arXiv subjects

Zhongyuan Ruan

Publications and source records attributed to Zhongyuan Ruan.

At least 19 recordsLinked to original sources

arXiv2025

An effective method for profiling core-periphery structures in complex networks

Profiling core-periphery structures in networks has attracted significant attention, leading to the development of various methods. Among these, the rich-core method is distinguished for being entirely parameter-free and scalable to large networks. However, the cores it identifies are not always structurally cohesive, as they may lack high link density. Here, we propose an improved method building upon the rich-core framework. Instead of relying on node degree, our approach incorporates both the node's coreness $k$ and its centrality within the $k$-core. We apply the approach to twelve real-world networks, and find that the cores identified are generally denser compared to those derived from the rich-core method. Additionally, we demonstrate that the proposed method provides a natural way for identifying an exceptionally dense core, i.e., a clique, which often approximates or even matches the maximum clique in many real-world networks. Furthermore, we extend the method to multiplex networks, and show its effectiveness in identifying dense multiplex cores across several well-studied datasets. Our study may offer valuable insights into exploring the meso-scale properties of complex networks.

physics.soc-ph↗

arXiv2024

Social contagion under hybrid interactions

Threshold-driven models and game theory are two fundamental paradigms for describing human interactions in social systems. However, in mimicking social contagion processes, models that simultaneously incorporate these two mechanisms have been largely overlooked. Here, we study a general model that integrates hybrid interaction forms by assuming that a part of nodes in a network are driven by the threshold mechanism, while the remaining nodes exhibit imitation behavior governed by their rationality (under the game-theoretic framework). Our results reveal that the spreading dynamics are determined by the payoff of adoption. For positive payoffs, increasing the density of highly rational nodes can promote the adoption process, accompanied by a double phase transition. The degree of rationality can regulate the spreading speed, with less rational imitators slowing down the spread. We further find that the results are opposite for negative payoffs of adoption. This model may provide valuable insights into understanding the complex dynamics of social contagion phenomena in real-world social networks.

physics.soc-ph↗

arXiv2024

Exploring agent interaction patterns in the comment sections of fake and real news

User comments on social media have been recognized as a crucial factor in distinguishing between fake and real news, with many studies focusing on the textual content of user reactions. However, the interactions among agents in the comment sections for fake and real news have not been fully explored. In this study, we analyze a dataset comprising both fake and real news from Reddit to investigate agent interaction patterns, considering both the network structure and the sentiment of the nodes. Our findings reveal that (i) comments on fake news are more likely to form groups, (ii) compared to fake news, where users generate more negative sentiment, real news tend to elicit more neutral and positive sentiments. Additionally, nodes with similar sentiments cluster together more tightly than anticipated. From a dynamic perspective, we found that the sentiment distribution among nodes stabilizes early and remains stable over time. These findings have both theoretical and practical implications, particularly for the early detection of real and fake news within social networks.

cs.SI↗

arXiv2024

Epidemic spreading under game-based self-quarantine behaviors: The different effects of local and global information

During the outbreak of an epidemic, individuals may modify their behaviors in response to external (including local and global) infection-related information. However, the difference between local and global information in influencing the spread of diseases remains inadequately explored. Here we study a simple epidemic model that incorporates the game-based self-quarantine behavior of individuals, taking into account the influence of local infection status, global disease prevalence and node heterogeneity (non-identical degree distribution). Our findings reveal that local information can effectively contain an epidemic, even with only a small proportion of individuals opting for self-quarantine. On the other hand, global information can cause infection evolution curves shaking during the declining phase of an epidemic, owing to the synchronous release of nodes with the same degree from the quarantined state. In contrast, the releasing pattern under the local information appears to be more random. This shaking phenomenon can be observed in various types of networks associated with different characteristics. Moreover, it is found that under the proposed game-epidemic framework, a disease is more difficult to spread in heterogeneous networks than in homogeneous networks, which differs from conventional epidemic models.

physics.soc-ph↗

arXiv2024

A simple model of global cascades on random hypergraphs

This study introduces a comprehensive framework that situates information cascades within the domain of higher-order interactions, utilizing a double-threshold hypergraph model. We propose that individuals (nodes) gain awareness of information through each communication channel (hyperedge) once the number of information adopters surpasses a threshold $ϕ_m$. However, actual adoption of the information only occurs when the cumulative influence across all communication channels exceeds a second threshold, $ϕ_k$. We analytically derive the cascade condition for both the case of a single seed node using percolation methods and the case of any seed size employing mean-field approximation. Our findings underscore that when considering the fractional seed size, $r_0 \in (0,1]$, the connectivity pattern of the random hypergraph, characterized by the hyperdegree, $k$, and cardinality, $m$, distributions, exerts an asymmetric impact on the global cascade boundary. This asymmetry manifests in the observed differences in the boundaries of the global cascade within the $(ϕ_m, \langle m \rangle)$ and $(ϕ_k, \langle k \rangle)$ planes. However, as $r_0 \to 0$, this asymmetric effect gradually diminishes. Overall, by elucidating the mechanisms driving information cascades within a broader context of higher-order interactions, our research contributes to theoretical advancements in complex systems theory.

physics.soc-ph↗

arXiv2024

Hub-collision avoidance and leaf-node options algorithm for fractal dimension and renormalization of complex networks

The box-covering method plays a fundamental role in the fractal property recognition and renormalization analysis of complex networks. This study proposes the hub-collision avoidance and leaf-node options (HALO) algorithm. In the box sampling process, a forward sampling rule (for avoiding hub collisions) and a reverse sampling rule (for preferentially selecting leaf nodes) are determined for bidirectional network traversal to reduce the randomness of sampling. In the box selection process, the larger necessary boxes are preferentially selected to join the solution by continuously removing small boxes. The compact-box-burning (CBB) algorithm, the maximum-excluded-mass-burning (MEMB) algorithm, the overlapping-box-covering (OBCA) algorithm, and the algorithm for combining small-box-removal strategy and maximum box sampling with a sampling density of 30 (SM30) are compared with HALO in experiments. Results on nine real networks show that HALO achieves the highest performance score and obtains 11.40%, 7.67%, 2.18%, and 8.19% fewer boxes than the compared algorithms, respectively. The algorithm determinism is significantly improved. The fractal dimensions estimated by covering four standard networks are more accurate. Moreover, different from MEMB or OBCA, HALO is not affected by the tightness of the hubs and exhibits a stable performance in different networks. Finally, the time complexities of HALO and the compared algorithms are all O(N^2), which is reasonable and acceptable.

physics.soc-ph↗

arXiv2023

MAD-MulW: A Multi-Window Anomaly Detection Framework for BGP Security Events

In recent years, various international security events have occurred frequently and interacted between real society and cyberspace. Traditional traffic monitoring mainly focuses on the local anomalous status of events due to a large amount of data. BGP-based event monitoring makes it possible to perform differential analysis of international events. For many existing traffic anomaly detection methods, we have observed that the window-based noise reduction strategy effectively improves the success rate of time series anomaly detection. Motivated by this observation, we propose an unsupervised anomaly detection model, MAD-MulW, which incorporates a multi-window serial framework. Firstly, we design the W-GAT module to adaptively update the sample weights within the window and retain the updated information of the trailing sample, which not only reduces the outlier samples' noise but also avoids the space consumption of data scale expansion. Then, the W-LAT module based on predictive reconstruction both captures the trend of sample fluctuations over a certain period of time and increases the interclass variation through the reconstruction of the predictive sample. Our model has been experimentally validated on multiple BGP anomalous events with an average F1 score of over 90\%, which demonstrates the significant improvement effect of the stage windows and adaptive strategy on the efficiency and stability of the timing model.

cs.CR↗

arXiv2022

Epidemic spreading under mutually independent intra- and inter-host pathogen evolution

The dynamics of epidemic spreading is often reduced to the single control parameter $R_0$, whose value, above or below unity, determines the state of the contagion. If, however, the pathogen evolves as it spreads, $R_0$ may change over time, potentially leading to a mutation-driven spread, in which an initially sub-pandemic pathogen undergoes a breakthrough mutation. To predict the boundaries of this pandemic phase, we introduce here a modeling framework to couple the network spreading patterns with the intra-host evolutionary dynamics. For many pathogens these two processes, intra- and inter-host, are driven by different selection forces. And yet here we show that even in the extreme case when these two forces are mutually independent, mutations can still fundamentally alter the pandemic phase-diagram, whose transitions are now shaped, not just by $R_0$, but also by the balance between the epidemic and the evolutionary timescales. If mutations are too slow, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, we identify a broad range of conditions in which an initially sub-pandemic pathogen can break through to gain widespread prevalence.

q-bio.PE↗

arXiv2022

How the reversible change of contact network affects the epidemic spreading

The mobility patterns of individuals in China during the early outbreak of the COVID-19 pandemic exhibit reversible changes -- in many regions, the mobility first decreased significantly and later restored. Based on this observation, here we study the classical SIR model on a particular type of time-varying network where the links undergo a freeze-recovery process. We first focus on an isolated network and find that the recovery mechanism could lead to the resurgence of an epidemic. The influence of link freezing on epidemic dynamics is subtle. In particular, we show that there is an optimal value of the freezing rate for links which corresponds to the lowest prevalence of the epidemic. This result challenges our conventional idea that stricter prevention measures (corresponding to a larger freezing rate) could always have a better inhibitory effect on epidemic spreading. We further investigate an open system where a small fraction of nodes in the network may acquire the disease from the "environment" (the outside infected nodes). In this case, the second wave would appear even if the number of infected nodes has declined to zero, which can not be explained by the isolated network model.

physics.soc-ph↗

arXiv2021

A Multi-View Framework for BGP Anomaly Detection via Graph Attention Network

As the default protocol for exchanging routing reachability information on the Internet, the abnormal behavior in traffic of Border Gateway Protocols (BGP) is closely related to Internet anomaly events. The BGP anomalous detection model ensures stable routing services on the Internet through its real-time monitoring and alerting capabilities. Previous studies either focused on the feature selection problem or the memory characteristic in data, while ignoring the relationship between features and the precise time correlation in feature (whether it's long or short term dependence). In this paper, we propose a multi-view model for capturing anomalous behaviors from BGP update traffic, in which Seasonal and Trend decomposition using Loess (STL) method is used to reduce the noise in the original time-series data, and Graph Attention Network (GAT) is used to discover feature relationships and time correlations in feature, respectively. Our results outperform the state-of-the-art methods at the anomaly detection task, with the average F1 score up to 96.3% and 93.2% on the balanced and imbalanced datasets respectively. Meanwhile, our model can be extended to classify multiple anomalous and to detect unknown events.

cs.LG↗

arXiv2021

Inferring Multiple Relationships between ASes using Graph Convolutional Network

Precisely understanding the business relationships between Autonomous Systems (ASes) is essential for studying the Internet structure. So far, many inference algorithms have been proposed to classify the AS relationships, which mainly focus on Peer-Peer (P2P) and Provider-Customer (P2C) binary classification and achieved excellent results. However, there are other types of AS relationships in actual scenarios, i.e., the businessbased sibling and structure-based exchange relationships, that were neglected in the previous research. These relationships are usually difficult to be inferred by existing algorithms because there is no discrimination on the designed features compared to the P2P or P2C relationships. In this paper, we focus on the multi-classification of AS relationships for the first time. We first summarize the differences between AS relationships under the structural and attribute features, and the reasons why multiple relationships are difficult to be inferred. We then introduce new features and propose a Graph Convolutional Network (GCN) framework, AS-GCN, to solve this multi-classification problem under complex scene. The framework takes into account the global network structure and local link features concurrently. The experiments on real Internet topological data validate the effectiveness of our method, i.e., AS-GCN achieves comparable results on the easy binary classification task, and outperforms a series of baselines on the more difficult multi-classification task, with the overall accuracy above 95%.

cs.NI↗

arXiv2021

Sampling Subgraph Network with Application to Graph Classification

Graphs are naturally used to describe the structures of various real-world systems in biology, society, computer science etc., where subgraphs or motifs as basic blocks play an important role in function expression and information processing. However, existing research focuses on the basic statistics of certain motifs, largely ignoring the connection patterns among them. Recently, a subgraph network (SGN) model is proposed to study the potential structure among motifs, and it was found that the integration of SGN can enhance a series of graph classification methods. However, SGN model lacks diversity and is of quite high time complexity, making it difficult to widely apply in practice. In this paper, we introduce sampling strategies into SGN, and design a novel sampling subgraph network model, which is scale-controllable and of higher diversity. We also present a hierarchical feature fusion framework to integrate the structural features of diverse sampling SGNs, so as to improve the performance of graph classification. Extensive experiments demonstrate that, by comparing with the SGN model, our new model indeed has much lower time complexity (reduced by two orders of magnitude) and can better enhance a series of graph classification methods (doubling the performance enhancement).

cs.SI↗

arXiv2020

Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission

Motivated by the importance of individual differences in risk perception and behavior change in people's responses to infectious disease outbreaks (particularly the ongoing COVID-19 pandemic), we propose a heterogeneous Disease-Behavior-Information (hDBI) transmission model, in which people's risk of getting infected is influenced by information diffusion, behavior change, and disease transmission. We use both a mean-field approximation and Monte Carlo simulations to analyze the dynamics of the model. Information diffusion influences behavior change by allowing people to be aware of the disease and adopt self-protection, and subsequently affects disease transmission by changing the actual infection rate. Results show that (a) awareness plays a central role in epidemic prevention; (b) a reasonable fraction of "over-reacting" nodes are needed in epidemic prevention; (c) R0 has different effects on epidemic outbreak for cases with and without asymptomatic infection; (d) social influence on behavior change can remarkably decrease the epidemic outbreak size. This research indicates that the media and opinion leaders should not understate the transmissibility and severity of diseases to ensure that people could become aware of the disease and adopt self-protection to protect themselves and the whole population.

physics.soc-ph↗

arXiv2020

Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria

Adversarial attacks have been alerting the artificial intelligence community recently, since many machine learning algorithms were found vulnerable to malicious attacks. This paper studies adversarial attacks to scale-free networks to test their robustness in terms of statistical measures. In addition to the well-known random link rewiring (RLR) attack, two heuristic attacks are formulated and simulated: degree-addition-based link rewiring (DALR) and degree-interval-based link rewiring (DILR). These three strategies are applied to attack a number of strong scale-free networks of various sizes generated from the Barabási-Albert model. It is found that both DALR and DILR are more effective than RLR, in the sense that rewiring a smaller number of links can succeed in the same attack. However, DILR is as concealed as RLR in the sense that they both are constructed by introducing a relatively small number of changes on several typical structural properties such as average shortest path-length, average clustering coefficient, and average diagonal distance. The results of this paper suggest that to classify a network to be scale-free has to be very careful from the viewpoint of adversarial attack effects.

cs.SI↗

arXiv2019

Subgraph Networks with Application to Structural Feature Space Expansion

Real-world networks exhibit prominent hierarchical and modular structures, with various subgraphs as building blocks. Most existing studies simply consider distinct subgraphs as motifs and use only their numbers to characterize the underlying network. Although such statistics can be used to describe a network model, or even to design some network algorithms, the role of subgraphs in such applications can be further explored so as to improve the results. In this paper, the concept of subgraph network (SGN) is introduced and then applied to network models, with algorithms designed for constructing the 1st-order and 2nd-order SGNs, which can be easily extended to build higher-order ones. Furthermore, these SGNs are used to expand the structural feature space of the underlying network, beneficial for network classification. Numerical experiments demonstrate that the network classification model based on the structural features of the original network together with the 1st-order and 2nd-order SGNs always performs the best as compared to the models based only on one or two of such networks. In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.

cs.SI↗

arXiv2018

A Self-Learning Information Diffusion Model for Smart Social Networks

In this big data era, more and more social activities are digitized thereby becoming traceable, and thus the studies of social networks attract increasing attention from academia. It is widely believed that social networks play important role in the process of information diffusion. However, the opposite question, i.e., how does information diffusion process rebuild social networks, has been largely ignored. In this paper, we propose a new framework for understanding this reversing effect. Specifically, we first introduce a novel information diffusion model on social networks, by considering two types of individuals, i.e., smart and normal individuals, and two kinds of messages, true and false messages. Since social networks consist of human individuals, who have self-learning ability, in such a way that the trust of an individual to one of its neighbors increases (or decreases) if this individual received a true (or false) message from that neighbor. Based on such a simple self-learning mechanism, we prove that a social network can indeed become smarter, in terms of better distinguishing the true message from the false one. Moreover, we observe the emergence of social stratification based on the new model, i.e., the true messages initially posted by an individual closer to the smart one can be forwarded by more others, which is enhanced by the self-learning mechanism. We also find the crossover advantage, i.e., interconnection between two chain networks can make the related individuals possessing higher social influences, i.e., their messages can be forwarded by relatively more others. We obtained these results theoretically and validated them by simulations, which help better understand the reciprocity between social networks and information diffusion.

cs.SI↗

arXiv2017

Service adoption spreading in online social networks

The collective behaviour of people adopting an innovation, product or online service is commonly interpreted as a spreading phenomenon throughout the fabric of society. This process is arguably driven by social influence, social learning and by external effects like media. Observations of such processes date back to the seminal studies by Rogers and Bass, and their mathematical modelling has taken two directions: One paradigm, called simple contagion, identifies adoption spreading with an epidemic process. The other one, named complex contagion, is concerned with behavioural thresholds and successfully explains the emergence of large cascades of adoption resulting in a rapid spreading often seen in empirical data. The observation of real world adoption processes has become easier lately due to the availability of large digital social network and behavioural datasets. This has allowed simultaneous study of network structures and dynamics of online service adoption, shedding light on the mechanisms and external effects that influence the temporal evolution of behavioural or innovation adoption. These advancements have induced the development of more realistic models of social spreading phenomena, which in turn have provided remarkably good predictions of various empirical adoption processes. In this chapter we review recent data-driven studies addressing real-world service adoption processes. Our studies provide the first detailed empirical evidence of a heterogeneous threshold distribution in adoption. We also describe the modelling of such phenomena with formal methods and data-driven simulations. Our objective is to understand the effects of identified social mechanisms on service adoption spreading, and to provide potential new directions and open questions for future research.

physics.soc-ph↗

arXiv2017

An efficient approach to suppress the negative role of contrarian oscillators in synchronization

It has been found that contrarian oscillators usually take a negative role in the collective behaviors formed by conformist oscillators. However, experiments revealed that it is also possible to achieve a strong coherence even when there are contrarians in the system such as neuron networks with both excitable and inhibitory neurons. To understand the underlying mechanism of this abnormal phenomenon, we here consider a complex network of coupled Kuramoto oscillators with mixed positive and negative couplings and present an efficient approach, i.e. tit-for-tat strategy, to suppress the negative role of contrarian oscillators in synchronization and thus increase the order parameter of synchronization. Two classes of contrarian oscillators are numerically studied and a brief theoretical analysis is provided to explain the numerical results.

physics.soc-ph↗