Real Network Research Articles

A Multi-Attribute Decision-Making Approach for Critical Node Identification in Complex Networks

Correctly identifying influential nodes in a complex network and implementing targeted protection measures can significantly enhance the overall security of the network. Currently, indicators such as degree centrality, closeness centrality, betweenness centrality, H-index, and K-shell are commonly used to measure node influence. Although these indicators can identify critical nodes to some extent, they often consider node attributes from a narrow perspective and have certain limitations. Therefore, evaluating the importance of nodes using most existing indicators remains incomplete. In this paper, we propose the multi-attribute CRITIC-TOPSIS network decision indicator, or MCTNDI, which integrates closeness centrality, betweenness centrality, H-index, and network constraint coefficients to identify critical nodes in a network. This indicator combines information from multiple perspectives, including local neighborhood importance, network topological location, path centrality, and node mutual information, thereby solving the issue of the one-sided perspective of single indicators and providing a more comprehensive measure of node importance. Additionally, MCTNDI is validated through the analysis of several real-world networks, including the Contiguous USA network, Dolphins network, USAir97 network, and Tech-routers-rf network. The validation is conducted from four aspects: the results of simulated network attacks, the distribution of node importance, the monotonicity of rankings, and the similarity of indicators, illustrating MCTNDI’s effectiveness in real networks.

Topology Bench: systematic graph-based benchmarking for core optical networks

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterized by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource, and represents a 61.5% increase in spatially referenced real-world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyze both real and synthetic networks using structural, spatial, and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalizability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups based on nine optimal graph metrics using K-means. It employs a two-step optimization process: optimal features are selected by maximizing feature uniqueness through principal component analysis, and the optimal number of clusters is determined by maximizing decision boundary distances via support vector machines. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies. Topology Bench, openly available via Dataset 1 (https://zenodo.org/records/13921775) and Code 1 (https://github.com/TopologyBench), promotes accessibility, consistency, and reproducibility.

Efficiency in public transportation: a new flow direction method for optimizing multi-route networks

ABSTRACT Transit users generally travel between different pairs of stops on the transit line. Therefore, decision-makers should consider the route designs for each passenger’s benefit. In this study, the factor of the number of public transit passengers is combined with trip coefficients such as the route circuity coefficient used to determine the efficiency of public transportation (PT) vehicles. The flow direction performance (FDP), a novel criterion based on the circuity coefficient in PT, is developed. Accordingly, the flow direction method (FDM) a new hybrid metaheuristic optimization method that determines the optimum PT routes, is created. FDM calculates the optimum routes between several predetermined origin points and terminal points. Both sequences of stations and the transit route are optimized at the same time. We tested FDM’s performance on 2 benchmark and 3 real case network samples. Test results show the effectiveness of FDP and robustness of FDM on multi-route problems.

A Priority based Self-Organised MAC Protocol for Real Time Wireless Sensor Network Applications

Wireless Sensor Networks (WSNs) are expressively utilized in various real-time control and monitoring applications. WSNs have been expanded considering the necessities in industrial time-bounded applications to support the dependable and time-bound delivery of data. Recently, Machine Learning (ML) algorithms have been used to address various WSN-related issues. The use of ML techniques supports dynamically modifying MAC settings based on traffic patterns and network conditions. In WSNs to control the communication between a large numbers of tiny, low-power sensor nodes while preserving energy and reducing latency, effective MAC protocols are essential. This paper addresses the ML centered priority-based self-organized MAC (ML-MAC) protocol to provide a priority-based transmission system to ensure the timely delivery of critical data packets. In this research, depending upon the predictions of the ML model, the MAC parameters are dynamically adjusted to find priority-based channel access and the optimal routing path to meet the deadline of critical data packets. From the result analysis, the average throughput and delay of the proposed ML-MAC algorithm outperforms the existing I-MAC protocol.

Exploiting self-evolutionary strategies of components for Dynamic Heterogeneous Redundancy

The network security situation is increasingly serious. Traditional security equipment like firewalls and intrusion detection systems cannot prevent unknown risks due to their passive nature. Dynamic Heterogeneous Redundancy (DHR) actively defends by switching the attack surface and confusing attackers, becoming essential in modern network defense. However, existing DHR approaches based on scheduling algorithms struggle under high-intensity attacks due to resource limitations. To overcome this weakness, we propose a Genetic Algorithm and Particle Swarm Optimization (GAPSO), a novel DHR architecture. GAPSO provides real-time security awareness of host components, maps potential attacker paths, and calculates the risk probability of each component. High-risk components evolve into others in the pool during attacks. Experiments show that GAPSO significantly reduces system risk compared to scheduling-based DHR and effectively delays the hacker’s attack lifecycle. Additionally, we developed a prototype system and evaluated it in a real network environment, obtaining positive results.

AI-based optimization of EM radiation estimates from GSM base stations using traffic data

The fast expansion of mobile networks has sparked worries regarding base station EM radiation's health impacts. Traffic load is commonly ignored when evaluating EM radiation levels using maximum power output. This study proposes utilizing AI and ML on real network traffic data to optimize GSM base station EM radiation estimations. We obtained EM radiation measurements and traffic data from selecting GSM base stations by location and configuration. To predict EM radiation levels, traffic patterns were used to train linear regression, random forests, and neural networks. Base stations were clustered by radiation profile using unsupervised learning. Considering regulatory restrictions and measurement feasibility, an optimization methodology was created to minimize EM radiation estimate inaccuracy. The results show better prediction accuracy than power-based estimations and high generalisability across base station types. Site-specific factors influenced daily EM radiation patterns after clustering. EM radiation levels can be monitored using traffic data and the optimized AI/ML model. This research helps telecom operators and regulators analyze EM radiation more accurately and efficiently. Future projects should include 5G and small cell network extensions and intelligent city platform integration. The suggested method develops data-driven, AI-powered Public Safety and mobile network trust solutions.

Quantifying and predicting evolutionary networks

The process of network evolution is typically characterized by the emergence of topological structures and the intricate interplay between determinism and stochasticity. Our work introduces a novel structural analysis framework to observe fluctuations in the network evolution process, profoundly influenced by the underlying network generation mechanisms. Based on theoretical reasoning and empirical examination, utilizing synthetic, static, and temporal networks, we arrive at two principal conclusions. Firstly, we reveal that the degree and distance distributions of networks exhibit two dynamic phenomena, convergence and divergence on high- and low-frequency curves, depend on the reflections of the network generation mechanism at both topological extreme values and the overall distribution. Secondly, we develop a novel link prediction method based on the convergence of topological fluctuations, which significantly outperforms benchmark algorithms in both real static and temporal networks. These findings are of considerable significance to the study of real network evolution mechanisms, contributing to a more comprehensive understanding of network behavior over time.

APPROACH TO DETECTION OF ANOMALOUS NETWORK TRAFFIC USING LOF AND HBOS ALGORITHMS

The article is devoted to the problem of detecting anomalies in modern computer networks, which is one of the main threats to cyber security. With the development of Internet technologies, the number of devices and the volume of network traffic are constantly increasing, which leads to an increase in the risk of various cyber threats, such as DDoS attacks, zero-day attacks, and exploitation of protocol vulnerabilities. Abnormal network traffic can result from malicious activity and technical malfunctions, such as configuration errors or hardware failures. Specialised algorithms and methods of analysing large volumes of data are used to detect such threats. The paper considers the main methods of detecting anomalies in network traffic, including classical approaches and modern deep and machine learning methods. Special attention is paid to the efficiency of using methods based on convolutional neural networks, long-term memory and their combinations to detect anomalies. An analysis of the disadvantages and advantages of various approaches to detecting anomalous traffic, such as high computational requirements and the complexity of setting up models, is performed. Still, their effectiveness in analysing large volumes of data is noted. One of the main methods used for anomaly analysis is the local outlier algorithm, which compares the density of objects with their neighbours, allowing for the detection of anomalies in regional segments of the data. Another method is histogram-based outlier estimation, which is faster and more efficient using one-dimensional histograms for each variable. The work also explores the application of unsupervised machine learning methods, which allows for analysing network traffic in real time without the need for prior labelling of data. The article also considers the prospects of further testing the proposed methods in real networks. The combined use of LOF and HBOS balances anomaly detection accuracy and data processing speed, essential to ensure continuous system operation in high-load networks. The implementation of similar solutions in actual conditions requires further research, particularly regarding optimising the use of computing resources and adapting methods to the specific conditions of the network environment. Thus, the paper presents a thorough analysis of modern approaches to detecting anomalies in network traffic and substantiates the feasibility of their application in actual conditions to increase the effectiveness of cyber security.

FAIRLANE: A multi-agent approach to priority lane management in diverse traffic composition

The rise of autonomous driving technologies prompts a reevaluation of traditional urban traffic control and lane management. Dedicated lanes for connected and autonomous vehicles (CAVs), with intermittent access for other vehicles, have been proven to enhance road capacity and reduce underutilization moderately. However, this assumes all non-CAVs are smart vehicles, which is different from the current baseline for the street vehicle mix. Presently, our streets feature a mix of CAVs, smart vehicles, and human-driven vehicles, and the research on dedicated lanes using the realistic mixed traffic environment is missing. In this paper, we investigate the enhancement of road utilization using realistic mixed traffic combinations and identify the penetration rate of CAVs and smart vehicles necessary to improve baseline utilization. Previous studies have focused on lane-management strategies in single-vehicle settings, neglecting the interaction of CAVs with neighboring CAVs and smart vehicles. Therefore, we propose a multi-agent reinforcement learning-based framework to facilitate fair utilization of priority lanes, considering driving comfort, traffic efficiency, and safety during lane-changing. Through multiple experiments on a realistic network, our results demonstrate that the proposed framework significantly improves traffic efficiency, particularly when the penetration rate of CAVs is below 40% and Semi-Autonomous Vehicles (SAVs) constitute 50% of the remaining vehicles. The framework outperforms traditional lane management strategies, reducing mean waiting time and increasing average speed. This study provides nuanced information on different vehicle penetration rates, enabling more informed decisions on when to install priority lanes. This highlights the importance of considering mixed traffic environments in designing autonomous vehicle infrastructure and sets the stage for future advancements in urban traffic management.

Measuring network dynamics of opioid overdose deaths in the United States

The US opioid overdose epidemic has been a major public health concern in recent decades. There has been increasing recognition that its etiology is rooted in part in the social contexts that mediate substance use and access; however, reliable statistical measures of social influence are lacking in the literature. We use Facebook’s social connectedness index (SCI) as a proxy for real-life social networks across diverse spatial regions that help quantify social connectivity across different spatial units. This is a measure of the relative probability of connections between localities that offers a unique lens to understand the effects of social networks on health outcomes. We use SCI to develop a variable, called “deaths in social proximity”, to measure the influence of social networks on opioid overdose deaths (OODs) in US counties. Our results show a statistically significant effect size for deaths in social proximity on OODs in counties in the United States, controlling for spatial proximity, as well as demographic and clinical covariates. The effect size of standardized deaths in social proximity in our cluster-robust linear regression model indicates that a one-standard-deviation increase, equal to 11.70 more deaths per 100,000 population in the social proximity of ego counties in the contiguous United States, is associated with thirteen more deaths per 100, 000 population in ego counties. To further validate our findings, we performed a series of robustness checks using a network autocorrelation model to account for social network effects, a spatial autocorrelation model to capture spatial dependencies, and a two-way fixed-effect model to control for unobserved spatial and time-invariant characteristics. These checks consistently provide statistically robust evidence of positive social influence on OODs in US counties. Our analysis provides a pathway for public health interventions informed by social network structures. The statistical robustness of our primary variable of interest, deaths in social proximity, supports the hypothesis of a social network effect on OODs. Using agent-based modeling (ABM) to simulate social networks can offer an effective method to design interventions that incorporate the dynamics of social networks for maximum impact.

DUdetector: A dual-granularity unsupervised model for network anomaly detection

Internet of Things (IoT) devices are often used as springboards for network intrusion due to the open nature of IoT protocol stacks that enable automatic inter-connection and data sharing among devices, so it is critical to develop network anomaly detection algorithms that can be deployed at important nodes such as gateways and routers. However, existing detection algorithms based on signature rules and supervised machine learning heavily rely on known anomaly types, yielding low detection accuracy when deployed in realistic network environments with a significant number of unknown attacks. With this in mind, we propose DUdetector, an unsupervised anomaly detection algorithm by employing Transformer and Conv1d&MaxPool1d AutoEncoder with residual connection (abbr., CM&RC-AE) to realize a dual-granularity learning from the perspective of segments and points, respectively. Specifically, we perform coarse-grained segment-level anomaly detection based on an improved Transformer to detect whether there is any anomalous traffic within a time window. Then, we perform fine-grained point-level anomaly detection based on CM&RC-AE for each packet within the problematic segment output by the first step. Extensive experiments on three datasets (SSDP Flood, Mirai and IDS2017) demonstrate that our DUdetector achieves a better performance than existing work: an F1-score of 95.98% for Mirai, and over 99.2% for both SSDP Flood and IDS2017, with false positive rates less than 0.5% for all three datasets.

Group Sparse β-Model for Network

Sparsity, homogeneity, and heterogeneity are three important characteristics of many real-life networks. The recently proposed Sparse β -Model divides nodes into core ones and peripheral ones to accommodate sparsity, but the parameters of core nodes are assumed to be of similar magnitude, which may not be in line with applications. In this article, we propose the Group Sparse β -Model that splits the core nodes into groups and assumes different orders of magnitude of parameters in different groups, accounting for the heterogeneity among core nodes. When the groups are known, we provide consistent and asymptotically normal moment estimators of the parameters that control the global and local density. Based on that, consistency and asymptotic normality of the maximum likelihood estimators of the remaining parameters are derived. We also establish finite-sample error bounds results. When the groups are unknown, a ratio method is proposed to detect groups, which is computationally efficient. Simulations show competitive results and the analysis of a corporate inter-relationships network illustrates the usefulness of the proposed model.

Social media’s impact on environmental awareness: a marginal treatment effect analysis of WeChat usage in China

Environmental awareness serves as an intrinsic motivator for individuals’ engagement in environmental protection behaviors, and social media platforms can foster such awareness. Drawing on the 2018 China General Social Survey data (CGSS 2018), this study employs non-parametric and semi-parametric approaches within the framework of marginal treatment effect (MTE) to examine the influence of WeChat usage on the environmental awareness of Chinese residents. The findings unveil significant heterogeneity in the impact of frequent WeChat usage on individuals’ levels of environmental awareness. Through estimation of the average treatment effect, treatment group average treatment effect, and non-treatment group average treatment effect, the study reveals that WeChat exerts a stronger positive influence on the environmental awareness of residents with a high propensity for using the platform, while it has no discernible effect on the environmental awareness of residents with a low inclination to engage with WeChat. The growth of environmental knowledge facilitated by WeChat plays a crucial role in shaping environmental awareness. Furthermore, this research underscores the influence of real-life social network structures on information exchange within the WeChat platform in China. This observation suggests that WeChat transcends its role as a mere information transmission tool and becomes intricately woven into the social network fabric of its users, thus mirroring certain characteristics of information dissemination within offline social networks. These insights imply that targeted strategies can be devised to bolster residents’ environmental awareness by capitalizing on the interconnectedness of real-life social networks and leveraging the information exchange potential of WeChat. By doing so, it is possible to enhance environmental protection endeavors and encourage more sustainable behaviors among individuals.

An improved dynamic-sensitive centrality based on interactive influence for identifying influential nodes in aviation networks

The identification of influential nodes in complex networks is a hot topic among scholars. Classical methods commonly analyze single node information or static structures but seldom emphasize dynamic properties and the interactive influence of nodes. Here, we proposed an improved Dynamic-Sensitive centrality (IDS) method by considering the interactive influence of both the self and neighbor nodes. Based on six real aviation networks and the Susceptible Infected Recovered (SIR) spreading disease model, we simulated the actual spreading process within these networks. Relevant experiments were conducted through Kendall’s correlation coefficient, the imprecision function, and the complementary cumulative distribution function. The experimental results demonstrated that the IDS can more accurately identify the influential node and effectively differentiate the node influence in the network compared with other benchmark methods. Especially in the EU air-2 network, the IDS results in Kendall’s correlation coefficient are improved by 105% compared to the DS centrality.

A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm

For the protection of information security, link prediction, as a basic problem of network science, has important application significance. However, most of the existing link prediction algorithms rely on the node information of the graph structure, which is not applicable in some graph structure data involving privacy. At the same time, most of the algorithms only consider the general graph structure and do not fully consider the high-order information in the graph. Because of this, this paper proposes an algorithm called hypergraph-based link prediction with self-attention (HWA) to solve the above problems. The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. Experiments show that the HWA algorithm proposed in this paper, combined with the GCN framework, shows better link prediction performance than other graph-based neural network benchmark algorithms on eight real networks. This further verifies the validity and reliability of the model in this paper and provides new protection ideas and technical means for information security.

The disruption index suffers from citation inflation: Re-analysis of temporal CD trend and relationship with team size reveal discrepancies

Measuring the rate of innovation in academia and industry is fundamental to monitoring the efficiency and competitiveness of the knowledge economy. To this end, a disruption index (CD) was recently developed and applied to publication and patent citation networks (Wu et al., 2019; Park et al., 2023). Here we show that CD systematically decreases over time due to secular growth in research production, following two distinct mechanisms unrelated to innovation – one behavioral and the other structural. Whereas the behavioral explanation reflects shifts associated with techno-social factors (e.g. self-citation practices), the structural explanation follows from ‘citation inflation’ (CI), an inextricable feature of real citation networks attributable to increasing reference list lengths, which causes CD to systematically decrease. We demonstrate this causal link by way of mathematical deduction, computational simulation, multi-variate regression, and quasi-experimental comparison of the disruptiveness of PNAS versus PNAS Plus articles, which differ primarily in their lengths. Accordingly, we analyze CD data available in the SciSciNet database and find that disruptiveness incrementally increased from 2005-2015, and that the negative relationship between disruption and team-size is remarkably small in overall magnitude effect size, and shifts from negative to positive for team size ≥ 8 coauthors.

Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge

Influence Maximization (IM) stands as a central challenge within the domain of complex network analysis, with the primary objective of identifying an optimal seed set of a predetermined size that maximizes the reach of influence propagation. Over time, numerous methodologies have been proposed to address the IM problem. However, one certain network referred to as Imbalanced Heterogeneous Networks (IHN), which widely used in social situation, urban and rural areas, and merchandising, presents challenges in achieving high-quality solutions. In this work, we introduce the Lightweight Reinforcement Learning algorithm with Prior knowledge (LRLP), which leverages the Struc2Vec graph embedding technique that captures the structural similarity of nodes to generate vector representations for nodes within the network. In details, LRLP incorporates prior knowledge based on a group of centralities, into the initial experience pool, which accelerates the reinforcement learning training for better solutions. Additionally, the node embedding vectors are input into a Deep Q Network (DQN) to commence the lightweight training process. Experimental evaluations conducted on synthetic and real networks showcase the effectiveness of the LRLP algorithm. Notably, the improvement seems to be more pronounced when the the scale of the network is larger. We also analyze the effect of different graph embedding algorithms and prior knowledge on algorithmic results. Moreover, we conduct an analysis about some parameters, such as number of seed set selections T, embedding dimension d and network update frequency C. It is significant that the reduction of number of seed set selections T not only keeps the quality of solutions, but lowers the algorithm’s computational cost.

Network Embedding-based Directed Community Detection with Unknown Community Number

Community detection of network analysis plays an important role in numerous application areas, in which estimating the number of communities is a fundamental issue. However, many existing methods focus on undirected networks ignoring the directionality of edges or unrealistically assume that the number of communities is known a priori. In this article, we develop a data-dependent community detection method for the directed network to determine the number of communities and recover community structures simultaneously, which absorbs the ideas of network embedding and penalized fusion by embedding the out- and in-nodes into low-dimensional vector space and forcing the embedding vectors toward its center. The asymptotic consistency properties of the proposed method are established in terms of network embedding, directed community detection, and estimation of the number of communities. The proposed method is applied on synthetic networks and real brain functional networks, which demonstrate the superior performance of the proposed method against a number of competitors. Supplementary materials for this article are available online.

Mining contextually meaningful subgraphs from a vertex-attributed graph

Networks have emerged as a natural data structure to represent relations among entities. Proteins interact to carry out cellular functions and protein-Protein interaction network analysis has been employed for understanding the cellular machinery. Advances in genomics technologies enabled the collection of large data that annotate proteins in interaction networks. Integrative analysis of interaction networks with gene expression and annotations enables the discovery of context-specific complexes and improves the identification of functional modules and pathways. Extracting subnetworks whose vertices are connected and have high attribute similarity have applications in diverse domains. We present an enumeration approach for mining sets of connected and cohesive subgraphs, where vertices in the subgraphs have similar attribute profile. Due to the large number of cohesive connected subgraphs and to overcome the overlap among these subgraphs, we propose an algorithm for enumerating a set of representative subgraphs, the set of all closed subgraphs. We propose pruning strategies for efficiently enumerating the search tree without missing any pattern or reporting duplicate subgraphs. On a real protein-protein interaction network with attributes representing the dysregulation profile of genes in multiple cancers, we mine closed cohesive connected subnetworks and show their biological significance. Moreover, we conduct a runtime comparison with existing algorithms to show the efficiency of our proposed algorithm.

Social network structure modulates neural activities underlying group norm processing: evidence from event-related potentials.

Social ties play a crucial role in determining the health and wellbeing of individuals. However, it remains unclear whether the capacity to process social information distinguishes well-connected individuals from their less-connected peers. This study explored how an individual's social network structure influences the dynamic processing of group norms, utilizing event-related potentials (ERPs). The study involved 43 university students from the same class who participated in a social network study measuring metrics such as real-life social network size, in-degree, out-degree, and betweenness centrality. Subsequently, 27 students participated in an EEG study assessing their willingness to engage in various exercises after being exposed to peer feedback or in its absence. The results indicate that an individual's social network structure is significantly associated with the dynamic processing of group norms. Notably, well-connected individuals exhibited larger ERP amplitudes linked to feedback (e.g., N200, P300, and LPP), greater functional segregation within the brain network (e.g., local efficiency and clustering coefficient), and enhanced synchronization within frontal area and across different brain areas. These findings highlight that well-connected individuals possess enhanced sensitivity and efficiency in processing social information, pointing to potential areas for further research on the factors influencing social network evolution.