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1. Quantifying functionally equivalent species and ecological network dissimilarity with optimal transport distances

   https://doi.org/10.1111/2041-210x.70130  

   Hung, Kai M; Beaudrot, Lydia; Finneran, Ann E; Zalles, Alex G; Uribe, César A (August 2025, Methods in Ecology and Evolution)

   Abstract Quantifying the structure and dynamics of species interactions in ecological communities is fundamental to studying ecology and evolution. While there are numerous approaches to analysing ecological networks, there is not yet an approach that can (1) quantify dissimilarity in the global structure of ecological networks that range from identical species and interaction composition to zero shared species or interactions and (2) map species between such networks while incorporating additional ecological information, such as species traits or abundances.To address these challenges, we introduce the use of optimal transport distances to quantify ecological network dissimilarity and functionally equivalent species between networks. Specifically, we describe the Gromov–Wasserstein (GW) and Fused Gromov–Wasserstein (FGW) distances. We apply these optimal transport methods to synthetic and empirical data, using mammal food webs throughout sub‐Saharan Africa for illustration. We showcase the application of GW and FGW distances to identify the most functionally similar species between food webs, incorporate additional trait information into network comparisons and quantify food web dissimilarity among geographic regions.Our results demonstrate that GW and FGW distances can effectively differentiate ecological networks based on their topological structure while identifying functionally equivalent species, even when networks have different species. The FGW distance further improves node mapping for basal species by incorporating node‐level traits. We show that these methods allow for a more nuanced understanding of the topological similarities in food web networks among geographic regions compared to an alternative measure of network dissimilarity based on species identities.Optimal transport distances offer a new approach for quantifying functional equivalence between networks and a measure of network dissimilarity suitable for a broader range of uses than existing approaches. OT methods can be harnessed to analyse ecological networks at large spatial scales and compare networks among ecosystems, realms or taxa. Optimal transport‐based distances, therefore, provide a powerful tool for analysing ecological networks with great potential to advance our understanding of ecological community structure and dynamics in a changing world. 

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2. Social network analysis of manga: similarities to real-world social networks and trends over decades

   https://doi.org/10.1007/s41109-023-00604-0  

   Sugishita, Kashin; Masuda, Naoki (December 2023, Applied Network Science)

   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. 

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3. 4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia

   https://doi.org/10.1002/hbm.26773  

   Pusuluri, Krishna; Fu, Zening; Miller, Robyn; Pearlson, Godfrey; Kochunov, Peter; Van_Erp, Theo_G_M; Iraji, Armin; Calhoun, Vince_D (July 2024, Human Brain Mapping)

   Abstract Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel‐wise changes within and between brain networks. 

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4. Aromatic foldamers as molecular springs in network polymers

   https://doi.org/10.1039/D2CC01223E  

   Miller, K. Andrew; Dodo, Obed J.; Devkota, Govinda Prasad; Kirinda, Viraj C.; Bradford, Kate G.; Sparks, Jessica L.; Hartley, C. Scott; Konkolewicz, Dominik (May 2022, Chemical Communications)

   Polymer networks crosslinked with spring-like ortho -phenylene ( o P) foldamers were developed. NMR analysis indicated the o P crosslinkers were well-folded. Polymer networks with o P-based crosslinkers showed enhanced energy dissipation and elasticity compared to divinylbenzene crosslinked networks. The energy dissipation was attributed to the strain-induced reversible unfolding of the o P units. Energy dissipation increased with the number of helical turns in the foldamer. 

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5. The training process of many deep networks explores the same low-dimensional manifold

   https://doi.org/10.1073/pnas.2310002121  

   Mao, Jialin; Griniasty, Itay; Teoh, Han Kheng; Ramesh, Rahul; Yang, Rubing; Transtrum, Mark K; Sethna, James P; Chaudhari, Pratik (March 2024, Proceedings of the National Academy of Sciences)

   We develop information-geometric techniques to analyze the trajectories of the predictions of deep networks during training. By examining the underlying high-dimensional probabilistic models, we reveal that the training process explores an effectively low-dimensional manifold. Networks with a wide range of architectures, sizes, trained using different optimization methods, regularization techniques, data augmentation techniques, and weight initializations lie on the same manifold in the prediction space. We study the details of this manifold to find that networks with different architectures follow distinguishable trajectories, but other factors have a minimal influence; larger networks train along a similar manifold as that of smaller networks, just faster; and networks initialized at very different parts of the prediction space converge to the solution along a similar manifold. 

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