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Loneliness is detrimental to well-being and is often accompanied by self-reported feelings of not being understood by other people. What contributes to such feelings in lonely people? We used functional MRI of 66 first-year university students to unobtrusively measure the relative alignment of peoples mental processing of naturalistic stimuli and tested whether lonely people actually process the world in idiosyncratic ways. We found evidence for such idiosyncrasy: Lonely individuals neural responses were dissimilar to those of their peers, particularly in regions of the default-mode network in which similar responses have been associated with shared perspectives and subjective understanding. These relationships persisted when we controlled for demographic similarities, objective social isolation, and individuals friendships with each other. Our findings raise the possibility that being surrounded by people who see the world differently from oneself, even if one is friends with them, may be a risk factor for loneliness.

Abstract: Ideological divisions in the United States have become increasingly prominent in daily communication. Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a text document, one can attempt to discern and describe its polarity. Intuitively, the named entities (i.e., the nouns and the phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term “pro-choice” are likely to be liberal and people who use the term “pro-life” are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the Polarity-aware Embedding Multi-task learning (PEM) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding’s polarity component and its semantic component. Our experimental results demonstrate that our PEM model can successfully learn polarity-aware embeddings that perform well at tweet-level and account-level classification tasks. We examine a variety of applications—including a study of spatial and temporal distributions of polarities and a comparison between tweets from Twitter and posts from Parler—and we thereby demonstrate the effectiveness of our PEM model. We also discuss important limitations of our work and encourage caution when applying the PEM model to real-world scenarios.

Convergent processing of the world may be a factor that contributes to social connectedness. We use neuroimaging and network analysis to investigate the association between the social-network position (as measured by in-degree centrality) of first-year university students and their neural similarity while watching naturalistic audio-visual stimuli (specifically, videos). There were 119 students in the social-network study; 63 of them participated in the neuroimaging study. We show that more central individuals had similar neural responses to their peers and to each other in brain regions that are associated with high-level interpretations and social cognition (e.g., in the default mode network), whereas less-central individuals exhibited more variable responses. Self-reported enjoyment of and interest in stimuli followed a similar pattern, but accounting for these data did not change our main results. These findings show that neural processing of external stimuli is similar in highly-central individuals but is idiosyncratic in less-central individuals.

Networks, which represent agents and interactions between them, arise inmyriad applications throughout the sciences, engineering, and even thehumanities. To understand large-scale structure in a network, a common task isto cluster a network's nodes into sets called "communities", such that thereare dense connections within communities but sparse connections between them. Apopular and statistically principled method to perform such clustering is touse a family of generative models known as stochastic block models (SBMs). Inthis paper, we show that maximum likelihood estimation in an SBM is a networkanalog of a well-known continuum surface-tension problem that arises from anapplication in metallurgy. To illustrate the utility of this relationship, weimplement network analogs of three surface-tension algorithms, with which wesuccessfully recover planted community structure in synthetic networks andwhich yield fascinating insights on empirical networks that we construct fromhyperspectral videos.

Networks, which represent agents and interactions between them, arise inmyriad applications throughout the sciences, engineering, and even thehumanities. To understand large-scale structure in a network, a common task isto cluster a network's nodes into sets called "communities", such that thereare dense connections within communities but sparse connections between them. Apopular and statistically principled method to perform such clustering is touse a family of generative models known as stochastic block models (SBMs). Inthis paper, we show that maximum likelihood estimation in an SBM is a networkanalog of a well-known continuum surface-tension problem that arises from anapplication in metallurgy. To illustrate the utility of this relationship, weimplement network analogs of three surface-tension algorithms, with which wesuccessfully recover planted community structure in synthetic networks andwhich yield fascinating insights on empirical networks that we construct fromhyperspectral videos.

The goal of our project is to automatically generate school bus routes for the Los Angeles Unified School District (LAUSD). We examined four algorithms, including two from the existing literature and two new ones that we developed. A major focus of our work was the construction of “mixed-load routes,” which transport students from multiple schools. Based on our measurements (whose imperfections we discuss), three of the four algorithms perform at least as well as the existing route plan, and one of those three performs better than the existing route plan. We also delivered a user-friendly routing program to LAUSD that uses one of these algorithms, and we have made our software publicly available. Our insights and results are also applicable to other school districts that permit mixed-load routing.

The goal of our project is to automatically generate school bus routes for the Los Angeles Unified School District (LAUSD). We examined four algorithms, including two from the existing literature and two new ones that we developed. A major focus of our work was the construction of “mixed-load routes,” which transport students from multiple schools. Based on our measurements (whose imperfections we discuss), three of the four algorithms perform at least as well as the existing route plan, and one of those three performs better than the existing route plan. We also delivered a user-friendly routing program to LAUSD that uses one of these algorithms, and we have made our software publicly available. Our insights and results are also applicable to other school districts that permit mixed-load routing.

Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as multilayer network analysis, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour through connected 'layers' of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population and evolutionary levels of organization. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.

Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as multilayer network analysis, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour through connected 'layers' of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population and evolutionary levels of organization. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.

There is often latent network structure in spatial and temporal data and thetools of network analysis can yield fascinating insights into such data. Inthis paper, we develop a nonparametric method for network reconstruction fromspatiotemporal data sets using multivariate Hawkes processes. In contrast toprior work on network reconstruction with point-process models, which has oftenfocused on exclusively temporal information, our approach uses both temporaland spatial information and does not assume a specific parametric form ofnetwork dynamics. This leads to an effective way of recovering an underlyingnetwork. We illustrate our approach using both synthetic networks and networksconstructed from real-world data sets (a location-based social media network, anarrative of crime events, and violent gang crimes). Our results demonstratethat, in comparison to using only temporal data, our spatiotemporal approachyields improved network reconstruction, providing a basis for meaningfulsubsequent analysis --- such as community structure and motif analysis --- ofthe reconstructed networks.

There is often latent network structure in spatial and temporal data and thetools of network analysis can yield fascinating insights into such data. Inthis paper, we develop a nonparametric method for network reconstruction fromspatiotemporal data sets using multivariate Hawkes processes. In contrast toprior work on network reconstruction with point-process models, which has oftenfocused on exclusively temporal information, our approach uses both temporaland spatial information and does not assume a specific parametric form ofnetwork dynamics. This leads to an effective way of recovering an underlyingnetwork. We illustrate our approach using both synthetic networks and networksconstructed from real-world data sets (a location-based social media network, anarrative of crime events, and violent gang crimes). Our results demonstratethat, in comparison to using only temporal data, our spatiotemporal approachyields improved network reconstruction, providing a basis for meaningfulsubsequent analysis --- such as community structure and motif analysis --- ofthe reconstructed networks.

We study quasiperiodicity-induced localization of waves in strongly precompressed granular chains. We propose three different set-ups, inspired by the Aubry-André (AA) model, of quasiperiodic chains; and we use these models to compare the effects of on-site and off-site quasiperiodicity in nonlinear lattices. When there is purely on-site quasiperiodicity, which we implement in two different ways, we show for a chain of spherical particles that there is a localization transition (as in the original AA model). However, we observe no localization transition in a chain of cylindrical particles in which we incorporate quasiperiodicity in the distribution of contact angles between adjacent cylinders by making the angle periodicity incommensurate with that of the chain. For each of our three models, we compute the Hofstadter spectrum and the associated Minkowski-Bouligand fractal dimension, and we demonstrate that the fractal dimension decreases as one approaches the localization transition (when it exists). We also show, using the chain of cylinders as an example, how to recover the Hofstadter spectrum from the system dynamics. Finally, in a suite of numerical computations, we demonstrate localization and also that there exist regimes of ballistic, superdiffusive, diffusive and subdiffusive transport. Our models provide a flexible set of systems to study quasiperiodicity-induced analogues of Anderson phenomena in granular chains that one can tune controllably from weakly to strongly nonlinear regimes.This article is part of the theme issue 'Nonlinear energy transfer in dynamical and acoustical systems'.

The original version of this Article contained an error in second sentence of the Acknowledgements, which incorrectly read 'J.Y. and P.G.K. also acknowledge support from US-ARO (W911NF-15-1-0604) and US-AFOSR (FA9550-17-1-011), and P.G.K. also gratefully acknowledges support from the Stavros Niarchos Foundation via the Greek Diaspora Fellowship Program.' The correct version states 'US-AFOSR (FA9550-17-1-0114)' in place of 'US-AFOSR (FA9550-17-1-011)'. This has been corrected in both the PDF and HTML versions of the Article.

Interactions among individual animals - and between these individuals and their environment - yield complex, multifaceted systems. The development of multilayer network analysis offers a promising new approach for studying animal social behavior and its relation to eco-evolutionary dynamics.

Energy transport properties in heterogeneous materials have attracted scientific interest for more than half of a century, and they continue to offer fundamental and rich questions. One of the outstanding challenges is to extend Anderson theory for uncorrelated and fully disordered lattices in condensed-matter systems to physical settings in which additional effects compete with disorder. Here we present the first systematic experimental study of energy transport and localization properties in simultaneously disordered and nonlinear granular crystals. In line with prior theoretical studies, we observe in our experiments that disorder and nonlinearity-which individually favor energy localization-can effectively cancel each other out, resulting in the destruction of wave localization. We also show that the combined effect of disorder and nonlinearity can enable manipulation of energy transport speed in granular crystals. Specifically, we experimentally demonstrate superdiffusive transport. Furthermore, our numerical computations suggest that subdiffusive transport should be attainable by controlling the strength of the system's external precompression force.

Energy transport properties in heterogeneous materials have attracted scientific interest for more than half of a century, and they continue to offer fundamental and rich questions. One of the outstanding challenges is to extend Anderson theory for uncorrelated and fully disordered lattices in condensed-matter systems to physical settings in which additional effects compete with disorder. Here we present the first systematic experimental study of energy transport and localization properties in simultaneously disordered and nonlinear granular crystals. In line with prior theoretical studies, we observe in our experiments that disorder and nonlinearity-which individually favor energy localization-can effectively cancel each other out, resulting in the destruction of wave localization. We also show that the combined effect of disorder and nonlinearity can enable manipulation of energy transport speed in granular crystals. Specifically, we experimentally demonstrate superdiffusive transport. Furthermore, our numerical computations suggest that subdiffusive transport should be attainable by controlling the strength of the system's external precompression force.

The original version of this Article contained an error in second sentence of the Acknowledgements, which incorrectly read 'J.Y. and P.G.K. also acknowledge support from US-ARO (W911NF-15-1-0604) and US-AFOSR (FA9550-17-1-011), and P.G.K. also gratefully acknowledges support from the Stavros Niarchos Foundation via the Greek Diaspora Fellowship Program.' The correct version states 'US-AFOSR (FA9550-17-1-0114)' in place of 'US-AFOSR (FA9550-17-1-011)'. This has been corrected in both the PDF and HTML versions of the Article.

The study of network structure is pervasive in sociology, biology, computer science, and many other disciplines. One of the most important areas of network science is the algorithmic detection of cohesive groups of nodes called "communities." One popular approach to finding communities is to maximize a quality function known as modularity to achieve some sort of optimal clustering of nodes. In this paper, we interpret the modularity function from a novel perspective: we reformulate modularity optimization as a minimization problem of an energy functional that consists of a total variation term and an -2 balance term. By employing numerical techniques from image processing and ≤1 compressive sensing-such as convex splitting and the Merriman-Bence-Osher (MBO) scheme-we develop a variational algorithm for the minimization problem. We present our computational results using both synthetic benchmark networks and real data. Copyright © by SIAM.

As a person learns a new skill, distinct synapses, brain regions, and circuits are engaged and change over time. In this paper, we develop methods to examine patterns of correlated activity across a large set of brain regions. Our goal is to identify properties that enable robust learning of a motor skill. We measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities, we find that the complex reconfiguration patterns of the brain's putative functional modules that control learning can be described parsimoniously by the combined presence of a relatively stiff temporal core that is composed primarily of sensorimotor and visual regions whose connectivity changes little in time and a flexible temporal periphery that is composed primarily of multimodal association regions whose connectivity changes frequently. The separation between temporal core and periphery changes over the course of training and, importantly, is a good predictor of individual differences in learning success. The core of dynamically stiff regions exhibits dense connectivity, which is consistent with notions of core-periphery organization established previously in social networks. Our results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior.