Nodes In Large-scale Networks Research Articles

Key Vulnerable Nodes Discovery Based on Bayesian Attack Subgraphs and Improved Fuzzy C-Means Clustering

Aiming at the problem that the search efficiency of key vulnerable nodes in large-scale networks is not high and the consideration factors are not comprehensive enough, in order to improve the time and space efficiency of search and the accuracy of results, a key vulnerable node discovery method based on Bayesian attack subgraphs and improved fuzzy C-means clustering is proposed. Firstly, the attack graph is divided into Bayesian attack subgraphs, and the analysis results of the complete attack graph are quickly obtained by aggregating the information of the attack path analysis in the subgraph to improve the time and space efficiency. Then, the actual threat features of the vulnerability nodes are extracted from the analysis results, and the threat features of the vulnerability itself in the common vulnerability scoring standard are considered to form the clustering features together. Next, the optimal number of clusters is adaptively adjusted according to the variance idea, and fuzzy clustering is performed based on the extracted clustering features. Finally, the key vulnerable nodes are determined by setting the feature priority. Experiments show that the proposed method can optimize the time and space efficiency of analysis, and the fuzzy clustering considering multiple features can improve the accuracy of analysis results.

Effective graph-neural-network based models for discovering Structural Hole Spanners in large-scale and diverse networks

A Structural Hole Spanner (SHS) is a set of nodes in a network that act as a bridge among different otherwise disconnected communities. While several solutions exist for SHS discovery, they often suffer from high runtime, particularly in large-scale networks. Additionally, discovering SHSs across diverse networks poses another challenge, as the traditional one-model-fits-all approach fails to capture inter-graph differences, especially in diverse networks. Therefore, there is an urgent need of developing effective solutions for discovering SHSs in large-scale and diverse networks. Inspired by the recent advancement of graph neural network approaches on various graph problems, we propose graph neural network-based models to discover SHS nodes in large-scale and diverse networks. Our approach transform the problem into a learning problem and propose an efficient model GraphSHS, that exploits both the network structure and node features to discover SHS nodes in large scale networks, endeavouring to lessen the computational cost while maintaining high accuracy. To effectively discover SHSs across diverse networks, we propose another model Meta-GraphSHS based on meta-learning that learns generalizable knowledge from diverse training graphs (instead of directly learning the model) and utilizes the learned knowledge to create a customized model to identify SHSs in each new graph. Theoretically, we demonstrate that the depth of the proposed graph neural network model should be at least Ω(n/logn) to accurately calculate the SHSs discovery problem. We evaluate the performance of the proposed models through extensive experiments on synthetic and real-world datasets. Our experimental results show that GraphSHS discovers SHSs with high accuracy and is at least 167.1 times faster than the comparative methods on large-scale real-world datasets. Furthermore, Meta-GraphSHS effectively discovers SHSs across diverse synthetic networks with an accuracy of 96.2%.

Convex Isolating Clustering Centrality to Discover the Influential Nodes in Large Scale Networks

Convex Isolating Clustering Centrality to Discover the Influential Nodes in Large Scale Networks

Estimation and update of betweenness centrality with progressive algorithm and shortest paths approximation

Betweenness centrality is one of the key measures of the node importance in a network. However, it is computationally intractable to calculate the exact betweenness centrality of nodes in large-scale networks. To solve this problem, we present an efficient CBCA (Centroids based Betweenness Centrality Approximation) algorithm based on progressive sampling and shortest paths approximation. Our algorithm firstly approximates the shortest paths by generating the network centroids according to the adjacency information entropy of the nodes; then constructs an efficient error estimator using the Monte Carlo Empirical Rademacher averages to determine the sample size which can achieve a balance with accuracy; finally, we present a novel centroid updating strategy based on network density and clustering coefficient, which can effectively reduce the computation burden of updating shortest paths in dynamic networks. The experimental results show that our CBCA algorithm can efficiently output high-quality approximations of the betweenness centrality of a node in large-scale complex networks.

XG-BoT: An explainable deep graph neural network for botnet detection and forensics

In this paper, we propose XG-BoT, an explainable deep graph neural network model for botnet node detection. The proposed model comprises a botnet detector and an explainer for automatic forensics. The XG-BoT detector can effectively detect malicious botnet nodes in large-scale networks. Specifically, it utilizes a grouped reversible residual connection with a graph isomorphism network to learn expressive node representations from botnet communication graphs. The explainer, based on the GNNExplainer and saliency map in XG-BoT, can perform automatic network forensics by highlighting suspicious network flows and related botnet nodes. We evaluated XG-BoT using real-world, large-scale botnet network graph datasets. Overall, XG-BoT outperforms state-of-the-art approaches in terms of key evaluation metrics. Additionally, we demonstrate that the XG-BoT explainers can generate useful explanations for automatic network forensics.

Systems using Wireless Sensor Networks for Big Data

Wireless sensor networks are continually developing in the big data world and are widely employed in many aspects of life. In the monitoring region, the WSN gathers, analyses, and sends information about the detected item. In recent years, WSN has also made important strides in the management of critical data protection, traffic monitoring, and climate - change detection. The rich big data contributors known as wireless sensor networks provide a significant amount of data from numerous sensor nodes in large-scale networks (WSNs), which are among the numerous potential datasets. However, unlike traditional wireless networks, suffer from significant constraints in communication and data dependability due to the cluster’s constraints. This paper gives a detailed assessment of cutting-edge research on using WSN into large data systems Potential network and effective deployment and scientific problems are presented and discussed in the context of the study topics and aim. Finally, unresolved issues are addressed in order to discuss interesting future research possibilities.

Indexing complex networks for fast attributed kNN queries

The k nearest neighbor (kNN) query is an essential graph data-management tool used for finding relevant data entities suited to a user-specified query node. Graph indexing methods have the potential to achieve a quick kNN search response and thus are promising approaches. However, they struggle to handle large-scale attributed complex networks. This is because constructing indices and querying kNN nodes in large-scale networks are computationally expensive, and they are not designed to handle node attributes included in the networks. In this paper, we propose a novel graph indexing algorithm, namely CT index, for fast kNN queries on large complex networks. To overcome the aforementioned limitations, our algorithm generates two types of indices based on the topological properties of complex networks. In addition, we further propose BAG index along with CT index so that our algorithm enables to explore kNN nodes based on the attribute similarity. Our extensive experiments on real-world graphs show that our algorithm achieves up to 18,074 times faster indexing and 146 times faster kNN query than other state-of-the-art methods.

Node and network resistance to bribery in multi-agent systems

In this paper, we propose a framework to study the resistance to bribery of nodes in a network via average consensus. We extend the proposed bribery resistance measure to sets of nodes, and networks. The proposed framework evaluates quantitatively how much an external entity needs to drive the state of an agent away from its current state, to change the final consensus value. Subsequently, we illustrate our framework with a set of examples, namely: i) how we can use it to compute the bribing resistance of each node in a network; ii) comparing our measure against metrics from the literature in measuring network bribing resistance; iii) how we may utilize the proposed framework to evaluate the bribing resistance of clusters/groups of nodes in large-scale networks.

Efficient network immunization under limited knowledge.

Targeted immunization of centralized nodes in large-scale networks has attracted significant attention. However, in real-world scenarios, knowledge and observations of the network may be limited, thereby precluding a full assessment of the optimal nodes to immunize (or quarantine) in order to avoid epidemic spreading such as that of the current coronavirus disease (COVID-19) epidemic. Here, we study a novel immunization strategy where only n nodes are observed at a time and the most central among these n nodes is immunized. This process can globally immunize a network. We find that even for small n (≈10) there is significant improvement in the immunization (quarantine), which is very close to the levels of immunization with full knowledge. We develop an analytical framework for our method and determine the critical percolation threshold pc and the size of the giant component P∞ for networks with arbitrary degree distributions P(k). In the limit of n → ∞ we recover prior work on targeted immunization, whereas for n = 1 we recover the known case of random immunization. Between these two extremes, we observe that, as n increases, pc increases quickly towards its optimal value under targeted immunization with complete information. In particular, we find a new general scaling relationship between |pc(∞) − pc(n)| and n as |pc(∞) − pc(n)| ∼ n−1exp(−αn). For scale-free (SF) networks, where P(k) ∼ k−γ, 2 < γ < 3, we find that pc has a transition from zero to nonzero when n increases from n = 1 to O(log N) (where N is the size of the network). Thus, for SF networks, having knowledge of ≈log N nodes and immunizing the most optimal among them can dramatically reduce epidemic spreading. We also demonstrate our limited knowledge immunization strategy on several real-world networks and confirm that in these real networks, pc increases significantly even for small n.

Optimal Measurement Policy for Linear Measurement Systems With Applications to UAV Network Topology Prediction

Dynamic network topology can pose important challenges to communication and control protocols in networks of autonomous vehicles. For instance, maintaining connectivity is a key challenge in unmanned aerial vehicle (UAV) networks. However, tracking and computational resources of the observer module might not be sufficient for constant monitoring of all surrounding nodes in large-scale networks. In this article, we propose an optimal measurement policy for network topology monitoring under constrained resources. To this end, We formulate the localization of multiple objects in terms of linear networked systems and solve it using Kalman filtering with intermittent observation. The proposed policy includes two sequential steps. We first find optimal measurement attempt probabilities for each target using numerical optimization methods to assign the limited number of resources among targets. The optimal resource allocation follows a waterfall-like solution to assign more resources to targets with lower measurement success probability. This provides a 10% to 60% gain in prediction accuracy. The second step is finding optimal on-off patterns for measurement attempts for each target over time. We show that a regular measurement pattern that evenly distributed resources over time outperforms the two extreme cases of using all measurement resources either in the beginning or at the end of the measurement cycle. Our proof is based on characterizing the fixed-point solution of the error covariance matrix for regular patterns. Extensive simulation results confirm the optimality of the most alternating pattern with up to 10-fold prediction improvement for different scenarios. These two guidelines define a general policy for target tracking under constrained resources with applications to network topology prediction of autonomous systems.

Identification of Critical Nodes in Large-Scale Spatial Networks

The notion of network connectivity is used to characterize the robustness and failure tolerance of networks, with high connectivity being a desirable feature. In this paper, we develop a novel gradient flow-based approach to the problem of identifying critical nodes in large-scale networks, with algebraic connectivity (the second smallest eigenvalue of the graph Laplacian) as the chosen metric. Employing a graph-embedding technique, we reduce the class of considered weight-balanced graphs to spatial networks with uniformly distributed nodes and nearest-neighbors communication topologies. Through a continuum approximation, we consider the Laplace operator on a manifold (with the Neumann boundary condition) as the limiting case of the graph Laplacian. We then reduce the critical node set identification problem to that of finding a ball of fixed radius, whose removal minimizes the second (Neumann) eigenvalue of the Laplace operator on the residual domain. This leads us to consider two functional and nested optimization problems. Resorting to the min–max theorem, we first treat the problem of determining the second smallest eigenvalue for a fixed domain by minimizing an energy functional. We then obtain a closed-form expression for a projected gradient flow that converges to the set of eigenfunctions of the Laplace operator and prove that the only locally asymptotically stable critical point is the second eigenfunction. Building on these results, we consider the critical ball identification problem and define novel dynamics to converge asymptotically to these points. Finally, we provide a characterization of the location of critical nodes (for infinitesimally small balls) as those points that belong to the nodal set of the second eigenfunction of the Laplacian operator.

Wireless Sensor Networks for Big Data Systems.

Before discovering meaningful knowledge from big data systems, it is first necessary to build a data-gathering infrastructure. Among many feasible data sources, wireless sensor networks (WSNs) are rich big data sources: a large amount of data is generated by various sensor nodes in large-scale networks. However, unlike typical wireless networks, WSNs have serious deficiencies in terms of data reliability and communication owing to the limited capabilities of the nodes. Moreover, a considerable amount of sensed data are of no interest, meaningless, and redundant when a large number of sensor nodes is densely deployed. Many studies address the existing problems and propose methods to overcome the limitations when constructing big data systems with WSN. However, a published paper that provides deep insight into this research area remains lacking. To address this gap in the literature, we present a comprehensive survey that investigates state-of-the-art research work on introducing WSN in big data systems. Potential applications and technical challenges of networks and infrastructure are presented and explained in accordance with the research areas and objectives. Finally, open issues are presented to discuss promising directions for further research.

Software Group Rejuvenation Based on Matrix Completion and Cerebellar Model Articulation Controller

This paper aims to accurately evaluate the aging state of nodes in largescale networks and identify the optimal rejuvenation plan for these nodes. To this end, the aging phenomenon in distributed systems was described as a random low-rank matrix. The CMAC network was introduced to collect the data of network nodes and evaluate their aging state and rejuvenation plan. Based on the aging state and plan applicability, the node relationship was integrated with matrix completion, aiming to improve the efficiency of aging evaluation. Compared to the traditional methods, our method significantly improved aging evaluation and reduced hardware cost, and offered suitable rejuvenation plans for aging nodes. The improvement is partially attributable to the incorporation of node relationship. The research findings shed new light on software aging and group rejuvenation.

Wireless Sensor Network Lifetime Enhancement Using Modified Clustering and Scheduling Algorithm

Random distribution of sensor nodes in large scale network leads redundant nodes in the application field. Sensor nodes are with irreplaceable battery in nature, which drains the energy due to repeated collection of data and decreases network lifetime. Scheduling algorithms are the one way of addressing this issue. In proposed method, an optimized sleep scheduling used to enhance the network lifetime. While using the scheduling algorithm, the target coverage and data collection must be maintained throughout the network. In-network, aggregation method also used to remove the unwanted information in the collected data in level. Modified clustering algorithm highlights three cluster heads in each cluster which are separated by minimum distance between them. The simulation results show the 20% improvement in network lifetime, 25% improvement in throughput and 30% improvement in end to end delay.

A Novel Method for Survivability Test Based on End Nodes in Large Scale Network

Survivability is a necessary property of network system in disturbed environment. Recovery ability is a key actor of survivability. This paper concludes network survivability into a novel composite metric, i.e. Network Recovery Degree (NRD). In order to measure this metric in quantity, a concept of Source-Destination Pair (SD Pair), is created to abstract end-to-end activity based on end nodes in network, and the quality of SD Pair is also used to describe network performance, such as connectivity, quality of service, link degree, and so on. After that, a Survivability Test method in large scale Network based on SD pairs, called STNSD, is provided. How to select SD Pairs effectively in large scale network is also provided. We set up simulation environment to validate the test method in a severe destroy scenario and evaluate the method scalability in different large scale network scenarios. Experiment and analysis shows that the metric NRD correctly reflects the effort of different survivability strategy, and the proposed test method STNSD has good scalability and can be used to test and evaluate quantitative survivability in large scale network.

Generalized friendship paradox in complex networks: The case of scientific collaboration

The friendship paradox states that your friends have on average more friends than you have. Does the paradox “hold” for other individual characteristics like income or happiness? To address this question, we generalize the friendship paradox for arbitrary node characteristics in complex networks. By analyzing two coauthorship networks of Physical Review journals and Google Scholar profiles, we find that the generalized friendship paradox (GFP) holds at the individual and network levels for various characteristics, including the number of coauthors, the number of citations, and the number of publications. The origin of the GFP is shown to be rooted in positive correlations between degree and characteristics. As a fruitful application of the GFP, we suggest effective and efficient sampling methods for identifying high characteristic nodes in large-scale networks. Our study on the GFP can shed lights on understanding the interplay between network structure and node characteristics in complex networks.

Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network

Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.

A Probabilistic Computing Approach of Attack Graph-Based Nodes in Large-scale Network

A Probabilistic Computing Approach of Attack Graph-Based Nodes in Large-scale Network