Human mobility describes physical patterns of movement of people within a spatial system. Many of these patterns, including daily commuting, are cyclic and quantifiable. These patterns capture physical phenomena tied to processes studied in urban planning, epidemiology, and other social, behavioral, and economic sciences. This paper advances human mobility research by proposing a statistical method for identifying locations that individual move to and through at a rate proportionally higher than other locations, using commuting data for the country of New Zealand as a case study. We term these locations mobility loci and they capture a global property of communities in which people commute. The method makes use of a directed-graph representation where vertices correspond to locations, and traffic between locations correspond to edge weights. Following a normalization, the graph can be regarded as a Markov chain whose stationary distribution can be calculated. The proposed permutation procedure is then applied to determine which stationary distribution values are larger than what would be expected, given the structure of the directed graph and traffic between locations. The results of this method are evaluated, including a comparison to what is already known about commuting patterns in the area as well as a comparison with similar features.

A wide variety of complex systems are characterized by interactions of different types involving varying numbers of units. Multiplex hypergraphs serve as a tool to describe such structures, capturing distinct types of higher-order interactions among a collection of units. In this work, we introduce a comprehensive set of measures to describe structural connectivity patterns in multiplex hypergraphs, considering scales from node and hyperedge levels to the system’s mesoscale. We validate our measures with three real-world datasets: scientific co-authorship in physics, movie collaborations, and high school interactions. This validation reveals new collaboration patterns, identifies trends within and across movie subfields, and provides insights into daily interaction dynamics. Our framework aims to offer a more nuanced characterization of real-world systems marked by both multiplex and higher-order interactions.

There are various methods for handling longitudinal data in graphs and social networks, all of which have an impact on the algorithms used in data analysis. This article provides an overview of limitations, potential solutions, and unanswered questions regarding different temporal data schemas in social networks that are comparable to existing techniques. Restricting algorithms to a specific time point or layer has no effect on the results. However, when applying these approaches to a network with multiple time points, adjusted algorithms or reinterpretation becomes necessary. Therefore, using a generic definition of temporal networks as one graph, we aim to explore how we could analyze longitudinal social networks with centrality measures. Additionally, we introduce two new measures, “importance” and “change”, to identify nodes with specific behaviors. We provide case studies featuring three different real-world networks exhibiting both limitations and benefits of the novel approach. Furthermore, we present techniques to estimate variations in importance and degree centrality over time.

Migration’s influence in shaping population dynamics in times of impending climate and population crises exposes its crucial role in upholding societal cohesion. As migration impacts virtually all aspects of life, it continues to require attention across scientific disciplines. This study aims to bridge the gap between theoretical understanding and practical application by integrating network analysis and regression methodologies within Migration Studies. In the study we employ network analysis to elucidate migration patterns at various geographical scales-city, country, and global. Additionally, regression analysis is discussed on an exploratory level, where we focus on the underlying factors driving migration, and identifying the key independent variables to enhance predictive accuracy. The study exposes distinct migration network structure and its features, and the consequences these have on conventional regression analysis applications. We conclude on the importance of methodological coherence and disciplinary integration, and highlight the avenues for enhancing the predictive power of migration models.

Real-world networks often encounter disruptions that can have profound societal impacts. Therefore, enhancing network robustness against random failures and targeted attacks is crucial. Bypass rewiring, in which adjacent nodes are immediately reconnected following node removal, has been proposed as a promising method for enhancing network robustness. However, this method typically requires numerous bypass links and incurs significant costs. In this paper, we propose link-limited bypass rewiring, in which bypass links are reconnected stochastically. Additionally, we investigate the relationship between the number of bypass links and robustness improvement. Our findings, which are based on analytical and numerical results, reveal a tradeoff relationship between the number of added bypass links and robustness enhancement. Notably, preferentially reconnecting high-degree nodes was found to be effective for improving robustness. These insights can inform the design of resilient systems in real-world networks, offering strategies for enhancing robustness against node removal.

Digital transformation refers to an organization’s use of digital technology to improve its products, services, and operations, aligning them with evolving business requirements. To demonstrate this transformative process, we present a real-life case study where a company seeks to automate the classification of their textual data rather than relying on manual methods. Transitioning to automated classification involves deploying machine learning models, which rely on pre-labeled datasets for training and making predictions on new data. However, upon receiving the dataset from the company, we faced challenges due to the imbalanced distribution of labels and moderately large label spaces. To tackle text classification with such a business dataset, we evaluated four distinct methods for multi-label text classification: fine-tuned Bidirectional Encoder Representations from Transformers (BERT), Binary Relevance, Classifier Chains, and Label Powerset. The results revealed that fine-tuned BERT significantly outperformed the other methods across key metrics like Accuracy, F1-score, Precision, and Recall. Binary Relevance also displayed competence in handling the dataset effectively, while Classifier Chains and Label Powerset exhibited comparatively less impressive performance. These findings highlight the remarkable effectiveness of fine-tuned BERT model and the Binary Relevance classifier in multi-label text classification tasks, particularly when dealing with imbalanced training datasets and moderately large label spaces. This positions them as valuable assets for businesses aiming to automate data classification in the digital transformation era.

We present a nation-wide network analysis of non-fatal opioid-involved overdose journeys in the United States. Leveraging a unique proprietary dataset of Emergency Medical Services incidents, we construct a journey-to-overdose geospatial network capturing nearly half a million opioid-involved overdose events spanning 2018–2023. We analyze the structure and sociological profiles of the nodes, which are counties or their equivalents, characterize the distribution of overdose journey lengths, and investigate changes in the journey network between 2018 and 2023. Our findings include that authority and hub nodes identified by the HITS algorithm tend to be located in urban areas and involved in overdose journeys with particularly long geographical distances.

Often, due to prohibitively large size or to limits to data collecting APIs, it is not possible to work with a complete network dataset and sampling is required. A type of sampling which is consistent with Twitter API restrictions is uniform edge sampling. In this paper, we propose a methodology for the recovery of two fundamental network properties from an edge-sampled network: the degree distribution and the triangle count (we estimate the totals for the network and the counts associated with each edge). We use a Bayesian approach and show a range of methods for constructing a prior which does not require assumptions about the original network. Our approach is tested on two synthetic and three real datasets with diverse sizes, degree distributions, degree-degree correlations and triangle count distributions.

The degree of polarization in many societies has become a pressing concern in media studies. Typically, it is argued that the internet and social media have created more media producers than ever before, allowing individual, biased media consumers to expose themselves only to what already confirms their beliefs, leading to polarized echo-chambers that further deepen polarization. This work introduces extensions to the recent Cognitive Cascades model of Rabb et al. to study this dynamic, allowing for simulation of information spread between media and networks of variably biased citizens. Our results partially confirm the above polarization logic, but also reveal several important enabling conditions for polarization to occur: (1) the distribution of media belief must be more polarized than the population; (2) the population must be at least somewhat persuadable to changing their belief according to new messages they hear; and finally, (3) the media must statically continue to broadcast more polarized messages rather than, say, adjust to appeal more to the beliefs of their current subscribers. Moreover, and somewhat counter-intuitively, under these conditions we find that polarization is more likely to occur when media consumers are exposed to more diverse messages, and that polarization occurred most often when there were low levels of echo-chambers and fragmentation. These results suggest that polarization is not simply due to biased individuals responding to an influx of media sources in the digital age, but also a consequence of polarized media conditions within an information ecosystem that supports more diverse exposure than is typically thought.

The labor market has transformed with the advent of the gig economy, characterized by short-term and flexible work arrangements facilitated by online platforms. As this trend becomes increasingly prevalent, it presents unique opportunities and challenges. In this manuscript, we comprehensively characterize the social networks of gig economy workers in each of the 15 cities studied. Our analysis reveals a scaling relationship between networks and the city population. In particular, we note the high level of modularity of the networks, and we argue that it results from the natural specialization of couriers along different areas of the cities. Furthermore, we show that degree and betweenness centrality is positively correlated with income but not with tenure. Our findings shed new light on the social organization of the gig economy workers and provide valuable insights for the management and design of gig economy platforms.