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A strategy to assist visualization and analysis of large and complex datasets is dimensionality reduction, with which one maps each data point into a low-dimensional manifold. However, various dimensionality reduction techniques are computationally infeasible for large data. Out-of-sample techniques aim to resolve this difficulty; they only apply the dimensionality reduction technique on a small portion of data, referred to as landmarks, and determine the embedding coordinates of the other points using landmarks as references. Out-of-sample techniques have been applied to online settings, or when data arrive as time series. However, existing online out-of-sample techniques use either all the previous data points as landmarks or the fixed set of landmarks and therefore are potentially not good at capturing the geometry of the entire dataset when the time series is non-stationary. To address this problem, we propose an online landmark replacement algorithm for out-of-sample techniques using geometric graphs and the minimal dominating set on them. We mathematically analyse some properties of the proposed algorithm, particularly focusing on the case of landmark multi-dimensional scaling as the out-of-sample technique, and test its performance on synthetic and empirical time-series data. [ABSTRACT FROM AUTHOR]

Community detection is a valuable tool for studying the function and dynamic structure of most real-world networks. Existing techniques either concentrate on the network's topological structure or node properties without adequately addressing the dynamic aspect. As a result, in this research, we present a unique technique called Multi-Objective Optimization Overlapping Dynamic Community Detection (MOOODCD) that leverages both the topological structure and node attributes of dynamic networks. By incorporating the Dirichlet distribution to control network dynamics, we formulate dynamic community detection as a non-negative matrix factorization problem. The block coordinate ascent method is used to estimate the latent elements of the model. Our experiments on artificial and real networks indicate that MOOODCD detects overlapping communities in dynamic networks with acceptable precision and scalability. [ABSTRACT FROM AUTHOR]

In Web3 social platforms, i.e. social web applications that rely on blockchain technology to support their functionalities, interactions among users are usually multimodal, from common social interactions such as following, liking, or posting, to specific relations given by crypto-token transfers facilitated by the blockchain. In this dynamic and intertwined networked context, modeled as a financial network, our main goals are (i) to predict whether a pair of users will be involved in a financial transaction, i.e. the transaction prediction task, even using textual information produced by users, and (ii) to verify whether performances may be enhanced by textual content. To address the above issues, we compared current snapshot-based temporal graph learning methods and developed T3GNN, a solution based on state-of-the-art temporal graph neural networks' design, which integrates fine-tuned sentence embeddings and a simple yet effective graph-augmentation strategy for representing content, and historical negative sampling. We evaluated models in a Web3 context by leveraging a novel high-resolution temporal dataset, collected from one of the most used Web3 social platforms, which spans more than one year of financial interactions as well as published textual content. The experimental evaluation has shown that T3GNN consistently achieved the best performance over time and for most of the snapshots. Furthermore, through an extensive analysis of the performance of our model, we show that, despite the graph structure being crucial for making predictions, textual content contains useful information for forecasting transactions, highlighting an interplay between users' interests and economic relationships in Web3 platforms. Finally, the evaluation has also highlighted the importance of adopting sampling methods alternative to random negative sampling when dealing with prediction tasks on temporal networks. [ABSTRACT FROM AUTHOR]

Abstract In the new media environment, the constantly emerging social platforms further expand the channels for the propagation of public opinion. Under the framework of complex network theory and faced the needs of management practice, modeling the spreading dynamics of public opinion in temporal group networks is of great significance for understanding its spreading law and improving the governance system of cyberspace and the development of network science. Through analyzing the changes of group networks topology and the spreading rules of public opinion, the spreading model of public opinion in temporal group networks was proposed by coupling the two dynamic processes, and the spreading thresholds of public opinion in static and temporal group networks were derived respectively. Then, the spreading characteristics of public opinion under different network topology, as well as the influence of important parameters on public opinion spreading process were discussed with the help of simulation experiments. The research results indicated that the propagation of public opinion in static and temporal group networks exhibits both similar trends and differentiated characteristics; compared with Spreader, the propagation of public opinion in temporal group networks is more sensitive to Ignorant’s behavior; both groups’ and netizens’ active probability have significant influence on public opinion propagation, but netizens’ affects more. Based on the relevant results, this paper proposed a series of countermeasures such as, grading social platforms, strengthening relationship management between them and introducing time management systems, so as to promote the formation of a good network ecosystem and the modernization of the national governance system.

With the advancement of computational network science, its research scope has significantly expanded beyond static graphs to encompass more complex structures. The introduction of streaming, temporal, multilayer, and hypernetwork approaches has brought new possibilities and imposed additional requirements. For instance, by utilising these advancements, one can model structures such as social networks in a much more refined manner, which is particularly relevant in simulations of the spreading processes. Unfortunately, the pace of advancement is often too rapid for existing computational packages to keep up with the functionality updates. This results in a significant proliferation of tools used by researchers and, consequently, a lack of a universally accepted technological stack that would standardise experimental methods (as seen, e.g., in machine learning). This article addresses that issue by presenting an extended version of the Network Diffusion library. First, a survey of the existing approaches and toolkits for simulating spreading phenomena is shown, and then, an overview of the framework functionalities. Finally, we report four case studies conducted with the package to demonstrate its usefulness: the impact of sanitary measures on the spread of COVID-19, the comparison of information diffusion on two temporal network models, and the effectiveness of seed selection methods in the task of influence maximisation in multilayer networks. We conclude the paper with a critical assessment of the library and the outline of still awaiting challenges to standardise research environments in computational network science.

Lifelines are critical infrastructure systems characterized by a high level of interdependency that can lead to cascading failures after any disaster. Many approaches can be used to analyze infrastructural interdependencies, but they are usually not able to describe the sequence of events during emergencies. Therefore, interdependencies need to be modeled also taking into account the time effects. The methodology proposed in this paper is based on a modified version of the Input-output Inoperability Model and returns the probabilities of failure for each node of the system. Lifelines are modeled using graph theory, while perturbations, representing a natural or man-made disaster, are applied to the elements of the network following predetermined rules. The cascading effects among interdependent networks have been simulated using a spatial multilayer approach, while the use of an adjacency tensor allows to consider the temporal dimension and its effects. The method has been tested on a case study based on the 2011 Fukushima Dai-ichi nuclear disaster. Different configurations of the system have been analyzed and their probability of occurrence evaluated. Two models of the nuclear power plant have been developed to evaluate how different spatial scales and levels of detail affect the results.

Under the new media environment, social platforms, as the carrier of information propagation, have shown a drastic change in their form and structure, endowing public opinion with unique propagation characteristics. In view of this, considering the dynamic changes of online social network (OSN) structure, this article intends to analyse the spreading mechanism of public opinion in temporal networks and improve the applicability of public opinion governance strategies. Combing the changes of OSN topology with the classical susceptible–infected–recovered (SIR) dynamics model, we constructed a co-evolution model of temporal networks structure and public opinion propagation, and the propagation threshold of public opinion was derived with the help of Markov process. Then, the propagation characteristics of public opinion in temporal networks and their co-evolution process under different factors were discussed through simulation experiments. The results show that the propagation of public opinion in temporal networks has faster speed and wider scope compared with that in static networks; netizens' social activity has a phased impact on the evolution process of public opinion and with its significant heterogeneity, the propagation of public opinion is gradually suppressed; compared with Ignorant , the evolution process of public opinion in temporal networks is more sensitive to the state change of public opinion Spreader. Our research can further enrich the theoretical research system of network science and information science and also provide a certain decision-making reference for the regulators to reasonably govern the propagation of public opinion in social platforms. [ABSTRACT FROM AUTHOR]

The analysis of social and biological networks often involves modeling clusters of interest as cliques or their graph‐theoretic generalizations. The k$$ k $$‐club model, which relaxes the requirement of pairwise adjacency in a clique to length‐bounded paths inside the cluster, has been used to model cohesive subgroups in social networks and functional modules or complexes in biological networks. However, if the graphs are time‐varying, or if they change under different conditions, we may be interested in clusters that preserve their property over time or under changes in conditions. To model such clusters that are conserved in a collection of graphs, we consider a cross‐graphk$$ k $$‐club model, a subset of nodes that forms a k$$ k $$‐club in every graph in the collection. In this article, we consider the canonical optimization problem of finding a cross‐graph k$$ k $$‐club of maximum cardinality in a graph collection. We develop integer programming approaches to solve this problem. Specifically, we introduce strengthened formulations, valid inequalities, and branch‐and‐cut algorithms based on delayed constraint generation. The results of our computational study indicate the significant benefits of using the approaches we introduce. [ABSTRACT FROM AUTHOR]

The analysis of node order structure in dynamic temporal networks is significant for network propagation control. To further accurately characterize the inter-layer coupling relationship of dynamic temporal networks, this paper firstly defines the node neighborhood structure homogeneity rate and node neighborhood location heterogeneity rate based on the node neighborhood structure evolution feature information and node neighborhood location evolution feature information, and integrates the influence of the change in both neighborhood structure and neighborhood location on the node importance in the process of node temporal evolution. Secondly, a Supra-Adjacency Matrix based on Neighborhood Structure (NSAM) temporal network node importance order structure modeling method is proposed by combining the local structure and overall structure evolution information of nodes during the temporal evolution process. Finally, the node importance order structure of the dynamic temporal network is obtained by combining the eigenvector centrality to represent the node importance attribute value. Simulations show that compared with the classical hierarchical temporal network model, the NSAM model can improve the identification accuracy by 38.2% and 7.8%, respectively, and can identify the important nodes in the dynamic temporal network more effectively. [ABSTRACT FROM AUTHOR]

This study advances the field of infectious disease forecasting by introducing a novel approach to micro-level contact modeling, leveraging human movement patterns to generate realistic temporal-dynamic networks. Through the incorporation of human mobility models and parameter tuning, this research presents an innovative method for simulating micro-level encounters that closely mirror infection dynamics within confined spaces. Central to our methodology is the application of Bayesian optimization for parameter selection, which refines our models to emulate both the properties of real-world infection curves and the characteristics of network properties. Typically, large-scale epidemiological simulations overlook the specifics of human mobility within confined spaces or rely on overly simplistic models. By focusing on the distinct aspects of infection propagation within specific locations, our approach strengthens the realism of such pandemic simulations. The resulting models shed light on the role of spatial encounters in disease spread and improve the capability to forecast and respond to infectious disease outbreaks. This work not only contributes to the scientific understanding of micro-level transmission patterns but also offers a new perspective on temporal network generation for epidemiological modeling. [ABSTRACT FROM AUTHOR]

Smurfing in financial networks is a popular fraud technique in which fraudsters inject their illegal money into the legitimate financial system. This activity is performed within a short period of time, with recurring transactions and multiple intermediaries. A major problem of existing graph-based methods for detecting smurfing is that they fall short of retrieving accurate fraud patterns. Consequently, the result is numerous non-fraudulent patterns alongside a few fraud patterns, causing a high false-positive rate. To alleviate this problem, we propose SMoTeF, a framework that extends existing graph-based smurf detection methods by distinguishing fraudulent smurfing patterns from non-fraudulent ones, thus significantly reducing the false-positive ratio. The core of the approach is a novel algorithm based on computing maximum temporal flow within temporal order of events. In order to evaluate the approach, a framework for injecting various smurfing patterns is developed, and experimental results on three real-world datasets from different domains show that SMoTeF significantly improves on the effectiveness of the state-of-the-art baseline, with only marginal runtime overhead. [ABSTRACT FROM AUTHOR]

A popular approach to model interactions is to represent them as a network with nodes being the agents and the interactions being the edges. Interactions are often timestamped, which leads to having timestamped edges. Many real-world temporal networks have a recurrent or possibly cyclic behaviour. In this paper, our main interest is to model recurrent activity in such temporal networks. As a starting point we use stochastic block model, a popular choice for modelling static networks, where nodes are split into R groups. We extend the block model to temporal networks by modelling the edges with a Poisson process. We make the parameters of the process dependent on time by segmenting the time line into K segments. We require that only H ≤ K different set of parameters can be used. If H < K , then several, not necessarily consecutive, segments must share their parameters, modelling repeating behaviour. We propose two variants where a group membership of a node is fixed over the course of entire time line and group memberships are allowed to vary from segment to segment. We prove that searching for optimal groups and segmentation in both variants is NP-hard. Consequently, we split the problem into 3 subproblems where we optimize groups, model parameters, and segmentation in turn while keeping the remaining structures fixed. We propose an iterative algorithm that requires O K H m + R n + R 2 H time per iteration, where n and m are the number of nodes and edges in the network. We demonstrate experimentally that the number of required iterations is typically low, the algorithm is able to discover the ground truth from synthetic datasets, and show that certain real-world networks exhibit recurrent behaviour as the likelihood does not deteriorate when H is lowered. [ABSTRACT FROM AUTHOR]

In this work, we propose a diffusion model for temporal social networks and relate it to other well-known models of social influence by investigating its formal properties. The model establishes dyadic influence weights based on two antagonistic components: the susceptibility to be influenced (or, conversely, inertia with respect to the status quo) and becoming independent of prior influence. The proposed model generalizes the Friedkin-Johnsen model by the inertia with respect to the current influence relationships. We show that this generalization is an over-parameterization for static but not for dynamic influence networks. These findings suggest that the model at hand expands the set of existing social influence models in a non-trivial way. [ABSTRACT FROM AUTHOR]

Co-evolving network models, wherein dynamics such as random walks on the network influence the evolution of the network structure, which in turn influences the dynamics, are of interest in a range of domains. While much of the literature in this area is currently supported by numerics, providing evidence for fascinating conjectures and phase transitions, proving rigorous results has been quite challenging. We propose a general class of co-evolving tree network models driven by local exploration, started from a single vertex called the root. New vertices attach to the current network via randomly sampling a vertex and then exploring the graph for a random number of steps in the direction of the root, connecting to the terminal vertex. Specific choices of the exploration step distribution lead to the well-studied affine preferential attachment and uniform attachment models, as well as less well understood dynamic network models with global attachment functionals such as PageRank scores (Chebolu and Melsted, in: SODA, 2008). We obtain local weak limits for such networks and use them to derive asymptotics for the limiting empirical degree and PageRank distribution. We also quantify asymptotics for the degree and PageRank of fixed vertices, including the root, and the height of the network. Two distinct regimes are seen to emerge, based on the expected exploration distance of incoming vertices, which we call the 'fringe' and 'non-fringe' regimes. These regimes are shown to exhibit different qualitative and quantitative properties. In particular, networks in the non-fringe regime undergo 'condensation' where the root degree grows at the same rate as the network size. Networks in the fringe regime do not exhibit condensation. Further, non-trivial phase transition phenomena are shown to arise for: (a) height asymptotics in the non-fringe regime, driven by the subtle competition between the condensation at the root and network growth; (b) PageRank distribution in the fringe regime, connecting to the well known power-law hypothesis. In the process, we develop a general set of techniques involving local limits, infinite-dimensional urn models, related multitype branching processes and corresponding Perron–Frobenius theory, branching random walks, and in particular relating tail exponents of various functionals to the scaling exponents of quasi-stationary distributions of associated random walks. These techniques are expected to shed light on a variety of other co-evolving network models. [ABSTRACT FROM AUTHOR]

We experience air traffic delays every day, but are there any recurrent patterns in these delays? In this study, we investigate the recurrence of delay propagation patterns in Japan’s domestic air transport network in 2019 by integrating delay causality networks and temporal network analysis. Additionally, we examine characteristics unique to delay propagation by comparing delay causality networks with corresponding randomized networks generated by a directed configuration model. As a result, we found that the structure of the delay propagation patterns can be classified into several groups. The identified groups exhibit statistically significant differences in total delay time and average out-degree, with different airports playing central roles in spreading delays. The results also suggest that some delay propagation patterns are particularly prominent during specific times of the year, which could be influenced by Japan’s seasonal and geographical factors. Moreover, we discovered that specific network motifs appear significantly more (or less) frequently in delay causality networks than their corresponding randomized counterparts. This characteristic is particularly pronounced in groups with more significant delays. These results suggest that delays propagate following specific directional patterns, which could significantly contribute to predicting air traffic delays. We expect the present study to trigger further research on recurrent and non-recurrent natures of air traffic delay propagation.

Complexity science provides a powerful framework for understanding physical, biological and social systems, and network analysis is one of its principal tools. Since many complex systems exhibit multilateral interactions that change over time, in recent years, network scientists have become increasingly interested in modelling and measuring dynamic networks featuring higher-order relations. At the same time, while network analysis has been more widely adopted to investigate the structure and evolution of law as a complex system, the utility of dynamic higher-order networks in the legal domain has remained largely unexplored. Setting out to change this, we introduce temporal hypergraphs as a powerful tool for studying legal network data. Temporal hypergraphs generalize static graphs by (i) allowing any number of nodes to participate in an edge and (ii) permitting nodes or edges to be added, modified or deleted. We describe models and methods to explore legal hypergraphs that evolve over time and elucidate their benefits through case studies on legal citation and collaboration networks that change over a period of more than 70 years. Our work demonstrates the potential of dynamic higher-order networks for studying complex legal systems, and it facilitates further advances in legal network analysis. This article is part of the theme issue 'A complexity science approach to law and governance'. [ABSTRACT FROM AUTHOR]

Dynamic community detection (DCD) in temporal networks is a complicated task that involves the selection of a method and its associated hyperparameters. How to choose the most appropriate method generally depends on the type of network being analysed and the specific properties of the data that define the network. In functional temporal networks derived from neuronal spike train data, communities are expected to be transient, and it is common for the network to contain multiple singleton communities. Here, we compare the performance of different DCD methods on functional temporal networks built from synthetic neuronal time series data with known community structure. We find that, for these networks, DCD methods that utilize interlayer links to perform community carry over between layers outperform other methods. However, we also observe that DCD performance is highly dependent on the topology of interlayer links, especially in the presence of singleton and transient communities. We therefore define a novel way of defining interlayer links in temporal networks called skeleton coupling that is specifically designed to enhance the linkage of communities in the network throughout time based on the topological properties of the community history. We show that integrating skeleton coupling with current DCD methods improves the method's performance in synthetic data with planted singleton and transient communities. The use of skeleton coupling to perform DCD will therefore allow for more accurate and interpretable results of community evolution in real-world neuronal data or in other systems with transient structure and singleton communities. [ABSTRACT FROM AUTHOR]

Temporal networks are commonly used to represent dynamical complex systems like social networks, simultaneous firing of neurons, human mobility or public transportation. Their dynamics may evolve on multiple time scales characterizing for instance periodic activity patterns or structural changes. The detection of these time scales can be challenging from the direct observation of simple dynamical network properties like the activity of nodes or the density of links. Here, we propose two new methods, which rely on already established static representations of temporal networks, namely supra-adjacency and temporal event graphs. We define dissimilarity metrics extracted from these representations and compute their power spectra from their Fourier transforms to effectively identify dominant periodic time scales characterizing the changes of the temporal network. We demonstrate our methods using synthetic and real-world data sets describing various kinds of temporal networks. We find that while in all cases the two methods outperform the reference measures, the supra-adjacency-based method identifies more easily periodic changes in network density, while the temporal event graph-based method is better suited to detect periodic changes in the group structure of the network. Our methodology may provide insights into different phenomena occurring at multiple time scales in systems represented by temporal networks. [ABSTRACT FROM AUTHOR]

The use of quantum satellites can be significant paving the way for novel telecommunications network paradigms, making it possible to connect remote regions. Given the rapid development of quantum communication technologies, many studies are focusing on quantum satellites interconnections via Free Space Optic (FSO), especially considering that the adoption of wired alternatives did not achieve significant results, mainly in terms of covering long distances. In this context, the global interconnection of Quantum Computers (QCs) through the so-called Quantum Internet (QI) compels for new communications and computing architectures such as Quantum Cloud (QCloud). To this purpose, the deployment of Satellite Quantum Networks (SQNs) could make easily available innovative services spanning from security to advanced computing, especially by using Software-Defined Networking (SDN) technology, which is considered a significant enabler for the management of SQNs. Moreover, considering the rapid variability of the topology in terms of nodes and links features, the paper proposes a specific Temporal Networks (TNs) optimization strategy embedded in an SDN Controller ecosystem, dynamically optimizing the path durations to achieve the highest entanglement rate, while limiting the disconnections. Performance evaluation in a worst case scenario shows that the proposed framework is able to support distributed applications by exploiting all the connection opportunities.

Abstract Pairwise temporal interactions between entities can be represented as temporal networks, which code the propagation of processes such as epidemic spreading or information cascades, evolving on top of them. The largest outcome of these processes is directly linked to the structure of the underlying network. Indeed, a node of a network at a given time cannot affect more nodes in the future than it can reach via time-respecting paths. This set of nodes reachable from a source defines an out-component, which identification is costly. In this paper, we propose an efficient matrix algorithm to tackle this issue and show that it outperforms other state-of-the-art methods. Secondly, we propose a hashing framework to coarsen large temporal networks into smaller proxies on which out-components are more easily estimated, and then recombined to obtain the initial components. Our graph hashing solution has implications in privacy respecting representation of temporal networks.

Scientists mapped the seismic time series into networks by considering the geographical location of events as nodes and establishing links between the nodes with different rules. Applying the successively defined models to construct the networks of seismic data, a variety of features of earthquake networks are detected (scale-free and small-world structures). Network construction models had changed in detail to optimize the performance of the verification of the minimum geographical size defined for the node. In all the studies, people try to use large data sets like years of data to ensure their results are good enough. In this work, by proposing the temporal network construction and employing the small-worldness property for data from Iran and California, we could achieve the minimum time scale needed for the best results. We verified the importance of this scale by analyzing two significant centrality measures (degree centrality and PageRank) introduced in the concept of earthquake network. [ABSTRACT FROM AUTHOR]

To explore evolutionary dynamics of collective behaviors within the interconnected population, previous studies usually map non-pairwise interactions to higher-order static networks. However, from human communications to chemical reactions and biological systems, interactions often change over time, which cannot be simply described by higher-order static networks. In this study, we introduce time effects into higher-order networks and correspondingly investigate the evolutionary dynamics of multiplayer snowdrift games on higher-order temporal networks. Specifically, extensive simulations from four empirical datasets reveal that (1) the temporal effect of higher-order networks can facilitate the evolution of cooperation; (2) the higher-order topology can enhance the emergence of cooperation within a certain range of parameters; (3) the contribution of temporal burstiness and participants burstiness to cooperation is reversed. Furthermore, we theoretically demonstrate that the higher-order structure will suppress the propagation of defection in temporal networks. Our findings offer a new avenue for studying the evolution of altruistic behaviors in realistic complex networks. [ABSTRACT FROM AUTHOR]

The controllability of temporal networks has become an important issue for researchers in recent years. In temporal networks, the controllability is achieved through the control of dynamics. Existing studies take only some dynamics into account when controlling such networks, e.g., only adding links, and ignore the rest. Besides, conventional controllability methods on temporal networks suffer from time complexity and data overhead. In this paper, a dynamic controllability method is proposed which considers all dynamics of temporal networks to fully control the network, including adding and removing nodes and links. The proposed controllability method finds all the minimum driver nodes sets (MDSs) of a temporal network in a polynomial time. In this regard, to maintain and track the network changes, a new tree-based model, named Controllable Dynamics Temporal Network (CDTN), is proposed by which the control process is executed with high speed and low overhead. The experimental results show that the proposed controllability method outperforms state-of-the-art methods in terms of speed (47% improvement), overhead (33% improvement), and the size of MDS (38% improvement).

Abstract Temporal networks are networks whose topology changes over time. Two nodes in a temporal network are connected at a discrete time step only if they have a contact/interaction at that time. The classic temporal network prediction problem aims to predict the temporal network one time step ahead based on the network observed in the past of a given duration. This problem has been addressed mostly via machine learning algorithms, at the expense of high computational costs and limited interpretation of the underlying mechanisms that form the networks. Hence, we propose to predict the connection of each node pair one step ahead based on the connections of this node pair itself and of node pairs that share a common node with this target node pair in the past. The concrete design of our two prediction models is based on the analysis of the memory property of real-world physical networks, i.e., to what extent two snapshots of a network at different times are similar in topology (or overlap). State-of-the-art prediction methods that allow interpretation are considered as baseline models. In seven real-world physical contact networks, our methods are shown to outperform the baselines in both prediction accuracy and computational complexity. They perform better in networks with stronger memory. Importantly, our models reveal how the connections of different types of node pairs in the past contribute to the connection estimation of a target node pair. Predicting temporal networks like physical contact networks in the long-term future beyond short-term i.e., one step ahead is crucial to forecast and mitigate the spread of epidemics and misinformation on the network. This long-term prediction problem has been seldom explored. Therefore, we propose basic methods that adapt each aforementioned prediction model to address classic short-term network prediction problem for long-term network prediction task. The prediction quality of all adapted models is evaluated via the accuracy in predicting each network snapshot and in reproducing key network properties. The prediction based on one of our models tends to have the highest accuracy and lowest computational complexity.

Abstract Manga, Japanese comics, has been popular on a global scale. Social networks among characters, which are often called character networks, may be a significant contributor to their popularity. We collected data from 162 popular manga that span over 70 years and analyzed their character networks. First, we found that many of static and temporal properties of the character networks are similar to those of real human social networks. Second, the character networks of most manga are protagonist-centered such that a single protagonist interacts with the majority of other characters. Third, the character networks for manga mainly targeting boys have shifted to denser and less protagonist-centered networks and with fewer characters over decades. Manga mainly targeting girls showed the opposite trend except for the downward trend in the number of characters. The present study, which relies on manga data sampled on an unprecedented scale, paves the way for further population studies of character networks and other aspects of comics.

This study advances the field of infectious disease forecasting by introducing a novel approach to micro-level contact modeling, leveraging human movement patterns to generate realistic temporal-dynamic networks. Through the incorporation of human mobility models and parameter tuning, this research presents an innovative method for simulating micro-level encounters that closely mirror infection dynamics within confined spaces. Central to our methodology is the application of Bayesian optimization for parameter selection, which refines our models to emulate both the properties of real-world infection curves and the characteristics of network properties. Typically, large-scale epidemiological simulations overlook the specifics of human mobility within confined spaces or rely on overly simplistic models. By focusing on the distinct aspects of infection propagation within specific locations, our approach strengthens the realism of such pandemic simulations. The resulting models shed light on the role of spatial encounters in disease spread and improve the capability to forecast and respond to infectious disease outbreaks. This work not only contributes to the scientific understanding of micro-level transmission patterns but also offers a new perspective on temporal network generation for epidemiological modeling.