Anomaly analysis and visualization for dynamic networks through spatiotemporal graph segmentations

Background Autism spectrum disorder (ASD) is characterized by aberrant functional brain connectivity and deficits in network dynamics. Transcranial direct current stimulation (tDCS) has emerged as a promising intervention with potential therapeutic effects; however, its effects on both static and dynamic functional brain network organization remained insufficiently understood. Methods A total of 42 children with ASD aged 4–6 years were enrolled and randomly assigned to either active tDCS or sham stimulation groups. Resting-state electroencephalography (EEG) data were acquired before and after the intervention. Low-order functional connectivity (LOFC) and high-order functional connectivity (HOFC) networks were constructed, followed by graph-theoretical analyses to assess clustering coefficient, characteristic path length, global efficiency, and local efficiency. Furthermore, state entropy was employed to evaluate dynamic network transitions between integrated and segregated states. Results Active tDCS was associated with increased LOFC strength in the delta, alpha, and beta bands, and more widespread increases in HOFC across all examined frequency bands. Changes in network topology were primarily observed in HOFC, with reductions in characteristic path length and increases in global and local efficiency, particularly in the delta and theta bands. Dynamic network analysis indicated that tDCS modulated state entropy at specific time scales in both LOFC and HOFC networks. These findings suggest shifts in functional coordination and temporal variability among the recorded regions. Behavioral measures exhibited a trend toward improvement in the active group; however, these changes were not the focus of the present analysis, and their relationship to neural modulation remains to be clarified in future work. Conclusions tDCS modulated functional interaction patterns and dynamic state characteristics among the recorded brain regions in children with ASD. These results provide preliminary neurophysiological evidence regarding the influence of tDCS on both static and dynamic network organization and highlight potential network-based markers to guide future individualized neuromodulation research. Further studies with larger samples and longitudinal follow-ups are needed to clarify the functional and clinical significance of these network-level changes.

The field encompassing biomimetics, bioinspiration and nature inspiration in engineering science is growing steadily, pushed by exogenous factors like the search for potentially sustainable engineering solutions that might already exist in nature. With the help of information provided by a bibliometric database and further processed with a dynamic network and semantic analysis tool, we provide insight at two scales into the corpus of nature-inspired engineering field and its dynamics. At the macroscale, the Web of Science® (WoS) categories, countries and institutions are ranked and ordered by thematic clusters and country networks, highlighting the leading countries and institutions and how they focus on specific topics. Such an insight provides an overview at the macroscale that can be valuable to orient scientific strategy at the country level. At the mesoscale, where science is incarnated by collaborative networks of authors and institutions that run across countries, we identify six semantic clusters and subclusters within them, and their dynamics. We also pinpoint leading academic collaborative networks and their activity in relation to the six semantic clusters. Trends and prospective are also discussed. Typically, one observes that the field is becoming mature since, starting by imitating nature, it proceeded with mimicking more complex natural structures and functions and now it investigates ways used in nature in response to changes in the environment and implements them in innovative and adaptive artefacts. The sophistication of devices, methods and tools has been increasing over the years as well as their functionalities and adaptability, whereas the size of devices has decreased at the same time.

Wireless multihop networks are being increasingly used in military and civilian applications. Advanced applications of wireless multihop networks demand better understanding on their properties. Existing research on wireless multihop networks has largely focused on static networks, where the network topology is time-invariant; and there is comparatively a lack of understanding on the properties of dynamic networks with dynamically changing topology. In this paper, we use and extend a recently proposed graph theoretic model, i.e. evolving graphs, to capture the characteristics of such networks. We extend and develop the concepts of route matrix, connectivity matrix and probabilistic connectivity matrix as convenient tools to characterize and investigate the properties of evolving graphs and the associated dynamic networks. The properties of these matrices are established and their relevance to the properties of dynamic wireless multihop networks are introduced.

Access to resources depends on an individual's position within the environment. This is particularly important to animals that invest heavily in nest construction, such as social insects. Many ant species have a polydomous nesting strategy: a single colony inhabits several spatially separated nests, often exchanging resources between the nests. Different nests in a polydomous colony potentially have differential access to resources, but the ecological consequences of this are unclear. In this study, we investigate how nest survival and budding in polydomous wood ant (Formica lugubris) colonies are affected by being part of a multi‐nest system. Using field data and novel analytical approaches combining survival models with dynamic network analysis, we show that the survival and budding of nests within a polydomous colony are affected by their position in the nest network structure. Specifically, we find that the flow of resources through a nest, which is based on its position within the wider nest network, determines a nest's likelihood of surviving and of founding new nests. Our results highlight how apparently disparate entities in a biological system can be integrated into a functional ecological unit. We also demonstrate how position within a dynamic network structure can have important ecological consequences.

This article presents a platform for interactive graph mining and relational machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing important insights quickly as well as learning an appropriate and highly predictive model for a particular task (e.g., classification, link prediction, discovering the roles of nodes, and finding influential nodes). Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data. In particular, we propose techniques for interactive relational learning (e.g., node/link classification), interactive link prediction and weighting, role discovery and community detection, higher-order network analysis (via graphlets, network motifs), among others. GraphVis also allows for the refinement and tuning of graph mining and relational learning methods for specific application domains and constraints via an end-to-end interactive visual analytic pipeline that learns, infers, and provides rapid interactive visualization with immediate feedback at each change/prediction in real-time. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators (including new block model approaches), and a variety of multi-level network analysis techniques.

Distributed computation in dynamic networks via random walks

Many data analysis problems rely on dynamic networks, such as social or communication network analyses. Providing a scalable overview of long sequences of such dynamic networks remains challenging due to the underlying large-scale data containing elusive topological changes. We propose two complementary pixel-based visualizations, which reflect occurrences of selected sub-networks (motifs) and provide a time-scalable overview of dynamic networks: a network-level census (motif significance profiles) linked with a node-level sub-network metric (graphlet degree vectors) views to reveal structural changes, trends, states, and outliers. The network census captures significantly occurring motifs compared to their expected occurrences in random networks and exposes structural changes in a dynamic network. The sub-network metrics display the local topological neighborhood of a node in a single network belonging to the dynamic network. The linked pixel-based visualizations allow exploring motifs in different-sized networks to analyze the changing structures within and across dynamic networks, for instance, to visually analyze the shape and rate of changes in the network topology. We describe the identification of visual patterns, also considering different reordering strategies to emphasize visual patterns. We demonstrate the approach’s usefulness by a use case analysis based on real-world large-scale dynamic networks, such as the evolving social networks of Reddit or Facebook.

BackgroundMany biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis.ResultsIn this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets.ConclusionsTVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.

The analysis of network data is of interest to many disciplines, ranging from sociology to computer science. Recent interest has shifted from static networks to dynamic networks, which evolve over time. A fundamental problem in the analysis of dynamic networks is tracking long-term trends, which are obscured by short-term variations. In this paper, we propose a method for minimum mean-squared error tracking of dynamic networks using a recursive shrinkage estimation framework that accounts for the spatial correlation in the network. Unlike model-based tracking methods such as the Kalman filter, the proposed method does not require knowledge about the network dynamics. We demonstrate that the proposed method is able to track dynamic networks effectively through experiments on simulated and real networks.

Service provisioning has gained significant attention as a promising programming model for heterogeneous wireless sensor networks. Its key idea is to exploit the decoupling of service providers and consumers to enable platform-independent applications that are dynamically bound to platform-specific services. We explore novel adaptive service binding strategies that are able to cope with network dynamics and to promote energy conservation. To achieve this goal, we developed policies and algorithms that automatically switch providers in response to network topology changes and adapt application behavior when opportunities for energy savings surface. The latter is accomplished by providing limited information about the energy consumption associated with using various services, by systematically exploiting opportunities for sharing service invocations, and by exploiting the broadcast nature of wireless communication in WSNs. The policies and algorithms have been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can significantly increase service availability and enable energy-aware service binding decisions that result in increased energy efficiency, while imposing minimal additional burden on the application, service, and device developers. Applications involving medical patient monitoring and structural health monitoring are used in the evaluation process. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Adaptive Service Provisioning for Wireless Sensor Networks Chien-Liang Fok, Gruia-Catalin Roman and Chenyang Lu Washington University in St. Louis {liang, roman, lu}@cse.wustl.edu Abstract—Service provisioning has gained significant attention as a promising programming model for heterogeneous wireless sensor networks. Its key idea is to exploit the decoupling of service providers and consumers to enable platform-independent applications that are dynamically bound to platform-specific services. We explore novel adaptive service binding strategies that are able to cope with network dynamics and to promote energy conservation. To achieve this goal, we developed policies and algorithms that automatically switch providers in response to network topology changes and adapt application behavior when opportunities for energy savings surface. The latter is accomplished by providing limited information about the energy consumption associated with using various services, by systematically exploiting opportunities for sharing service invocations, and by exploiting the broadcast nature of wireless communication in WSNs. The policies and algorithms have been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can significantly increase service availability and enable energyaware service binding decisions that result in increased energy efficiency, while imposing minimal additional burden on the application, service, and device developers. Applications involving medical patient monitoring and structural health monitoring are used in the evaluation process, demonstrating the system’s efficacy.Service provisioning has gained significant attention as a promising programming model for heterogeneous wireless sensor networks. Its key idea is to exploit the decoupling of service providers and consumers to enable platform-independent applications that are dynamically bound to platform-specific services. We explore novel adaptive service binding strategies that are able to cope with network dynamics and to promote energy conservation. To achieve this goal, we developed policies and algorithms that automatically switch providers in response to network topology changes and adapt application behavior when opportunities for energy savings surface. The latter is accomplished by providing limited information about the energy consumption associated with using various services, by systematically exploiting opportunities for sharing service invocations, and by exploiting the broadcast nature of wireless communication in WSNs. The policies and algorithms have been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can significantly increase service availability and enable energyaware service binding decisions that result in increased energy efficiency, while imposing minimal additional burden on the application, service, and device developers. Applications involving medical patient monitoring and structural health monitoring are used in the evaluation process, demonstrating the system’s efficacy.

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid tumors. The rapid progression of PDAC results in an advanced stage of patients when diagnosed. However, the dynamic molecular mechanism underlying PDAC progression remains far from clear.MethodsThe microarray GSE62165 containing PDAC staging samples was obtained from Gene Expression Omnibus and the differentially expressed genes (DEGs) between normal tissue and PDAC of different stages were profiled using R software, respectively. The software program Short Time-series Expression Miner was applied to cluster, compare, and visualize gene expression differences between PDAC stages. Then, function annotation and pathway enrichment of DEGs were conducted by Database for Annotation Visualization and Integrated Discovery. Further, the Cytoscape plugin DyNetViewer was applied to construct the dynamic protein–protein interaction networks and to analyze different topological variation of nodes and clusters over time. The phosphosite markers of stage-specific protein kinases were predicted by PhosphoSitePlus database. Moreover, survival analysis of candidate genes and pathways was performed by Kaplan–Meier plotter. Finally, candidate genes were validated by immunohistochemistry in PDAC tissues.ResultsCompared with normal tissues, the total DEGs number for each PDAC stage were 994 (stage I), 967 (stage IIa), 965 (stage IIb), 1027 (stage III), 925 (stage IV), respectively. The stage-course gene expression analysis showed that 30 distinct expressional models were clustered. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the up-regulated DEGs were commonly enriched in five fundamental pathways throughout five stages, including pathways in cancer, small cell lung cancer, ECM-receptor interaction, amoebiasis, focal adhesion. Except for amoebiasis, these pathways were associated with poor PDAC overall survival. Meanwhile, LAMA3, LAMB3, LAMC2, COL4A1 and FN1 were commonly shared by these five pathways and were unfavorable factors for prognosis. Furthermore, by constructing the stage-course dynamic protein interaction network, 45 functional molecular modules and 19 nodes were identified as featured regulators for all PDAC stages, among which the collagen family and integrins were considered as two main regulators for facilitating aggressive progression. Additionally, the clinical relevance analysis suggested that the stage IV featured nodes MLF1IP and ITGB4 were significantly correlated with shorter overall survival. Moreover, 15 stage-specific protein kinases were identified from the dynamic network and CHEK1 was particularly activated at stage IV. Experimental validation showed that MLF1IP, LAMA3 and LAMB3 were progressively increased from tumor initiation to progression.ConclusionsOur study provided a view for a better understanding of the dynamic landscape of molecular interaction networks during PDAC progression and offered potential targets for therapeutic intervention.

The relevance of nodes with respect to the position they have in a network is often investigated with centrality measures. In particular, in cases where it is specifically meaningful to consider nodes’ ability to cumulate and convey information, like in economic systems, betweenness centrality is one of the most pertinent options because of its underlying concept. However, this statistic presents two limitations. First, as it relies on the computation of shortest paths, it is grounded on a binary topological evaluation: every time a node is not located in the shortest path between two other nodes, it gains no score at all in its centrality (even if it is located on a path just one step longer). Second, betweenness centrality does not allow the direct analysis of multi-dimensional and dynamic networks: it has to be computed one dimension and one instant at a time, and this causes problems of comparability in case of weighted connections. The scope and the originality of this work is to design a network model that makes it possible to solve these issues. The proposed Dynamic Multi-Layer Network (DMLN) allows the structural representation of the multi-dimensional and dynamic properties of nodes’ interactions. Then, this allows the computation of a metric that, based on Infomap random walks, assesses the level of information cumulated and conveyed by nodes in any moment and in any dimension of interaction. Importantly, this is performed without relying on a binary evaluation, and by jointly taking into account what occurred in all the dimensions and during the entire period, in which the system is observed. We present and discuss an implementation based on ICT worldwide trade of goods and services in the period 2004–2014.

Primary goal of wireless sensor network (WSN) to deal with real-world issues creates such network which is feasible and efficient to implement the applications such as monitoring, surveillance of man, machine, structures and natural phenomenon. Topological changes are inevitable due to dynamic nature of WSN. As network dynamics changes, all functional and non-functional operations of wireless sensor network are affected. Traditional approaches used in other networks are incapable of responding and learning dynamically. In current scenario, WSN is integrated with recent technologies like Internet of things and cyberphysical systems to facilitate scalability for providing common services. It is imperative that wireless sensor networks are energy-efficient, self-configurable and can operate independently with minimum human intervention. In order to instil these properties, recently a lot of work has been done to explore machine learning algorithms to tackle with issues and challenges of WSN. In this paper, a basic introduction to machine learning algorithms and their application to various domains of wireless sensor networks are covered.

The rapid expansion of Internet of Things (IoT) networks has introduced new cybersecurity risks, particularly due to their heterogeneous, dynamic, and resource-constrained nature. Intrusion Detection Systems (IDS) are commonly used to detect cyber threats in such environments, but traditional IDSs and many machine learning (ML) or deep learning (DL)-based methods face challenges. These include poor detection of unknown attacks, dependence on large datasets, static feature learning, and an inability to adapt to evolving traffic patterns without frequent retraining. Furthermore, while graph-based IDSs capture relational context, their high computational cost and difficulty in adapting to dynamic IoT networks hinder their scalability. To address these challenges, we propose LNNIDS , a novel IDS method that integrates spike encoding with a Hybrid Liquid Neural Network (HLLN) to capture the temporal evolution of IoT attack network traffic. To further enhance accuracy, we introduce DYNGraph , a dynamic graph-based method that performs n → 1 pattern clustering for scalable attack classification. This allows the LNNIDS to dynamically group related flow behaviors and adapt to new or unknown attacks without retraining. We evaluated LNNIDS on two public IoT datasets. This method achieved 99.50% accuracy and maintained robust performance with only 20% training data. Furthermore, we compared the LNNIDS with the seven recent state-of-the-art methods, and we found that LNNIDS outperformed them in the detection of known and unknown IoT attacks with 13.45% and 11.99% better accuracy, respectively.

The proliferation of dynamic network environments and Internet of Things (IoT) devices has transformed digital ecosystems while simultaneously exposing them to heightened cybersecurity threats.The complexity and interconnected nature of modern networks create vulnerabilities that cyber adversaries exploit through sophisticated techniques such as Distributed Denial of Service (DDoS) attacks, ransomware, and unauthorized access.This necessitates advanced, scalable, and adaptive security solutions to safeguard critical systems and data.This paper explores the transformative potential of Artificial Intelligence (AI) in enhancing cybersecurity across dynamic network environments and IoT ecosystems.AI-driven solutions, including machine learning algorithms, natural language processing (NLP), and predictive analytics, are analysed for their ability to detect anomalies, respond to threats in real time, and anticipate potential vulnerabilities.Special emphasis is placed on AI's role in automating threat detection, streamlining incident response, and bolstering network resilience.The discussion highlights the integration of AI with IoT, focusing on its ability to secure device communication, detect unauthorized behaviour, and ensure data integrity across decentralized systems.Case studies of successful implementations underscore AI's impact on preventing breaches, reducing response times, and mitigating risks.Challenges such as algorithmic biases, data privacy concerns, and the need for robust ethical frameworks are also addressed, alongside actionable recommendations for overcoming these hurdles.By examining emerging AI-driven cybersecurity technologies and their applications, this study provides insights into creating resilient and adaptive security frameworks.The findings emphasize the importance of collaboration among enterprises, policymakers, and technology providers to harness AI's full potential in mitigating evolving cyber threats.