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.

Temporal Heterogeneous Networks (THNs) are evolving networks that characterize many real-world applications such as citation and events networks, recommender systems, and knowledge graphs. Forecasting THNs involves predicting future connections within a network that evolves over time and comprises diverse types of nodes and interactions with varying temporal dynamics. Although some Graph Neural Networks (GNNs) models have been successfully applied to forecast THNs, there is a lack of a general overview of how the message-passing computation could be extended to treat THNs. Moreover, most of the current solutions exhibit pitfalls in their training and evaluation strategies. Hence, in this work, we propose a graph deep learning framework for THN forecasting. Our framework decomposes the computation of a GNN layer into multiple components and introduces two different schemes to update embedding representations for THNs. This design allows the classification of existing solutions into special instances of our framework and highlights their potential limitations. We also extend the set of benchmarks for THNs by introducing two novel high-resolution temporal heterogeneous graph datasets derived from an emerging Web3 platform and a well-established e-commerce website. Overall, we conducted the first massive evaluation of THNs solutions over four temporal heterogeneous network datasets on two different future link prediction tasks using a fair newly introduced evaluation setting that considers the evolving nature of the data. Based on the limitations of existing solutions, we develop a new model that combines working techniques from previous models and leverages a new embedding update scheme. Experiments show the prediction power of our model compared to current solutions for link prediction in temporal graphs. Moreover, the experimental evaluation highlights the strengths and weaknesses of the different solutions and shows the effectiveness of our framework design.

The problem of link prediction in temporal knowledge graphs (TKGs) consists of finding missing links in the knowledge base under temporal constraints.Recently, [4] and [8] proposed a solution to the problem inspired by the canonical decomposition of 4-order tensors, where they regularise the representations of time steps by learning similar transformation for adjacent timestamps.However, the impact of the choice of temporal regularisation terms is still poorly understood.In this work, we systematically analyse several choices of temporal regularisers using linear functions and recurrent architectures.In our experiments, we show that by carefully selecting the temporal regulariser and regularisation weight, a simple method like TNTComplEx [4] can produce comparable results with state-of-the-art methods and enhance its original performance.Specifically, we observe that linear regularisers for temporal smoothing based on specific nuclear norms can significantly improve the predictive accuracy of the base temporal link prediction methods.

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.

In many scientific fields, complex systems are characterized by a multitude of heterogeneous interactions/relationships that are challenging to model. Multilayer graphs constitute valuable tools that can represent such complex systems, thus making possible their analysis for downstream decision-making processes. Nevertheless, modeling such complex information still remains challenging in real-world scenarios. On the one hand, holistically including all relationships may lead to noisy or computationally intensive graphs. On the other hand, limiting the amount of information to model through the selection of a portion of the available relationships can introduce boundary specification biases. However, the current research studies are demonstrating that it is more beneficial to retain as much information as possible and at a later stage perform graph simplification i.e., removing uninformative or redundant parts of the graph to facilitate the final analysis. While simplification strategies, based on deep learning methods, have been already extensively explored in the context of single-layer graphs, only a limited amount of efforts have been devoted to simplification strategies for multilayer graphs. In this work, we propose the MultilAyer gRaph simplificAtion ( MARA ) framework, a GNN-based approach designed to simplify multilayer graphs based on the downstream task. MARA generates node embeddings for a specific task by training jointly two main components: (i) an edge simplification module and (ii) a (multilayer) graph neural network. We tested MARA on different real-world multilayer graphs for node classification tasks. Experimental results show the effectiveness of the proposed approach: MARA reduces the dimension of the input graph while keeping and even improving the performance of node classification tasks in different domains and across graphs characterized by different structures. Moreover, deep learning-based simplification allows MARA to preserve and enhance important graph properties for the downstream task. To our knowledge, MARA represents the first simplification framework especially tailored for multilayer graphs analysis.

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.

Abstract Link prediction in Online Social Networks—OSNs—has been the focus of numerous studies in the machine learning community. A successful machine learning-based solution for this task needs to (i) leverage global and local properties of the graph structure surrounding links; (ii) leverage the content produced by OSN users; and (iii) allow their representations to change over time, as thousands of new links between users and new content like textual posts, comments, images and videos are created/uploaded every month. Current works have successfully leveraged the structural information but only a few have also taken into account the textual content and/or the dynamicity of network structure and node attributes. In this paper, we propose a methodology based on temporal graph neural networks to handle the challenges described above. To understand the impact of textual content on this task, we provide a novel pipeline to include textual information alongside the structural one with the usage of BERT language models, dense preprocessing layers, and an effective post-processing decoder. We conducted the evaluation on a novel dataset gathered from an emerging blockchain-based online social network, using a live-update setting that takes into account the evolving nature of data and models. The dataset serves as a useful testing ground for link prediction evaluation because it provides high-resolution temporal information on link creation and textual content, characteristics hard to find in current benchmark datasets. Our results show that temporal graph learning is a promising solution for dynamic link prediction with text. Indeed, combining textual features and dynamic Graph Neural Networks—GNNs—leads to the best performances over time. On average, the textual content can enhance the performance of a dynamic GNN by 3.1% and, as the collection of documents increases in size over time, help even models that do not consider the structural information of the network.

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.

The effort to reach the 17 Sustainable Development Goals by the United Nations has incentivized the adoption of IT solutions in many fields. Many systems for sustainable economic development are now relying on a digital form making them more accessible and provides the access to new functionalities. A very interesting example of such systems are complementary currencies i.e. cooperative currency systems that support national economies to provide humanitarian aid and promote sustainable development. While there are many studies on the principles and case studies of successful complementary currencies, many aspects are still unexplored, especially regarding cooperative behavior. Cooperative behavior in these systems is a key aspect, as complementary currencies are often born out of cooperation among members that face a period of crisis or they usually have the objective of creating bonds of reciprocity and integrating social networks between people, which should lead to increased cooperation. However, there is a lack of studies on many aspects of cooperative behavior in complementary currencies, such as how such behavior changes over time, especially in times of a crisis like the COVID-19 pandemic. Moreover how cooperation behavior is affected by time and different geographical locations is still unclear. In this work, we analyze Sarafu, a complementary currency that went digital and now relies on blockchain technology. Sarafu is a successful case of a complementary currency that was used for humanitarian aid during the COVID-19 pandemic. Moreover, Sarafu is a perfect case study for the study of cooperative behavior, as it implements a special type of account, the group account, to support cooperation groups. This feature supports the study of group dynamics and behavior. What we find is that Sarafu users exhibit strong reliance on cooperation groups; we also observe that the interaction of users and cooperation groups is influenced by both time and geographical location. The study of group accounts and in general mechanisms that promote cooperation can be useful for other humanitarian or community development projects. Moreover, similar cooperation enhancers could have an important role in other social development projects, and in general, in any setting where there is a strong need to foster cooperation for reaching social good.

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.

We present an approach to the discovery and characterization of relevant locations and related mobility patterns in symbolic trajectories built on call detail records - CDRs - of mobile phones (telco trajectories). While the discovery of relevant locations has been widely investigated for continuous spatial trajectories (e.g., stay points detection methods), it is not clear how to deal with the problem when the movement is defined over a discrete space and the locations are symbolic, noisy and irregularly sampled, such as in telco trajectories. In this paper, we propose a methodological approach structured in two steps, called trajectory summarization and summary trajectories analysis, respectively, the former for removing noise and irrelevant locations; the latter to synthesize key mobility features in a few novel indicators. We evaluate the methodology over a dataset of approx 17,000 trajectories with 55 million points and spanning a period of 67 days. We find that trajectory summarization does not compromise data utility, while significantly reducing data size. Moreover, the mobility indicators provide novel insights into human mobility behavior.

A well-known result in social science states that, although people maintain a large number of social relationships, in practice, their interactions are concentrated mostly on a small portion of their neighbors. According to Dunbar's hypothesis, this phenomenon is due to a limited human cognitive capacity to handle too many social relationships, together with a few time for socializing. These constraints result in an organization of people's ego-network into four or five groups depending on the strength of the interactions, the so called Dunbar's circles. The verification of the Dunbar's hypothesis, the identification of social circles and their characterization are still open questions, although researchers have found evidence of its validity on different social networks, from small offline networks to online social networks, such as Twitter and Facebook. In addition, little is known about the semantic aspects of the circles, i.e. who are the members of each circle. In this paper, we cope with these issues by analyzing a mobile phone graph where people's interactions are expressed by both voice calls and text messages. We firstly compare two methods for the identification of the circles, which rely on the different definitions of strength proposed in literature. Both methods confirm the subdivision of ego-networks into four or five circles, each characterized by a specific interaction strength. Then, we validate the Dunbar's hypothesis and, by leveraging some powerful features of the mobile phone dataset in use, we provide a first semantic characterization of social circles. We show, for instance, that people maintain relationships with the members of the closest circles by combining calls and texts. In addition, by detecting home and work locations of each individual, we highlight that a semantic aspect, such as the role of the alters, impacts on the ego-network circles. In particular, family members and workmates are located in different circles: the former mainly form the closest circle, i.e. the support clique, while the latter are distributed among the outer circles.

The dichotomy between two opposite propensities, exploration and exploitation, characterizes and drives many human behaviors, from decision making to social learning. Recently, this dichotomy has been found also in human mobility where people can be divided into two basic types: returners, those who are very regular in their daily mobility; and explorers, those who are inclined to break out of their daily mobility routine and explore new places. While the former attitude has been widely studied in literature and results in the well-known tendency to frequently visit a few locations (e.g. home and workplace), the latter trait remains an unexplored aspect and deserves further investigation. In this work we focus on the characterization of the places that an individual visits when she is driven by her propensity for exploration, i.e. non-routine places which are outside her usual daily mobility patterns. To this end, we mine an anonymized mobile phone dataset which integrates call, text and data activities of about one million subscribers in Milan, to detect and characterize the non-routine places. Moreover, we complement it with Foursquare venues along with their category to semantically characterize the reasons driving the choice of the places to explore. The analysis of the non-routine places and the mobility patterns during the exploration phase brings to light some interesting findings: i) to a greater or lesser extent, all individuals are explorers since they visit a significant number of non-routine places; ii) due to the exceptionality of a visit, non-routine places are farther from home and workplace than frequently visited places; iii) we are explorers in our leisure time; iv) we get to a non-routine place leaving our home, then we return home later; and v) in Milan, shopping, in particular at fashion and clothing stores, is the main interest behind the need to explore non-routine places.

Within a 5G mobile network scenario, this paper adopts Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) technological frameworks to evaluate the gain in terms of user Quality of Experience (QoE) that can be achieved when contents and server of a video delivery service move from the cloud at the edge of the Radio Access Network. The application scenario we set up envisions a video streaming service geared a small group of individuals who have strong social ties and who move in an urban space. The scaling up of this emergent type of social interaction is set to become in the near future highly demanding for the resources of mobile operators. The paper recreates the network infrastructure of a mobile operator in the city of Milano. We model network conditions through Mininet, and manage (computing, network and storage) resources by means of an orchestrator named OpenVolcano. The paper shows that, compared to the traditional cloud approach, a MEC one provides better QoE to group members simply taking advantage both of the proximity and distribution of mini data centers and of proper resource orchestration.

The city's social structure is mainly based on the activities and behavior of small groups of people whose cohesion is preserved by their tight social relations. The interactions that people have among themselves and with the places/services of the city ensure strength and duration to these social ties. Among the many media at disposal today to carry on social activities, on-phone and off-line interactions are those mainly involved in the creation of the strongest social links. In this paper we leverage mobile data to detect groups of people with strong ties and to identify the urban places where they are used to gather. We apply this methodology to the city of Milan, by means of a large anonymized dataset of Call Detail Records (CDRs) collecting phone activities (calls, texts, and Internet traffic) of about 1 million mobile users. The paper shows that mobile phone data enables to detect socially cohesive groups gathering in city's places.

The rising attention to the spreading of fake news and unsubstantiated rumors on online social media and the pivotal role played by confirmation bias led researchers to investigate different aspects of the phenomenon. Experimental evidence shows that confirmatory information gets accepted even if containing deliberately false claims, while dissenting information is mainly ignored or might even increase group polarization. It seems reasonable that, to address misinformation problem properly, we have to understand the main determinants behind content consumption and the emergence of narratives on online social media. In this paper we address such a challenge by focusing on the discussion around the Italian Constitutional Referendum by conducting a quantitative, cross-platform analysis on both Facebook public pages and Twitter accounts. We observe the spontaneous emergence of well-separated communities on both platforms. Such a segregation is completely spontaneous, since no contents categorization was performed a priori. By exploring the dynamics behind the discussion, we find that users tend to restrict their attention to a specific set of Facebook pages/Twitter accounts. Finally, taking advantage of automatic topic extraction and sentiment analysis techniques, we are able to identify the most controversial topics inside and across both platforms. Thus, we measure the distance between how a certain topic is presented in the posts/tweets and the related users' emotional response. Our results provide interesting insights for the understanding of the evolution of the core narratives behind different echo chambers and for the early detection of massive viral phenomena around false claims.

This paper presents a simple policy to immediately reduce the overall core network's interpersonal voice/data traffic of nearly 15% on average in highly populated urban areas. This remarkable result can be achieved without resorting to complementary radio technologies, but by adding a few basic functionalities that simply rely on enabling the mobile operator to be aware of the fact that people are interacting by mobile phone in proximity. As a first contribution of this paper we give empirical evidence of the role played by these short-range social interactions through an extensive analysis of the large anonymized datasets of Call Detail Records (CDR) of two different mobile operators. Then, we describe a NFV/SDN-based approach that a mobile operator should adopt to detect whether a voice/text activity is directed to a nearby person, to negotiate with adjacent cells the establishment of the communication channel and to divert the traffic over it to offload the core network.

Unlike virtual sociality, in their daily social behavior individuals are used to communicate with a limited number of persons and periodically meet their inner social circle in specific city locations to perform common social activities. Physical encounters among a restricted number of people interestingly give rise to a significant amount of in-proximity voice/data traffic on the cellular network and advocate the provisioning of a new class of services supporting it. This paper gives empirical evidence of the role played by these location-centered social interactions through the extensive analysis of a large anonymized dataset of Call Detail Records (CDR) relying on the phone activities of nearly 1 million people in the city of Milano. The analysis and understanding of these human interactions have inspired the design of a new mobile service that detects, after user's consent, proximity with a person in my inner social circle and autonomously deploys the mobile social network supporting proximity interactions. The approach we propose brings together a few important contributions: first, it concretely shows that the current NFV-enabled trend of placing cloud services at the edge of the operator's network has a payoff in terms of traffic offloading and improved user's experience; secondly, it demonstrates for the first time that a few typical cloud-based services can actually be directly performed by the mobile network operator by simply leveraging the rich amount of data they possess and never exploit.

The unprecedented growth in mobile data usage is posing significant challenges to cellular operators. One key challenge is how to provide quality of service to subscribers when their residing cell is experiencing a significant amount of traffic, i.e. becoming a traffic hotspot. In this paper, we perform an empirical study on data hotspots in today's cellular networks using a 9-week cellular dataset with 734K+ users and 5327 cell sites. Our analysis examines in details static and dynamic characteristics, predictability, and causes of data hotspots, and their correlation with call hotspots. We believe the understanding of these key issues will lead to more efficient and responsive resource management and thus better QoS provision in cellular networks. To the best of our knowledge, our work is the first to characterize in detail traffic hotspots in today's cellular networks using real data.

In this paper we focus on mobile data offloading and propose a solution for the placement of offloading infrastructures that optimizes the trade-off between reducing deployed resources and increasing traffic breakout. We base our analysis on a real dataset of Internet accesses generated, in the city of Milano, by some 50,000 users of an important mobile network operator. The target application we consider is the distribution of digital contents (such as MP3 songs, videos and newspapers) over an urban area. The paper's contributions is showing that offloading of digital contents can be achieved by leveraging people's regular mobility patterns.

ABSTRACT The concepts of location and community are rapidly becoming key points in the design of new communication paradigms and in deploying emerging mobile computing services. The need of reliable and quantitative knowledge and predictions of some relevant information, such as which locations are enjoyed by people in their daily lives and how people aggregate within communities, advocates a realistic mobility model able to describe both the human mobility throughout locations and the human attitude to socialize within communities. Unfortunately, so far, neither the concept of location nor the concept of community has been univocally defined. In this paper, we approach the problem from the most basic of starting points, namely by analyzing the real Global Positioning System datasets of human mobility traces. On this elementary basis, the paper provides a few relevant contributions. We firstly derive a deep understanding of the term “location” and at the same time of the notion of community strictly related to it. Secondly, we merge the two concepts into what we call geo‐community. By proceeding from real spatial data rather than from a priori reasonings, we are able to quantitatively describe geo‐communities and infer the probability distributions of all the features of human behavior. Finally, not to lose social implications, we present the method to derive people sociality from geo‐communities. Copyright © 2012 John Wiley & Sons, Ltd.

Data confidentiality policies at major social network providers have severely limited researchers' access to large-scale datasets. The biggest impact has been on the study of network dynamics, where researchers have studied citation graphs and content-sharing networks, but few have analyzed detailed dynamics in the massive social networks that dominate the web today. In this paper, we present results of analyzing detailed dynamics in a large Chinese social network, covering a period of 2 years when the network grew from its first user to 19 million users and 199 million edges. Rather than validate a single model of network dynamics, we analyze dynamics at different granularities (per-user, per-community, and network-wide) to determine how much, if any, users are influenced by dynamics processes at different scales. We observe independent predictable processes at each level, and find that the growth of communities has moderate and sustained impact on users. In contrast, we find that significant events such as network merge events have a strong but short-lived impact on users, and they are quickly eclipsed by the continuous arrival of new users.

The study of complex networks led to the belief that the connectivity of network nodes generally follows a Power-law distribution. In this work, we show that modeling large-scale online social networks using a Power-law distribution produces significant fitting errors. We propose the use of a more accurate node degree distribution model based on the Pareto-Lognormal distribution. Using large datasets gathered from Facebook, we show that the Power-law curve produces a significant over-estimation of the number of high degree nodes, leading researchers to erroneous designs for a number of social applications and systems, including shortest-path prediction, community detection, and influence maximization. We provide a formal proof of the error reduction using the Pareto-Lognormal distribution, which we envision will have strong implications on the correctness of social systems and applications.

We propose a synthetic trace generator that, although based on a simple mobility model, generates traces with statistical properties (like inter-contact time and contact duration) resembling those of well-known real traces.The proposed model is based on a waypoint scheme, with some modifications: the introduction of two categories of nodes, steady and nomadic, with different mobility rates; the grouping of nodes in communities sharing the same location preferences, and the definition of a micro-mobility model inside each location.The statistical properties of the traces generated with this model are compared to those obtained from publicly available real traces.