The rapid growth of Non-Fungible Tokens (NFTs) and the extensive trading activities associated with such an intriguing domain led to the emergence of large-scale and interconnected transaction networks involving the most prominent NFT markets. Despite such interdependencies representing an inestimable source of information for the proper understanding of the NFT landscape, previous studies treated each market separately, overlooking relevant phenomena. In this study, we explore a multilayer network modeling approach to analyze transactions in multiple NFT markets. We reveal previously unnoticed macroscopic and mesoscopic traits by investigating indicators that discern whether markets are independent or linked: users trading NFTs are organized in cross-market communities where multi-market users act as bridges across marketplaces, adapting to the diverse nature of the markets they operate in. We also conduct an in-depth examination of such multi-market users, studying their specific activity patterns that leave a distinctive mark on the system: the majority of multi-market users well differentiate their earnings and expenses among the markets, while a fraction of them is directed toward a more polarized money allocation based on the typology of the markets. By offering a fresh perspective on this intricate financial system and emphasizing the importance of perceiving the NFT markets as a unique and interconnected world, our study paves the way for further contributions aimed at unraveling the complexity of cryptosystems and understanding the latent phenomena across NFT markets.

Social media platforms shape climate action discourse. Mapping these online conversations is essential for effective communication strategies. TikTok’s climate discussions are particularly relevant given its young, climate-concerned audience. In this work, we collect the first TikTok dataset on climate topics. We collected 590K videos from 14K creators along with their follower networks. By applying topic modeling to the video descriptions, we map the topics discussed on the platform on a climate taxonomy that we construct by consolidating existing categorizations. Results show TikTok creators primarily approach climate through the angle of lifestyle and dietary choices. By examining semantic connections between topics, we identified non-climate ”gateway” topics that could draw new audiences into climate discussions.

User migration (i.e., the movement of large sets of users from one online social platform to another one) is one of the main phenomena occurring in modern online social networks and even involves the most recent alternative paradigms of online social networks, such as blockchain-based online social networks. In these platforms, user migration mainly occurs through hard forks of the supporting blockchain (i.e., a split of the original blockchain and the creation of an alternative blockchain), to which users may decide to migrate. However, our understanding of user migration and its mechanisms is still limited, particularly regarding the role of densely connected user groups (communities) during migration and fork events. Are there differences between users who stay and those who decide to leave, in terms of network structure and discussion topics? In this work, we show, through network-based analysis centered on the identification of communities on multilayer networks and text mining that (a) the “position” of a group within the network of social and economic interactions is connected to the likelihood of a group to migrate (i.e., marginal groups are more likely to leave); (b) group network structure is also important, as users in densely connected groups interacting through monetary transactions are more likely to stay; (c) users who leave are characterized by different discussion topics; and (d) user groups interacting through monetary transactions show interest in migration-related content if they are going to leave. These findings highlight the importance of social and economic relationships between users during a user migration caused by fork events In general, in the larger context of online social media, it motivates the need to investigate user migration through a network-inspired approach based on groups and specific subgraphs while leveraging user-generated content, at the same time.

Online social platforms for digital communication necessitate an in-depth understanding of their evolving dynamics, especially after the renewal requests brought about by new paradigms, such as Web3. The dynamics within online social networks (OSNs) are influenced by numerous factors, encompassing user behavior, content generation, platform features, and technological advancements, with triadic closure standing out as a prominent and influential element. In this study, we focus on the temporal aspects of triadic closure and its role in the evolution of OSNs, especially after the advent of the Web3 paradigm. By analyzing networks with timestamped links from diverse platforms based on different architectures, including communication, Web3-based, and trade networks, we developed a comprehensive analytical pipeline to support the study of triadic closure patterns. This pipeline includes an algorithm for the census of time-ordered triads, a vector-based model for representing growing networks (growth triadic profile), the identification of triadic closure rules (TERs), and the evaluation of the speed of the formation of closed triads. Our findings reveal significant variations in the impact of triadic closure across different OSNs, marked by diverse growth triadic profiles and varying formation speeds of closed triads as well as diversity in the predictability of evolutionary patterns based on triads. This study not only enhances the comprehension of triadic closure in the temporal evolution of OSNs but also provides valuable insights to be taken into account for the design and administration of online social platforms.

Understanding and extracting knowledge from temporal networks is crucial to understand their dynamic nature and gain insights into their evolutionary characteristics Existing approaches to network growth often rely on single-parameterized mechanisms, neglecting the diverse and heterogeneous behaviors observed in contemporary techno-social networks. To overcome this limitation, methods based on graph evolution rules (GER) mining have proven promising GERs capture interpretable patterns describing the transformation of a small subgraph into a new subgraph, providing valuable insights into evolutionary behaviors However, current approaches primarily focus on estimating subgraph frequency, neglecting the evaluation of rule significance. To address this gap, we propose a tailored null model integrated into the GERM algorithm, the first and most stable graph evolution rule mining method. Our null model preserves the graph’s static structure while shuffling timestamps, maintaining temporal distribution, and introducing randomness to event sequences By employing a z-score test, we identify statistically significant rules deviating from the null model We evaluate our methodology on three temporal networks representing co-authorship and mutual online message exchanges Our results demonstrate that the introduction of the null model affects the evaluation and interpretation of identified rules, revealing the prevalence of under-represented rules and suggesting that temporal factors and other mechanisms may impede or facilitate evolutionary paths. These findings provide deeper insights into the dynamics and mechanisms driving temporal networks, highlighting the importance of assessing the significance of the evolution patterns in understanding network evolution.

The current online social network landscape is characterized by competition to get larger audiences leading to massive user migrations which will determine the shape of the future Web. However, user migration phenomena have not been fully understood and their driving mechanisms are still not well identified; in particular, the behaviors of hubs and the influence they exert on their followers are unclear. In this work, we focus on these aspects by analyzing the propensity of hubs to migrate towards a new social platform as a consequence of a shocking event; and the influence they exert on the decision of their neighbors of migrating to a new platform or staying on the native one. We conducted analysis on data made available after a user migration consequence of a hard fork involving two Web3 online social networks based on the blockchains Steem and Hive. Due to the blockchain nature of these Web3 platforms, we got detailed data about social and financial interactions among the users, along with information that allowed a precise reconstruction of the context surrounding the migration. The main findings suggest that different types of hubs apply different strategies when choosing to migrate, e.g. financial hubs diversify their strategy by staying and migrating at the same time. As for hub influence, results suggest that users directly interacting with hubs tend to migrate. In general, findings on influence indicate that understanding the activity and the influence of hubs is crucial in monitoring and controlling the user migration process.

Web3, one of the novel paradigms which may drive the evolution of the future Web, is offering an invaluable volume of data stored in the supporting blockchains. Researchers from different fields such as network science, computational social science and data mining, might benefit from these large collections of temporal and heterogeneous data capturing different kinds of interaction among people and between people and the platforms. In this study we focus on a specific issue related to these modern techno-social systems, i.e. the understanding of the rules driving their growth. To reach this goal, we performed an analysis based on graph evolution rules - GERs - on different networks gathered from Web3 platforms such as Steemit or OpenSea. Graph evolution rules mining is a frequency-based method for evaluating network evolution which does not require any prior growth process for disentangling how networks evolve. By comparing the evolution rules of social network platforms and asset trading services through GER profiles, we observe that some evolution rules are common to all Web3 platforms, regardless of the system specificity. On the other hand, in specific cases, the frequency of graph evolution rules is influenced by the nature of the platform: whereas social and token-transfer networks are characterized by rules which increase network transitivity and reciprocity, NFT trading networks, especially those specialized in a specific type of digital asset, are driven by rules which form trading chains. These findings suggest that the GER approach and the GER profiles are a good starting point to get insights into the evolutionary behavior of a network and to define a classification of graph evolution rules.

Customer segmentation is a central problem in different business processes. In the last few years, it is also becoming important for banking and financial institutions given the ever-growing volume of cashless payments. When dealing with customer segmentation with transactional data, the clustering approach is widely used. In this work, we propose a different modeling approach for customer segmentation based on a graph-based representation. Specifically, we reformulate customer segmentation as a community detection problem on a similarity multi-layer network, where each layer depends on a specific cashless payment method. We introduce a vector-based representation of the cardholders' spending patterns, namely the purchase profile, to build the similarity multi-layer network. The profiles capture how customers allocate their spending capacity among merchant categories through different payment systems. From purchase profiles, we evaluate the similarity of the cardholders in terms of consumption allocation and we infer different similarity graphs based on credit and debit card payments. Different segmentation strategies based on multi-layer community detection methods have been evaluated on a large-scale dataset of credit and debit card transactions of a banking group. Since one of the main goals is verifying the feasibility of graph-based approaches for customer segmentation, we discuss the outcomes of the methods in terms of explainability of the resulting segments. Specifically, methods based on random walks, such as Infomap, return more stable and insightful results than modularity-based ones, in different settings. To sum up, we experiment with community detection algorithms to cope with the customer segmentation problem starting from a large set of credit and debit card transactions. The outcome of the solutions may support recently developed methods for bank risk assessment based on clients' behavior or targeted applications for cashless payment management.