and robustness. Empirical evaluations across 14 real-world hypergraph datasets demonstrate that HIP consistently surpasses existing baselines in prediction accuracy, resilience, and identification of top influencers, all without relying on any diffusion trajectories or prior knowledge of the spreading model. These findings underline HIP's effectiveness and adaptability as a general-purpose solution for influence prediction in complex hypergraph environments.

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arXiv2025

Structural-Aware Key Node Identification in Hypergraphs via Representation Learning and Fine-Tuning

Evaluating node importance is a critical aspect of analyzing complex systems, with broad applications in digital marketing, rumor suppression, and disease control. However, existing methods typically rely on conventional network structures and fail to capture the polyadic interactions intrinsic to many real-world systems. To address this limitation, we study key node identification in hypergraphs, where higher-order interactions are naturally modeled as hyperedges. We propose a novel framework, AHGA, which integrates an Autoencoder for extracting higher-order structural features, a HyperGraph neural network-based pre-training module (HGNN), and an Active learning-based fine-tuning process. This fine-tuning step plays a vital role in mitigating the gap between synthetic and real-world data, thereby enhancing the model's robustness and generalization across diverse hypergraph topologies. Extensive experiments on eight empirical hypergraphs show that AHGA outperforms classical centrality-based baselines by approximately 37.4%. Furthermore, the nodes identified by AHGA exhibit both high influence and strong structural disruption capability, demonstrating their superiority in detecting multifunctional nodes.

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arXiv2025

Source Detection in Hypergraph Epidemic Dynamics using a Higher-Order Dynamic Message Passing Algorithm

Source detection is crucial for capturing the dynamics of real-world infectious diseases and informing effective containment strategies. Most existing approaches to source detection focus on conventional pairwise networks, whereas recent efforts on both mathematical modeling and analysis of contact data suggest that higher-order (e.g., group) interactions among individuals may both account for a large fraction of infection events and change our understanding of how epidemic spreading proceeds in empirical populations. In the present study, we propose a message-passing algorithm, called the HDMPN, for source detection for a stochastic susceptible-infectious dynamics on hypergraphs. By modulating the likelihood maximization method by the fraction of infectious neighbors, HDMPN aims to capture the influence of higher-order structures and do better than the conventional likelihood maximization. We numerically show that, in most cases, HDMPN outperforms benchmarks including the likelihood maximization method without modification.

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arXiv2025

Modeling Coupled Epidemic-Information Dynamics via Reaction-Diffusion Processes on Multiplex Networks with Media and Mobility Effects

While most existing epidemic models focus on the influence of isolated factors, infectious disease transmission is inherently shaped by the complex interplay of multiple interacting elements. To better capture real-world dynamics, it is essential to develop epidemic models that incorporate diverse, realistic factors. In this study, we propose a coupled disease-information spreading model on multiplex networks that simultaneously accounts for three critical dimensions: media influence, higher-order interactions, and population mobility. This integrated framework enables a systematic analysis of synergistic spreading mechanisms under practical constraints and facilitates the exploration of effective epidemic containment strategies. We employ a microscopic Markov chain approach (MMCA) to derive the coupled dynamical equations and identify epidemic thresholds, which are then validated through extensive Monte Carlo (MC) simulations. Our results show that both mass media dissemination and higher-order network structures contribute to suppressing disease transmission by enhancing public awareness. However, the containment effect of higher-order interactions weakens as the order of simplices increases. We also explore the influence of subpopulation characteristics, revealing that increasing inter-subpopulation connectivity in a connected metapopulation network leads to lower disease prevalence. Furthermore, guiding individuals to migrate toward less accessible or more isolated subpopulations is shown to effectively mitigate epidemic spread. These findings offer valuable insights for designing targeted and adaptive intervention strategies in complex epidemic settings.

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arXiv2024

BCIM: Budget and capacity constrained influence maximization in multilayer networks

Influence maximization (IM) seeks to identify a seed set that maximizes influence within a network, with applications in areas such as viral marketing, disease control, and political campaigns. The budgeted influence maximization (BIM) problem extends IM by incorporating cost constraints for different nodes. However, the current BIM problem, limited by budget alone, often results in the selection of numerous low-cost nodes, which may not be applicable to real-world scenarios. Moreover, considering that users can transmit information across multiple social platforms, solving the BIM problem across these platforms could lead to more optimized resource utilization. To address these challenges, we propose the Budget and Capacity Constrained Influence Maximization (BCIM) problem within multilayer networks and introduce a Multilayer Multi-population Genetic Algorithm (MMGA) to solve it. The MMGA employs modules, such as initialization, repair, and parallel evolution, designed not only to meet budget and capacity constraints but also to significantly enhance algorithmic efficiency. Extensive experiments on both synthetic and empirical multilayer networks demonstrate that MMGA improves spreading performance by at least 10% under the two constraints compared to baselines extended from classical IM problems. The BCIM framework introduces a novel direction in influence maximization, providing an effective and efficient solution to the problem.

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arXiv2024

ESND: An Embedding-based Framework for Signed Network Dismantling

Network dismantling aims to maximize the disintegration of a network by removing a specific set of nodes or edges and is applied to various tasks in diverse domains, such as cracking down on crime organizations, delaying the propagation of rumors, and blocking the transmission of viruses. Most of the current network dismantling methods are tailored for unsigned networks, which only consider the connection between nodes without evaluating the nature of the relationships, such as friendship/hostility, enhancing/repressing, and trust/distrust. We here propose an embedding-based algorithm, namely ESND, to solve the signed network dismantling problem. The algorithm generally iterates the following four steps, i.e., giant component detection, network embedding, node clustering, and removal node selection. To illustrate the efficacy and stability of ESND, we conduct extensive experiments on six signed network datasets as well as null models, and compare the performance of our method with baselines. Experimental results consistently show that the proposed ESND is superior to the baselines and displays stable performance with the change in the network structure. Additionally, we examine the impact of sign proportions on network robustness via ESND, observing that networks with a high ratio of negative edges are generally easier to dismantle than networks with high positive edges.

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arXiv2024

Locating influential nodes in hypergraphs via fuzzy collective influence

Complex contagion phenomena, such as the spread of information or contagious diseases, often occur among the population due to higher-order interactions between individuals. Individuals who can be represented by nodes in a network may play different roles in the spreading process, and thus finding the most influential nodes in a network has become a crucial topic in network science for applications such as viral marketing, rumor suppression, and disease control. To solve the problem of identifying nodes that have high influence in a complex system, we propose a higher-order distance-based fuzzy centrality methods (HDF and EHDF) that are customized for a hypergraph which can characterize higher-order interactions between nodes via hyperedges. The methods we proposed assume that the influence of a node is reliant on the neighboring nodes with a certain higher-order distance. We compare the proposed methods with the baseline centrality methods to verify their effectiveness. Experimental results on six empirical hypergraphs show that the proposed methods could better identify influential nodes, especially showing plausible performance in finding the top influential nodes. Our proposed theoretical framework for identifying influential nodes could provide insights into how higher-order topological structure can be used for tasks such as vital node identification, influence maximization, and network dismantling.

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arXiv2024

Directed network comparison using motifs

Analyzing and characterizing the differences between networks is a fundamental and challenging problem in network science. Previously, most network comparison methods that rely on topological properties have been restricted to measuring differences between two undirected networks. However, many networks, such as biological networks, social networks, and transportation networks, exhibit inherent directionality and higher-order attributes that should not be ignored when comparing networks. Therefore, we propose a motif-based directed network comparison method that captures local, global, and higher-order differences between two directed networks. Specifically, we first construct a motif distribution vector for each node, which captures the information of a node's involvement in different directed motifs. Then, the dissimilarity between two directed networks is defined on the basis of a matrix which is composed of the motif distribution vector of every node and Jensen-Shannon divergence. The performance of our method is evaluated via the comparison of six real directed networks with their null models as well as their perturbed networks based on edge perturbation. Our method is superior to the state-of-the-art baselines and is robust with different parameter settings.

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arXiv2023

Influence maximization in multilayer networks based on adaptive coupling degree

Influence Maximization(IM) aims to identify highly influential nodes to maximize influence spread in a network. Previous research on the IM problem has mainly concentrated on single-layer networks, disregarding the comprehension of the coupling structure that is inherent in multilayer networks. To solve the IM problem in multilayer networks, we first propose an independent cascade model (MIC) in a multilayer network where propagation occurs simultaneously across different layers. Consequently, a heuristic algorithm, i.e., Adaptive Coupling Degree (ACD), which selects seed nodes with high spread influence and a low degree of overlap of influence, is proposed to identify seed nodes for IM in a multilayer network. By conducting experiments based on MIC, we have demonstrated that our proposed method is superior to the baselines in terms of influence spread and time cost in 6 synthetic and 4 real-world multilayer networks.

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arXiv2023

A universal meta-heuristic framework for influence maximization in hypergraphs

Influence maximization (IM) aims to select a small number of nodes that are able to maximize their influence in a network and covers a wide range of applications. Despite numerous attempts to provide effective solutions in ordinary networks, higher-order interactions between entities in various real-world systems are not usually taken into account. In this paper, we propose a versatile meta-heuristic approach, hyper genetic algorithm (HGA), to tackle the IM problem in hypergraphs, which is based on the concept of genetic evolution. Systematic validations in synthetic and empirical hypergraphs under both simple and complex contagion models indicate that HGA achieves universal and plausible performance compared to baseline methods. We explore the cause of the excellent performance of HGA through ablation studies and correlation analysis. The findings show that the solution of HGA is distinct from that of other prior methods. Moreover, a closer look at the local topological features of the seed nodes acquired by different algorithms reveals that the selection of seed nodes cannot be based on a single topological characteristic, but should involve a combination of multiple topological features to address the IM problem.

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arXiv2023

A hyper-distance-based method for hypernetwork comparison

Hypernetwork is a useful way to depict multiple connections between nodes, making it an ideal tool for representing complex relationships in network science. In recent years, there has been a marked increase in studies on hypernetworks, however, the comparison of the difference between two hypernetworks has been given less attention. This paper proposes a hyper-distance-based method (HD) for comparing hypernetworks. This method takes into account high-order information, such as the high-order distance between nodes. The experiments carried out on synthetic hypernetworks have shown that HD is capable of distinguishing between hypernetworks generated with different parameters, and it is successful in the classification of hypernetworks. Furthermore, HD outperforms current state-of-the-art baselines to distinguish empirical hypernetworks when hyperedges are disrupted.

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arXiv2022

Vital node identification in hypergraphs via gravity model

Hypergraphs that can depict interactions beyond pairwise edges have emerged as an appropriate representation for modeling polyadic relations in complex systems. With the recent surge of interest in researching hypergraphs, the centrality problem has attracted abundant attention due to the challenge of how to utilize the higher-order structure for the definition of centrality metrics. In this paper, we propose a new centrality method (HGC) on the basis of the gravity model as well as a semi-local HGC (LHGC) which can achieve a balance between accuracy and computational complexity. Meanwhile, two comprehensive evaluation metrics, i.e., a complex contagion model in hypergraphs that mimics the group influence during the spreading process and network s-efficiency based on the higher-order distance between nodes, are first proposed to evaluate the effectiveness of our methods. The results show that our methods can filter out nodes that have fast spreading ability and are vital in terms of hypergraph connectivity.

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arXiv2022

Influence Maximization in Hypergraphs

Influence maximization in complex networks, i.e., maximizing the size of influenced nodes via selecting K seed nodes for a given spreading process, has attracted great attention in recent years. However, the influence maximization problem in hypergraphs, in which the hyperedges are leveraged to represent the interactions among more than two nodes, is still an open question. In this paper, we propose an adaptive degree-based heuristic algorithm, i.e., Heuristic Degree Discount (HDD), which iteratively selects nodes with low influence overlap as seeds, to solve the influence maximization problem in hypergraphs. We further extend algorithms from ordinary networks as baselines and compare the performance of the proposed algorithm and baselines on both real data and synthetic hypergraphs. Results show that HDD outperforms the baselines in terms of both effectiveness and efficiency. Moreover, the experiments on synthetic hypergraphs indicate that HDD shows high performance, especially in hypergraphs with heterogeneous degree distribution.

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arXiv2021

Quantification of network structural dissimilarities based on graph embedding

Identifying and quantifying structural dissimilarities between complex networks is a fundamental and challenging problem in network science. Previous network comparison methods are based on the structural features, such as the length of shortest path, degree and graphlet, which may only contain part of the topological information. Therefore, we propose an efficient network comparison method based on network embedding, i.e., \textit{DeepWalk}, which considers the global structural information. In detail, we calculate the distance between nodes through the vector extracted by \textit{DeepWalk} and quantify the network dissimilarity by spectral entropy based Jensen-Shannon divergences of the distribution of the node distances. Experiments on both synthetic and empirical data show that our method outperforms the baseline methods and can distinguish networks perfectly by only using the global embedding based distance distribution. In addition, we show that our method can capture network properties, e.g., average shortest path length and link density. Moreover, the experiments of modularity further implies the functionality of our method.

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arXiv2021

Measuring and utilizing temporal network dissimilarity

Quantifying the structural and functional differences of temporal networks is a fundamental and challenging problem in the era of big data. This work proposes a temporal dissimilarity measure for temporal network comparison based on the fastest arrival distance distribution and spectral entropy based Jensen-Shannon divergence. Experimental results on both synthetic and empirical temporal networks show that the proposed measure could discriminate diverse temporal networks with different structures by capturing various topological and temporal properties. Moreover, the proposed measure can discern the functional distinctions and is found effective applications in temporal network classification and spreadability discrimination.

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arXiv2021

The Aging Effect in Evolving Scientific Citation Networks

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.

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