Network Analysis Of Data Research Articles

Exploring Blockchain Data Analysis and Its Communications Architecture: Achievements, Challenges, and Future Directions: A Review Article

Blockchain technology is relatively young but has the potential to disrupt several industries. Since the emergence of Bitcoin, also known as Blockchain 1.0, there has been significant interest in this technology. The introduction of Ethereum, or Blockchain 2.0, has expanded the types of data that can be stored on blockchain networks. The increasing popularity of blockchain technology has given rise to new challenges, such as user privacy and illicit financial activities, but has also facilitated technical advancements. Blockchain technology utilizes cryptographic hashes of user input to record transactions. The public availability of blockchain data presents a unique opportunity for academics to analyze it and gain a better understanding of the challenges in blockchain communications. Researchers have never had access to such an opportunity before. Therefore, it is crucial to highlight the research problems, accomplishments, and potential trends and challenges in blockchain network data analysis and communications. This article aims to examine and summarize the field of blockchain data analysis and communications. The review encompasses the fundamental data types, analytical techniques, architecture, and operations related to blockchain networks. Seven research challenges are addressed: entity recognition, privacy, risk analysis, network visualization, network structure, market impact, and transaction pattern recognition. The latter half of this section discusses future research directions, opportunities, and challenges based on previous research limitations.

Research on the Impact and Optimization of Industrial Structure Adjustment on Marketing Strategy of Financial Institutions in the Digital Economy Era

The role of marketing strategy in the industrial structure adjustment and development of financial institutions in the era of the digital economy has changed marketing strategy and made the industrial structure adjustment of financial institutions in the era of the digital economy a hot spot. However, in the process of changing the marketing strategy of financial institutions, there are problems such as poor marketing concepts, weak online sales capabilities, network data analysis, and small mining volume. The main reason is that the traditional marketing model does not use information management technology to restrict the development of financial institutions. Therefore, this paper proposes a marketing method based on information management technology to plan the marketing strategies of financial institutions with different networks. First, phyton software is used to collect financial institutions' marketing data from the Guoan Tai database in 2018~2022, and according to the collected data of 33 financial institutions for analysis. Then, the judgment of marketing strategy is based on information management technology to improve the accuracy of financial institutions' selection of marketing strategy. The adjustment results show that under the background of the digital economy, information technology can improve the marketing strategy selection of financial institutions, promote the optimization of marketing strategies of financial institutions, and respond to the requirements of the financial market. Information technology can improve the accuracy and effect of marketing strategy selection of financial institutions, making its accuracy greater than 90%, which is significantly better than 80% of the manual identification method. At the same time, information technology can improve the recognition rate of marketing strategy, which is more than 80%, and provide support for the formulation of marketing strategy. Therefore, under the condition of a digital economy, industrial structure has a significant impact on the marketing strategy of financial institutions, while information technology methods can identify strategic indicators, improve the effect and accuracy of strategy formulation, and promote the development of financial institutions.

Analysis of Social Network Data Mining for Security Intelligence Privacy Machine Learning

The Modern communication on the Internet platform is most responsive through social media. Social media has changed and is still reshaping how we share our thoughts and emotions in communication. It has introduced a constant real-time communication pattern that was before unheard of. Young and old, organizations, governmental agencies, professional associations, etc., all have social media accounts that they use exclusively for communication with other users. Social media also acts as a powerful network engine that connects users regardless of where they are in the world. The development of global communication will greatly benefit from the availability of this new communication platform in the future. Consequently, there is a pressing need to research usage trends. Therefore, it is vital to investigate social media platform usage trends in order to develop automated systems that intelligence services can use to help avert national security incidents. Through the use of social media data mining, this research study suggests an automated machine learning model that can improve speedy response to crises involving national and International security.

Specificity of polygenic signatures across symptom dimensions in bipolar disorder: an analysis of UK Bipolar Disorder Research Network data

Current definitions and clinical heterogeneity in bipolar disorder are major concerns as they obstruct aetiological research and impede drug development. Therefore, stratification of bipolar disorder is a high priority. To inform stratification, our analysis aimed to examine the patterns and relationships between polygenic liability for bipolar disorder, major depressive disorder (MDD), and schizophrenia with multidimensional symptom representations of bipolar disorder. In this analysis, data from the UK Bipolar Disorder Research Network (BDRN) were assessed with the Operational Checklist for Psychotic Disorders. Individuals with bipolar disorder as defined in DSM-IV, of European ancestry (self-reported), aged 18 years or older at time of interview, living in the UK, and registered with the BDRN were eligible for inclusion. Psychopathological variables obtained via interview by trained research psychologists or psychiatrists and psychiatric case notes were used to identify statistically distinct symptom dimensions, calibrated with exploratory factor analysis and validated with confirmatory factor analysis (CFA). CFA was extended to include three polygenic risk scores (PRSs) indexing liability for bipolar disorder, MDD, and schizophrenia in a multiple indicator multiple cause (MIMIC) structural equation model to estimate PRS relationships with symptom dimensions. Of 4198 individuals potentially eligible for inclusion, 4148 (2804 [67·6%] female individuals and 1344 [32·4%] male individuals) with a mean age at interview of 45 years (SD 12·03) were available for analysis. Three reliable dimensions (mania, depression, and psychosis) were identified. The MIMIC model fitted the data well (root mean square error of approximation 0·021, 90% CI 0·019-0·023; comparative fit index 0·99) and suggests statistically distinct symptom dimensions also have distinct polygenic profiles. The PRS for MDD was strongly associated with the depression dimension (standardised β 0·125, 95% CI 0·080-0·171) and the PRS for schizophrenia was strongly associated with the psychosis dimension (0·108, 0·082-0·175). For the mania dimension, the PRS for bipolar disorder was weakly associated (0·050, 0·002-0·097). Our findings support the hypothesis that genetic heterogeneity underpins clinical heterogeneity, suggesting that different symptom dimensions within bipolar disorder have partly distinct causes. Furthermore, our results suggest that a specific symptom dimension has a similar cause regardless of the primary psychiatric diagnosis, supporting the use of symptom dimensions in precision psychiatry. Wellcome Trust and UK Medical Research Council.

Cellular QoE Prediction for Video Service Based on Causal Structure Learning

With the development of telecommunication technology and the popularity of intelligent devices, user experience in cellular network has become the primary factor of concern. At the same time, network operators are plagued by two problems: Prediction of users real-time experience and find network parameters that have a decisive impact on user experience. We proposed a novel scheme for user experience prediction to deal with these two problems. Causal structure learning for cellular networks was used to analyze numerous performance indicators (KPIs) collected from base stations and key quality indicators (KQIs). Through causal structure learning, a directed causal graph based on the association between KPIs and KQI can be obtained. This causal structure can be embedded in graph attention network. Among them, attention mechanism was selected to further strengthen the correlation between parameters. This correlation between each KPI and between KPIs and KQI was used to predict future value of cell level user experience. Results showed that proposed method performance well in cellular network data analysis and user experience prediction.

Big data and data analytics in 5G mobile networks

In this paper, we study the features of big data and data analytics. We see how Big Data contributes to mobile networks. We give a term in which big data generally refers to a large amount of digital data. Also, we estimate that the amount processed by "Big Data" systems will double every two years. Hence, Big Data on mobile networks need to be analyzed in-depth according to retrieve exciting and useful information. Big Data provides unprecedented opportunities for internet service providers to understand the behavior and requirements of their users, which in turn enables real-time decision making across a wide range of applications. After that, we mention the dimensions often describe the 4Vs of Big Data. We continue with the study about the use of big data analytics in mobile networks. As we see, new technologies for managing big data in a highly scalable, cost-effective, and damage-resistant manner are required. So, beyond 2020 the system capacity and data rates in mobile networks must support thousands of times more traffic than 2010 levels. Furthermore, we mention the end-to-end latency, the massive number of connections, the cost, the Quality of Experience, the Issues, and finally, the big data management. We continue with the study about the big data analytics in 5G. The 5G networks standardizing and the 5G mobile optimization are crucial areas. There are new research areas were exploring new analytics techniques in big data according to discover new patterns and extract knowledge from the data are collected. Big data analytics can provide organizations with the ability to profile and segment customers based on distinct socioeconomic characteristics and increase customer satisfaction and retention levels. Also, Big Data analytics techniques can provide telecom providers with in-depth knowledge of networks before making informed decisions. Also, as we see, these analytics techniques can help Telecommunication providers to monitor and analyze various types of data as well as event messages on networks. Important information, like business intelligence, can be extracted from momentary and stored data. Hence, the mass adoption of smartphones, mobile broadband modems, tablets, and mobile data applications has been overwhelmingly wireless. Operators bend under the pressure and cost of continuously adding capacity and improving coverage while maximizing the use of the existing components of their range. Advanced radio access technologies, and all Internet Protocols, open internet network architectures must evolve smoothly from 4G systems. So those needs are leading us to make a study about the heterogeneous network or else HetNet for 5G networks. We are continuing with the challenges, and we mention about the curse of modularity, dimensions procedure, feature engineering, non-linearity, Bonferonni's principle, category report, variance and bias, data locality, data heterogeneity, noisy data, data availability, real-time processing, and streaming, data provenance, and data security.

Devising a method for detecting “evil twin” attacks on IEEE 802.11 networks (Wi-Fi) with KNN classification model

The object of research is IEEE 802.11 (Wi-Fi) networks, which are often the targets of a group of attacks called "evil twin". Research into this area is extremely important because Wi-Fi technology is a very common method of connecting to a network and is usually the first target of cybercriminals when they attack businesses. With the help of a systematic analysis of the literature focused on countering attacks of the "evil twin" type, this work identifies the main advantages of using artificial intelligence systems in the analysis of network data and identification of intrusions in Wi-Fi networks. To evaluate the effectiveness of intrusion detection and cybercrime analysis, a number of experiments as close as possible to real attacks on Wi-Fi networks were conducted. As part of the research reported in this paper, a method is proposed for detecting cybercrimes in IEEE 802.11 (Wi-Fi) wireless networks using artificial intelligence, namely a model built on the basis of the k-nearest neighbors method. This method is based on the classification of previously collected data, namely the signal strength from the access point, and then continuous comparison of the newly collected data with the trained model. A compact and energy-efficient prototype of a hardware and software system has been designed for the implementation of monitoring, analysis of ethernet network packets and data storage based on time series. In order to reduce the load on the computer network and taking into account the limited computing power of the system, a method of data aggregation was proposed, which ensures fast transfer of information. The results, namely 100 % of test cases (more than 7 thousand), were classified correctly, which indicates that the chosen method of data analysis will significantly increase the security of information and communication systems at the state and private levels

Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection

In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance. This study delves into the underexplored potential of fusing graph theory with machine learning models to elevate their anomaly detection capabilities in the context of industrial Internet of things (IIoT) network data analysis. We employed a comprehensive experimental approach, encompassing data preprocessing, visualization, feature analysis, and machine learning model comparison, to assess the efficacy of graph theory representation in improving classification accuracy. More specifically, we converted network traffic data into a graph-based representation, where nodes represent devices and edges represent communication instances. We then incorporated these graph features into our machine learning models. Our findings reveal that incorporating graph theory into the analysis of network data results in a modest-yet-meaningful improvement in the performance of the tested machine learning models, including logistic regression, support vector machines, and K-means clustering. These results underscore the significance of graph theory representation in bolstering the discriminative capabilities of machine learning algorithms when applied to network data.

CLUSTER-BASED GRID COMPUTING ON WIRELESS NETWORK DATA TRANSMISSION WITH ROUTING ANALYSIS PROTOCOL AND DEEP LEARNING

Grid computing based on clusters has emerged as a promising strategy for improving the efficacy of wireless network data transmission. This study examines the incorporation of cluster-based grid computing, routing analysis protocols, and deep learning techniques to optimize data transmission in wireless networks. The proposed method utilizes clusters to distribute computing duties and enhance resource utilization, resulting in efficient data transmission. To further improve the routing process, a novel routing analysis protocol is introduced, which dynamically adapts to network conditions and chooses the most optimal routes. In addition, deep learning algorithms are used to analyze network data patterns, allowing for intelligent data routing and resource allocation decisions. Experiment results exhibit the efficacy of the proposed method, revealing substantial enhancements in network performance metrics such as throughput, latency, and energy consumption. This research contributes to the development of cluster-based grid computing and offers valuable insights for the design of efficient wireless network data transmission systems.

A Big Data Analytical Framework for Intrusion Detection Based On Novel Elephant Herding Optimized Finite Dirichlet Mixture Models

For the purpose of identifying a wide variety of hostile activity in cyberspace, an Intrusion Detection System (IDS) is a crucial instrument. However, traditional IDSs have limitations in detecting zero-day attacks, which can lead to high false alarm rates. To address this issue, it is crucial to integrate the monitoring and analysis of network data with decision-making methods that can identify anomalous events accurately. By combining these approaches, organizations can develop more effective cybersecurity measures and better protect their networks from cyber threats. In this study, we proposed a novel called the Elephant Herding Optimized Finite Dirichlet Mixture Model (EHO-FDMM). This framework consists of three modules: capture and logging, pre-processing, and an innovative IDS method based on the EHO-FDMM. The NSL-KDD and UNSW-NB15 datasets are used to assess this framework's performance. The empirical findings show that selecting the optimum model that accurately fits the network data is aided by statistical analysis of the data. The EHO-FDMM-based intrusion detection method also offers a lower False Alarm Rate (FPR) and greater Detection Rate (DR) than the other three strong methods. The EHO-FDMM and exact interval of confidence bounds were used to create the suggested method's ability to detect even minute variations between legal and attack routes. These methods are based on correlations and proximity measurements, which are ineffective against contemporary assaults that imitate everyday actions.

Social and asocial learning in zebrafish are encoded by a shared brain network that is differentially modulated by local activation

Group living animals use social and asocial cues to predict the presence of reward or punishment in the environment through associative learning. The degree to which social and asocial learning share the same mechanisms is still a matter of debate. We have used a classical conditioning paradigm in zebrafish, in which a social (fish image) or an asocial (circle image) conditioned stimulus (CS) have been paired with an unconditioned stimulus (US=food), and we have used the expression of the immediate early gene c-fos to map the neural circuits associated with each learning type. Our results show that the learning performance is similar to social and asocial CSs. However, the brain regions activated in each learning type are distinct and a community analysis of brain network data reveals segregated functional submodules, which seem to be associated with different cognitive functions involved in the learning tasks. These results suggest that, despite localized differences in brain activity between social and asocial learning, they share a common learning module and social learning also recruits a specific social stimulus integration module. Therefore, our results support the occurrence of a common general-purpose learning module, that is differentially modulated by localized activation in social and asocial learning.

Meta-path infomax joint structure enhancement for multiplex network representation learning

Network representation learning has achieved significant success in homogeneous network data analysis in recent years. Nevertheless, they cannot be directly applied in multiplex networks. To overcome the characteristic of heterogeneity in multiplex networks, several emerging methods utilize the concept of meta-path to denote different types of relations and obtain the node representations for each type of meta-path individually. Despite the remarkable progress, there still exist two important issues in the previous approaches. First, the complementary information between different types of meta-paths that may make the representations more discriminative, is rarely investigated. Second, current studies generally learn multiplex node representations based on the original graph structure, while overlooking the latent relations between nodes. To address the aforementioned issues, in this paper, we propose a novel model with Meta-path Infomax joint Structure Enhancement (MISE) for multiplex network representations. Specifically, we first develop a meta-path infomax mechanism, which maximizes the mutual information between local and global meta-path representations, making the node representation contain more complementary information. Additionally, we propose a graph structure learning module that captures the implicit correlations between nodes to construct the latent graph structure. Such structure enhancement is a simple yet surprisingly effective technique to learn high-quality representations. We sufficiently evaluate the performance of our proposal on both supervised and unsupervised downstream tasks. Comprehensive experimental results show that our MISE achieves a promising boost in performance on a variety of real-world datasets for multiplex network representation learning.

Detection of HTTP DDoS Attacks Using NFStream and TensorFlow

This paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine learning-based approach to detect the aforementioned attacks, by exploiting the machine learning capabilities of TensorFlow. We also focused on the collection and analysis of network traffic data using nfstream, which provides a detailed analysis of network traffic flows. We pre-processed and transformed the collected data into vectors, which were used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow is effective in detecting HTTP attacks. The machine learning model achieved high accuracy on the tested dataset, demonstrating its ability to correctly identify HTTP attacks while minimizing false positives.

Predicting a clinically narrow pelvis using neural network data analysis

Aim: to improve the efficiency of predicting a clinically narrow pelvis (СNP) using neural network data analysis and to evaluate its prognostic characteristics.Materials and Мethods. The study was designed as a retrospective non-randomized clinical trial. An analysis of 184 born neonates was carried out: group 1 included 135 female patients whose delivery occurred through the natural birth canal, group 2 – 49 patients whose delivery was complicated by СNP development and ended up with emergency caesarean section. Examination of patients was carried out on the eve of childbirth (1–2 days) and included anamnesis, general and special obstetric examination, including pelvimetry, a clinical assessment of cephalopelvic disproportion was carried out during childbirth. The condition of newborns was assessed using the Apgar scale, height and body weight were measured. Neural network analysis was performed using the built-in Neural Networks module of SPSS Statistics Version 25.0 (IBM, USA).Results. Despite hypothetically important role of anatomically narrowed pelvis in development of cephalopelvic disproportion, no significant inter-group differences were found. Significant parameters (abdominal circumference, uterine fundus height and woman’s weight, fetal head circumference, as well as data on the presence or absence of oligohydramnios and fetal macrosomia) were determined, which were included in the test database to create the basis for training the multilayer perceptron. Out of 135 patients of group 1, the prognosis was negative in 131 (97.0 %), positive in 4 (3.0 %); out of 49 patients in group 2, negative in 0 (0.0 %), positive in 49 (100.0 %). The forecast accuracy of the developed model was 98 % (sensitivity – 100 %, specificity –97 %). The information content of neural network data analysis in СNP predicting is presented in ROC analysis: area under the curve (AUC) = 0.99 (95 % confidence interval = 0.97–1.00). Neonatal anthropometric parameters were significantly higher in group 2 vs. group 1, and the Apgar score at 1 minute was correspondingly lower.Conclusion. The use of neural network analysis of clinical data obtained on the eve of childbirth allows to predict СNP development at sufficient degree of accuracy (98.0 %), which, in the future, after being introduced into clinical practice, will optimize a choice of delivery method in patients at risk (anatomically narrow pelvis, large fetus), reduce emergency caesarean sections and improve birth outcomes.

Exploring and Analyzing Network Data with Python

Esta lição introduz métricas de rede e como tirar conclusões das mesmas quando se trabalha com dados de Humanidades. O leitor aprenderá como usar o pacote NetworkX do Python para produzir e trabalhar com estas estatísticas de rede.

Editorial: Ontology-based Knowledge Presentation and Computational Linguistics for Semantic Big Social Data Analytics in Asian Social Networks

Data-driven ontology-based knowledge (OK) presentation and computational linguistics for evolving semantic Asian social networks (ASNs) can make one of the most important platforms that provide robust and real-time data mapping in massive access across the heterogeneous big data sources in the web that is named OK-ASN. It benefits from computational intelligence, web-of-things (WoT) architecture, semantic features, statistical learning and pattern recognition, database management, computer vision, cyber-security, and language processing. OK-ASN is a critical strategy for WoT big data mining and enterprises from social media to medical and industrial sectors.

Investigating the Relative Influence of Romantic Sex Partners and Close Friends on Adolescent Alcohol and Marijuana Use.

A large body of research has identified peer exposure as a key factor driving adolescent substance use. However, findings on the role of sex partners are less robust and mixed. This study aims to fill this gap by examining the independent contribution of close friends' and sex partners' alcohol and marijuana use on adolescents' use of these substances. A secondary data analysis of social network data collected in 2000-2002 from a household sample of African American youth (14-19years old) in the Bayview and Hunter's Point neighborhoods of San Francisco was conducted. Index participants and their nominated close friends and romantic sex partners (N = 104 triads) self-reported recent alcohol and marijuana use (defined as any use in the past 3months). Generalized estimated equations were used to estimate the independent association between adolescent's recent substance use and their friend's and sex partner's use. Adolescents with a marijuana-using romantic sex partner had a nearly six-fold higher odds of using marijuana compared to adolescents with a non-using partner, controlling for close friend's marijuana use and other confounders [OR:5.69, 95%CI: 1.94, 16.7]; no association with close friend's marijuana use was found. A similar pattern was observed for alcohol use. Adolescents with an alcohol-using romantic sex partner had increased odds of using alcohol compared to adolescents with a non-using partner, controlling for close friend's alcohol use and other confounders [OR:2.40, 95%CI: 1.02, 5.63]; no association with close friend's alcohol use was found. Romantic sex partners may play a unique and significant role in adolescent substance use. Peer-focused interventions may be more effective if they consider romantic sex partners. Future research should consider the role of romantic sex partners in changing social context related to substance use from adolescence to young adulthood.

Characteristics of Individuals Seen as Sources of Social Support in Populations at Increased Risk for HIV.

Access to social support from one's social network can serve as a protective factor against HIV infection; however, research exploring the availability of support in diverse populations that include high proportions of people at increased risk for HIV and the characteristics of network members associated with access to such support is limited. Multi-level dyadic analyses of social network data collected from women at risk for HIV and their network members reveal which individual and relationship characteristics of network members are associated with providing emotional, material, and/or health informational support. Results indicate that access to all three types of support was associated with a network member being a friend, a member of a participant's 'core' group, someone whose opinion matters to the respondent, and the respondent trusting them. These findings have implications for interventions designed to increase access to support among individuals at risk for HIV.

Abstract 1116: Identifying neuroendocrine prostate cancer drivers using in vivo forward genetics

Abstract Prostate cancer (PC) is the malignancy with the highest incidence worldwide in males. Although localized disease can be effectively treated and usually has a favorable prognosis, progression to metastasis results in high mortality rates as current treatment modalities are not yet curative. The standard of care for advanced PC is hormonal therapy, termed Androgen Deprivation Therapy (ADT). Unfortunately, ADT typically elicits the emergence of a lethal phenotype, termed Castration-Resistant Prostate Cancer (CRPC), which is treatment-resistant and frequently gives rise to a highly aggressive variant with features of neuroendocrine (NE) differentiation, known as Neuroendocrine PC (NEPC). There is no cure for NEPC, thus identifying drivers that lead to its emergence is of paramount importance. We used the Sleeping Beauty (SB) transposon system to perform a forward genetic screening on Genetically-Engineered Mouse Models (GEMMs) of CRPC that are known to develop NEPC under ADT pressure. SB transposon activation generated tumors with an accelerated growth phenotype and increased metastasis incidence compared to non-activated controls. Notably, several tumors presented histological features of NEPC. We sequenced SB transposon integration sites on tumors’ genomes to identify recurrent patterns suggesting genes responsible for phenotype acceleration and NEPC differentiation. Recently, we generated and assembled a set of >100 GEMM-derived tumors, that recapitulate a broad spectrum of PC disease states, to reverse-engineer a network of regulatory proteins. We used this network on RNA-Seq data collected from SB-activated tumors to identify Master Regulator (MR) proteins that are specifically associated with NEPC emergence. Cross-species network analysis of patient-specific MRs activity conservation between patient and our mouse cohorts, aligned NEPC patients to mice tumors with NE features, corroborating our study's clinical relevance. We performed an integrative network analysis that links genomic events as generated by SB transposon integrations to downstream regulatory programs implemented by MR proteins. This analysis prioritized Sirt1 as upstream modulator of NEPC MRs activity. Activation of SIRT1 in human adenocarcinoma PC cells induced NE-like phenotype, while SIRT1 inhibitor in NEPC cells reduced tumor aggressiveness. In summary, we used in vivo models of prostate cancer to elucidate molecular drivers of NEPC, a lethal variant that is treatment-resistant. Integrative network data analysis prioritized Sirt1 as driver of NEPC emergence and was validated in vivo. Citation Format: Alessandro Vasciaveo, Francisca Nunes De Almeida, Min Zou, Matteo Di Bernardo, Andrea Califano, Cory Abate-Shen. Identifying neuroendocrine prostate cancer drivers using in vivo forward genetics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1116.

GPU-based, interactive exploration of large spatiotemporal climate networks.

This paper introduces the Graphics Processing Unit (GPU)-based tool Geo-Temporal eXplorer (GTX), integrating a set of highly interactive techniques for visual analytics of large geo-referenced complex networks from the climate research domain. The visual exploration of these networks faces a multitude of challenges related to the geo-reference and the size of these networks with up to several million edges and the manifold types of such networks. In this paper, solutions for the interactive visual analysis for several distinct types of large complex networks will be discussed, in particular, time-dependent, multi-scale, and multi-layered ensemble networks. Custom-tailored for climate researchers, the GTX tool supports heterogeneous tasks based on interactive, GPU-based solutions for on-the-fly large network data processing, analysis, and visualization. These solutions are illustrated for two use cases: multi-scale climatic process and climate infection risk networks. This tool helps one to reduce the complexity of the highly interrelated climate information and unveils hidden and temporal links in the climate system, not available using standard and linear tools (such as empirical orthogonal function analysis).