Discovering NFT Rug Pulls: Matching Behavior Patterns Using Graph Isomorphism Networks

The explosive growth of non-fungible tokens (NFTs) on Web3 has created a new frontier for digital art and collectibles and an emerging space for fraudulent activities. This study provides an in-depth analysis of NFT rug pulls, the fraudulent schemes that steal investors’ funds. From a curated dataset of 760 rug pulls across 10 NFT marketplaces, we examine these schemes’ structural and behavioral properties, identify the characteristics and motivations of rug pullers, and classify NFT projects into 20 groups based on creators’ association with their accounts. Our findings reveal that repeated rug pulls account for a significant proportion of the rise in NFT-related cryptocurrency crimes, with one NFT creator attempting 37 rug pulls within 3 months. Additionally, we identify the largest group of creators influencing the majority of rug pulls and demonstrate the connection between rug pullers of different NFT projects using the same wallets to store and move money. Our study contributes to understanding NFT market risks and provides insights for designing preventative strategies to mitigate future losses.

The rise of Non-Fungible Tokens (NFTs) has transformed the landscape of digital asset ownership, introducing new platforms for content creation and consumption. This research delves into the changing content dynamics within NFT marketplaces, focusing on the evolution of digital asset characteristics and consumer behavior. By studying established categories such as visual arts, music, and collectibles, we analyze shifts in demand patterns over time. Furthermore, we explore the significant impact of celebrities and social media influencers on specific NFT projects and broader market trends. While their involvement can generate interest, we critically assess potential drawbacks, including the susceptibility of hype-driven markets to fraudulent activities. Through data analysis, our goal is to shed light on the dynamic nature of NFT content, offering insights into its status and future direction.

PurposeThe purpose of this paper is to test if and how interactive thematic maps can encourage the formation of tourists' perceptual maps of attractions in a destination.Design/methodology/approachData were derived from 365 online trip planners generated by users of the Amish Country web site from 1 August 2007 to 31 July 2008. Given the nature of the data set, network analysis techniques were used to analyze the data.FindingsNetwork analysis findings demonstrate that the things to do (TTD) included in the interactive Heritage Trail map were more frequently bundled together in the saved trip plans than TTDs included in other sections of the web site. Further analysis suggests that some additional non‐Heritage TTDs could be included in the Heritage Trail map due to their perceptual proximity.Practical implicationsThe findings provide evidence that interactive maps on web sites can play an important role in tourists' planning behavior, which has significant implications for online tourism marketing. The findings also provide insights as to how online behavioral data can be used to derive marketing intelligence.Originality/valueThis study contributes to the theoretical development of the role of interactive maps in travel planning and also illustrates the usefulness of network analysis for investigating tourism‐related issues.

Remiantis atliktų tarptautinių finansinių tinklų teorinių bei empirinių tyrimų analizės rezultatais, analizuojami tarptautinio finansinio tinklo identifikavimo ypatumai. Analizuojama metodika pritaikyta tarptautinio portfelinių investicijų tinklo atveju.

Advances in exponential random graph ( p*) models applied to a large social network

With rapid growth of digital transaction online payment systems have become increasingly vulnerable to fraudulent activities. This paper present novel deep learning based framework for detecting online payment fraud using hybrid architecture that combines Bidirectional Encoder Representations from Transformers (BERT) based feature extraction with a Transformer Enhanced Convolutional Neural Network (TECNN). The model developed using real world dataset from Kaggle which include user profile data, transaction history, metadata such as IP addresses, geolocation. The preprocessing pipeline involve normalization of numerical attribute, encoding of categorical data, and imputation of missing values along with feature engineering to derive temporal behavioral pattern. Textual metadata is processed through fine tuned BERT model to generate contextual embedding which help to enhance the representational power of the feature set. These embeddings along with numerical and categorical feature are passed through convolutional layers to detect local patterns and transformer module to capture long-term dependencies across transaction sequences. The model leverage multi-head attention to prioritize anomalous feature followed by fully connected layers for binary classification into fraudulent and authentic transactions. The aim of integrated approach to improve robustness, accuracy of fraud detection systems while addressing challenges like data imbalance, feature correlation, concept drift in dynamic financial environment. Preliminary results shows potential for significant performance gain over traditional models, establishing foundation for secure, intelligent payment processing in modern e-commerce systems.

Purpose The study aims to examine the YouTube videos of Malaysia and Indonesia’s education systems to determine what connections exist among curriculum, teacher, accessibility and equity. Design/methodology/approach Data included YouTube videos about the education system in Malaysia and Indonesia. The most influential YouTube videos about the education systems in Malaysia and Indonesia were first identified through social network analysis. Four videos with the highest betweenness centrality were selected from 100 videos related to the Malaysian education system, whereas the other four videos were selected from 95 videos related to the Indonesian education system, respectively. Transcripts of these videos were qualitatively coded through thematic analysis to study the Malaysian and Indonesian education systems. Epistemic network analysis was then applied to identify the strength of relationships between nodes that emerged from the inductive thematic analysis of the selected YouTube video transcriptions. Findings Epistemic network analysis revealed that the network model in Malaysia had the greatest connection between equity and curriculum. The network model in Indonesian demonstrated the greatest connection between curriculum and teacher. Originality/value The article will contribute to the knowledge about the issues related to Malaysia and Indonesia’s education systems by analyzing the most transcriptions from the most influential YouTube videos and identifying the connections among curriculum, teacher, accessibility and equity.

The goal of network analysis is to focus on relationships between social entities. It is used widely in the social and behavioral sciences, as well as in political science, economics, psychology andorganizational science. The social network approach has a long history and it has been developed over the last 60 years by researchers in psychology, sociology and anthropology. Nowadays, using high-speed computers, network analysis is mostly used for graphical presentation of relationships and dependencies; moreover, networks comprise graphical representations of the relationships (edges) between variables (nodes). Network analysis provides the capacity to estimate complex patterns of relationships and the network structure can be analyzed to reveal core features of the network. Recent developments however, stress the usefulness of network-based approaches for measurement models in social sciences, where CFA and IRT are are complemented by network approaches. In this paper we provide an overview of networks of European economic universities, and present and compare the results of classical Item Response Theory (Birnbaum model) and Latent Network Models (Ising and Residual Network Model) for measurement of networking ability among Polish economic universities. All calculations are conducted using R software. The results of IRT model shows that the highest significant difficulty parameters (that can be regarded as network “attractiveness”) are characteristic for CEEMAN, CESEENET and MAGNACARTA networks, whereas the lowest but insignificant parameters are related to ATLAS, PRME and NICE. Because of lack of evidence of unidimenstionality of common factor IRT model and lot of insignificant parameters, the Latent Network Model and Residual Network Model were used. The application of LNM and RNM revealed three-dimensional structure of latent networks. First cluster consists with relatively attractive networks (EUA, CESEENET, MAGNACARTA, NICE, CEEMAN), second cluster represents the average attractiveness (CEMS, EDAMBA, PIM) and the third is based on networks with lowest difficulty parameters (EFMD, PRME, ATLAS).

The present study investigated the relations between maternal depressive symptoms and internalizing problems in offspring during late childhood and early adolescence, examining sex differences using symptom network analysis. A total of 885 Chinese youths in late childhood (n = 497, 38.6% girls; age = 9.58 years, SD = 0.24) and early adolescence (n = 388, 48.5% girls; age = 11.30 years, SD = 0.24) and their mothers (Mage = 37.34 years, SD = 5.42) were recruited. Cross-lagged panel network (CLPN) analysis was used to explore bridge symptoms (i.e., symptoms connecting two or more mental disorders) and identify transmission pathways between maternal depressive symptoms and offspring's internalizing problems at these two developmental stages. The CLPN results revealed that in late childhood, the bridge connections in the network model were boys feeling worried to mothers feeling distractible, and girls feeling worried to mothers feeling powerless. In early adolescence, the bridge connections were boys experiencing depressed mood to mothers feeling powerless, and mothers feeling bad to girls experiencing depressed mood. These findings highlight the network-level relations between maternal depressive symptoms and offspring internalizing problems. They provide insights into the developmental differences and similarities in symptoms during these periods and suggest ways to break the vicious cycle of psychopathology between mothers and their children.

BackgroundAnxiety and sleep problems are common comorbidities among outpatients living in high-altitude areas. Network analysis is a novel method to investigate the interaction and the association between symptoms across diverse disorders. This study used network analysis to investigate the network structure symptoms of anxiety and sleep problems among outpatients in high-altitude areas, and to explore the differences in symptom associations in various sex, age, educational levels and employment groups.MethodsThe data was collected from the Sleep Medicine Center of The First People’s Hospital of Yunnan Province from November 2017 to January 2021 with consecutive recruitment (N = 11,194). Anxiety and sleep problems were measured by the Chinese version of the seven-item Generalized Anxiety Disorder Scale (GAD-7) and the Pittsburgh Sleep Quality Index (PSQI) respectively. Central symptoms were identified based on centrality indices and bridge symptoms were identified with bridge indices. The difference of network structures in various sex, age, educational levels and employment groups were also explored.ResultsAmong all the cases, 6,534 (58.37%; 95% CI: 57.45-59.29%) reported experiencing anxiety (GAD-7 total scores ≥ 5), and 7,718 (68.94%; 95% CI: 68.08-69.80%) reported experiencing sleep problems (PSQI total scores ≥ 10). Based on the results of network analysis, among participants, “Nervousness”, “Trouble relaxing”, “Uncontrollable worry” were the most critical central symptoms and bridge symptoms within the anxiety and sleep problems network structure. The adjusted network model after controlling for covariates was significantly correlated with the original (r = 0.75, P = 0.46). Additionally, there were significant differences in edge weights in the comparisons between sex, age and educational levels groups (P < 0.001), while the employed and unemployed groups did not show significant differences in edge weights (P > 0.05).ConclusionsIn the anxiety and sleep problems network model, among outpatients living in high-altitude areas, nervousness, uncontrollable worry, and trouble relaxing were the most central symptoms and bridge symptoms. Moreover, there were significant differences between various sex, age and educational levels. These findings can be used to provide clinical suggestions for psychological interventions and measures targeting to reduce symptoms that exacerbate mental health.

The article examines the relationship between marketing and financial and economic security in the context of the development of information technologies, as their convergence is an integral part of modern business. Digital transformations significantly change the landscape of marketing and financial and economic security, in order to work successfully in new conditions, enterprises must invest in digital technologies, protect customer data and systems from cyber attacks and fraudulent actions, constantly adapt to new technologies and market conditions. In today's conditions, economy, rationality and maximum efficiency based on the use of modern information and multimedia technologies are a guarantee not only of quality management, but also of the company's overall competitiveness in the markets. Marketing plays a key role in ensuring the financial and economic security of the enterprise, i.e. constant monitoring of the market, analysis of competitors and study of customer needs allow timely detection of potential threats and development of effective strategies to neutralize them. Thanks to a deep understanding of customer needs and market dynamics, marketing contributes to the development of effective strategies that allow the enterprise to adapt to changes and maintain its competitiveness, optimize its costs, improve product quality and strengthen its position in the market. In the conditions of growing global competition, companies must understand the development trends of international markets, cultural characteristics and take into account legislative norms in their activities. In this case, marketing helps to adapt business to new conditions, and economic security provides protection from external threats. The rapid development of digital technologies is changing the rules of the game in the market, while marketing allows effective use of digital channels to interact with customers, and economic security provides protection against cyber threats and other risks related to digital technologies. Consumers increasingly expect a personalized approach and high quality products and services, so marketing helps to understand the needs of customers and develop appropriate offers, and financial and economic security ensures that these offers will be fulfilled with quality and time. New players are constantly entering the market, which complicates competition, so there is a need to stand out among competitors, determine the company's competitive advantages, build a positive image and effectively manage crisis situations. In this case, economic security protects against unfair competition, ensures business stability in conditions of uncertainty, helps protect company data from cyber attacks and fraudulent actions, and prevents financial losses. Therefore, ensuring financial and economic security during marketing operations is a complex task that requires a complex approach. The convergence of marketing and financial and economic security allows you to minimize risks, ensure sustainable business development and effective promotion of goods and services, and protect the reputation of the company and its assets. The interaction of marketing and economic security helps prevent crisis situations related to security breaches that can negatively affect the company's image, as well as avoid ineffective spending on marketing campaigns related to unsafe or unethical practices

A phenomenal growth in the number of credit card transactions, especially for on-line purchases, has also led to a substantial rise in fraudulent activities. Implementation of efficient fraud detection systems has thus become imperative for all credit card companies in order to minimize their losses. In real life, fraudulent transactions could be interspersed with genuine transactions and simple pattern matching techniques are not often sufficient to detect the fraudulent transactions efficiently. In this paper, we propose a hybrid approach in which anomaly detection and misuse detection models are combined. Sequence alignment is used to determine similarity of an incoming sequence of transactions to both a genuine card holder's sequence as well as to sequences generated by a validated fraud model. The scores from these two stages are combined to determine if a transaction is genuine or not. We use stochastic models for studying the performance of the system.

Pregnancy loss represents a significant public health concern within the realm of female reproductive health. In the current landscape of research, post-traumatic stress disorder (PTSD), anxiety, and depression are often investigated as an undifferentiated whole. However, this approach overlooks the unique symptomatic relationships and nuances that may exist among these mental health conditions in women who have endured pregnancy loss. This study aimed to dissect the symptomatic interconnections among PTSD, anxiety, and depression in women who have experienced pregnancy loss. A cross-sectional study was carried out at a tertiary hospital in Zhengzhou, China. Women with pregnancy loss were surveyed using a general information questionnaire, the Posttraumatic Stress Disorder Checklist-Civilian Version, and the Hospital Anxiety and Depression Scale. Network analysis was performed using R software to examine pairwise associations among symptoms, identify core symptoms (i.e., key nodes with the strongest connectivity within the network), and detect bridge symptoms (i.e., symptoms that link post-traumatic stress, anxiety, and depressive symptom clusters). We also evaluated the accuracy and stability of the estimated network structure. Of the 691 women with pregnancy loss included in the analyses, PTSD entry 6 (Try to avoid thoughts and feelings that remind you of it) and PTSD entry 7 (Make an effort to avoid conversations, people, places and activities that remind you of the matter) had the strongest association, with a regularized partial correlation coefficient of 0.485. PTSD entry 7 was the most central symptom in the network, with a centrality strength of 1.748. Entry 5 (Muscle tension, shortness of breath, rapid heartbeat, dry mouth, and stomach cramps) had the strongest bridging effect. The accuracy and stability of the network was tested to be better, and the network model was more reliable. Through the innovative application of network analysis, this study successfully explored the associations between PTSD items and symptoms of anxiety and depression in women with pregnancy loss. By identifying the core and bridging symptoms, it provides valuable insights into the underlying mechanisms of these co-occurring mental health conditions.

This study uses network analysis to examine how self-differentiation sub-factors relate to marital satisfaction among 400 married adults in South Korea, a society undergoing rapid cultural transition. A network model was estimated to map the relationships between differentiation components (e.g., Emotional Reactivity, I-Position, Fusion) and marital satisfaction items. The analysis identified Emotional Reactivity and Emotional Fusion as the most central drivers of marital dissatisfaction. In contrast, a rigid "I-Position" (self-assertion) was negatively associated with marital satisfaction, suggesting its role is complex within a cultural context that prioritizes relational harmony. This may reflect "reactive individualism" against traditional suppression. The findings highlight a dynamic interplay between emotional regulation and culturally shaped self-expression, demonstrating network analysis's utility for developing targeted, culturally sensitive clinical interventions that promote a balanced sense of self.

Testosterone has complex effects on psychological traits and behavior; it is associated with social dominance and competition and is a potential human sex pheromone. This study aimed to investigate the associations between testosterone levels, aggressive behavior, and manic symptoms using a network analysis among bipolar disorder (BD) patients in psychiatric emergency departments (PED). Data from January 2021 and March 2022 BD patients in PED were analyzed. Manic symptoms were assessed using the Young Mania Rating Scale (YMRS). Aggression was assessed with subscale of the PANSS scale (PANSS-AG). The undirected network structures of testosterone levels, aggressive behavior, and manic symptoms were estimated, and centrality and bridge centrality indices were examined. Network stability was examined using the case-dropping procedure. The Network Comparison Test (NCT) was conducted to evaluate whether network characteristics differed by gender. We recruited a total of 898 BD patients, with the mean YMRS score as 13.30 ± 9.58. The prevalence of level II aggression was 35.6% (95%CI = 32.5%–38.7%), level III aggression was 29.5% (95%CI = 26.3%–32.6%), and level VI aggression was 7.0% (95%CI = 5.4%–8.8%). The male participants had a mean testosterone level of 391.71 (Standard Deviation (SD):223.39) compared to 36.90 (SD:30.50) for female participants in the whole sample. Through network analysis, “Increased motor activity-energy” emerged as the central symptom, with the highest centrality expected influence, followed by “Emotional Instability” and “Disruptive/aggression behavior”. Notably, “Emotional Instability” appeared to be the bridge symptom linking manic symptoms to aggressive behavior. Within the flow network model, “Speech rate and amount” exhibited the strongest positive correlation with testosterone levels, followed closely by “Disruptive/aggression behavior”. The constructed network model demonstrated robust stability, with gender showing no significant impact on the structure. In this study, “Increased motor activity-energy” stood out as the most influential symptom, and “Speech rate and amount” acted as the main bridge symptom linking testosterone levels, aggressive behavior, and manic symptoms. Targeting the central and bridge symptoms may improve the outcomes of aggression interventions implemented among BD patients in psychiatric emergency care.