Review
Genetics & Heredity
Yuanyuan Lv, Shan Huang, Tianjiao Zhang, Bo Gao
Summary: Multilayer networks play a key role in studying complex biological systems, with applications ranging from cells to organs and groups. The complexity of biological systems necessitates the use of multilayer network models, particularly evident in brain research. An emphasis on quality assessment is placed on multilayer and single-layer networks for evaluating the effectiveness of network studies.
FRONTIERS IN GENETICS
(2021)
Article
Computer Science, Information Systems
Yan Chen, Dongxu Mo
Summary: Multilayer networks encode multiple types of relations in complex systems and stochastic block models are commonly used for community detection. This article proposes a generalized stochastic block model for multilayer weighted networks and develops a variational expectation-maximization algorithm to estimate the parameters. An upper bound for the probability of misclassification is derived and the model is compared and validated on synthetic networks and real systems.
INFORMATION SCIENCES
(2022)
Article
Mathematics, Interdisciplinary Applications
Charles C. Hyland, Yuanming Tao, Lamiae Azizi, Martin Gerlach, Tiago P. Peixoto, Eduardo G. Altmann
Summary: The method proposed in the study is based on Multilayer Networks and Stochastic Block Models, applying the same non-parametric probabilistic framework to different types of datasets to tackle the challenge of clustering documents and finding topics. By taking into account multiple types of information, it provides a more nuanced view on topic- and document-clusters and increases the ability to predict missing links.
Article
Automation & Control Systems
Xing Fan, Marianna Pensky, Feng Yu, Teng Zhang
Summary: This paper considers a Mixture Multilayer Stochastic Block Model (MMLSBM) and proposes an alternating minimization algorithm (ALMA) for recovering layer partition and estimating connection probability matrices of different layers. Compared to previous methods, ALMA achieves higher accuracy both theoretically and numerically.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Xuerong Li, Xiaoyue Xu, Jiaqi Liu, Jichang Dong, Jinhu Lu
Summary: In the context of global integration, the theory of financial complex networks has made significant contributions to the establishment of stable financial systems and effective regulatory systems. This survey paper presents a systematic methodology for the multi-layer financial complex networks and their applications. It summarizes several typical financial networks in existing research and identifies financial risk contagion and spillover effects as the core issues that most literature focuses on. Finally, it reviews the shortcomings of the existing literature and suggests future research directions in this area.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2022)
Article
Biology
Yingnan Hou, Tengyu Xie, Liuqing He, Liang Tao, Jing Huang
Summary: The study revealed the presence of topological links in protein complexes predicted by AlphaFold-Multimer, highlighting the significance for protein structure prediction and the study of protein-protein interactions.
COMMUNICATIONS BIOLOGY
(2023)
Article
Mathematics, Interdisciplinary Applications
Jinming Wan, Genki Ichinose, Michael Small, Hiroki Sayama, Yamir Moreno, Changqing Cheng
Summary: This study presents a multi-layer network model to study contagion dynamics and behavioral adaptation. The model reveals the interaction between physically isolated communities and the coevolution of behavioral change and spreading dynamics. The analytical insights provide compelling guidelines for coordinated policy design to enhance preparedness for future pandemics.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Computer Science, Interdisciplinary Applications
Zhihao Zheng, Shuming Gao, Chun Shen
Summary: This paper proposes a progressive block decomposition algorithm that simplifies models by suppressing features and recovers the suppressed features to obtain a consistency-ensured block structure.
ENGINEERING WITH COMPUTERS
(2022)
Article
Operations Research & Management Science
Lorenzo Federico, Ayoub Mounim, Pierpaolo D'Urso, Livia De Giovanni
Summary: In this paper, the four stages of copper extraction, refining, and processing were identified using a lifecycle perspective. The import/export behaviors of countries at these stages reflect their position in the global copper production and consumption network. Trade flows of four commodities related to copper from 142 countries were analyzed for five years, and a directed multilayer network model was applied. Countries were grouped based on their structural equivalence using a Multilayer Stochastic Block Model, and a deep learning model was used to embed the countries in an Euclidean plane. Out of 142 countries, 97 consistently maintained the same position in the copper supply chain over the five years, while the other 45 had different roles.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Statistics & Probability
Kang Fu, Jianwei Hu
Summary: In this paper, the profile-pseudo likelihood method is extended from the single-layer stochastic block model to the multilayer stochastic block model, with the assumption of identical community membership labels across network layers. The proposed algorithm is proven to have convergence guarantee and produce strongly consistent estimated community labels. The method is further applied to the multilayer degree-corrected stochastic block model, and both simulation studies and real-world data examples show its effectiveness.
Article
Multidisciplinary Sciences
Devan Diwanji, Raphael Trenker, Tarjani M. Thaker, Feng Wang, David A. Agard, Kliment A. Verba, Natalia Jura
Summary: This study reveals the dynamics of the HER2-HER3 dimer upon binding of NRG1 beta using cryo-electron microscopy. The analysis of the structure shows that the mutant HER2 can interact with the dimerization arm of HER3 to stabilize the dimerization interface. The research suggests that both therapeutic agents and oncogenic mutations exploit the intrinsic dynamics of the HER2-HER3 heterodimer.
Article
Physics, Multidisciplinary
Prejaas Tewarie, Bastian Prasse, Jil Meier, Aine Byrne, Manlio De Domenico, Cornelis J. Stam, Matthew J. Brookes, Arjan Hillebrand, Andreas Daffertshofer, Stephen Coombes, Piet Van Mieghem
Summary: Recent studies suggest that treating neurophysiological networks separately for different frequency bands may be inadequate due to significant coupling between these bands. A multilayer network approach allows for analyzing frequency-specific networks in a unified framework, with interlayer connectivity playing a crucial role in network reconstruction.
NEW JOURNAL OF PHYSICS
(2021)
Article
Statistics & Probability
Jianwei Hu, Jingfei Zhang, Hong Qin, Ting Yan, Ji Zhu
Summary: The article introduces a goodness-of-fit test for the stochastic block model based on the maximum entry of the centered and rescaled adjacency matrix. It allows the number of communities to grow linearly and has asymptotic power guarantee. Both simulation studies and real-world data examples support the effectiveness of the proposed method.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2021)
Article
Mathematics, Interdisciplinary Applications
Chengyi Tu, Jianhong Luo, Ying Fan, Xuwei Pan
Summary: Dimensionality reduction is a powerful tool for analyzing complex systems and uncovering their underlying mechanisms and phenomena. We have developed a framework for dimensionality reduction of stochastic complex dynamical networks, which can capture the essential features and long-term dynamics of the original system in a low-dimensional effective equation.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Physics, Fluids & Plasmas
David Abella, Maxi San Miguel, Jose J. Ramasco
Summary: We investigated the non-Markovian effects of aging on binary-state dynamics in complex networks. Aging is defined as the tendency of agents to be less likely to change their state the longer they have been in the current state, resulting in heterogeneous activity patterns. We focused on the aging effects in the Threshold model, which is used to explain the adoption process of new technologies. Through analytical approximations and Monte Carlo simulations, we found that aging slows down the cascade dynamics towards the full-adoption state and alters the growth laws of adopters' density.
Article
Physics, Multidisciplinary
Oscar Fajardo-Fontiveros, Roger Guimera, Marta Sales-Pardo
Summary: Network inference is the process of learning complex network properties from data. Metadata, including node attributes and other network information, can improve inference in probabilistic network models. This study investigates the impact of metadata on the inference process and finds that the addition of metadata can dramatically change the accuracy of predictions. When data and metadata are correlated, metadata has the most significant contribution to the inference process.
Article
Multidisciplinary Sciences
Manusnan Suriyalaksh, Celia Raimondi, Abraham Mains, Anne Segonds-Pichon, Shahzabe Mukhtar, Sharlene Murdoch, Rebeca Aldunate, Felix Krueger, Roger Guimera, Simon Andrews, Marta Sales-Pardo, Olivia Casanueva
Summary: We designed a wisdom-of-the-crowds GRN inference pipeline coupled with complex network analysis to understand the organizational principles governing gene regulation in long-lived glp-1/Notch Caenorhabdities legans. Through screening 80% of regulators, we discovered 50 new aging genes, with 86% having human orthologues. The core genes essential for longevity, including those involved in insulin-like signaling (ILS), were found, indicating the predictive functionality of the GRN structure.
Article
Green & Sustainable Science & Technology
Daniel Vazquez, Roger Guimera, Marta Sales-Pardo, Gonzalo Guillen-Gosalbez
Summary: Precisely predicting the relationship between countries' energy consumption and pollution levels and socioeconomic drivers is crucial for supporting effective sustainable policy-making. Traditional predictive models based on rigid mathematical expressions with constant elasticities are limited, while a Bayesian approach to symbolic regression can find analytical expressions that outperform traditional models and challenge the assumption of constant elasticities.
SUSTAINABLE PRODUCTION AND CONSUMPTION
(2022)
Article
Biochemistry & Molecular Biology
Alba Gonzalez-Franquesa, Pau Gama-Perez, Marta Kulis, Karolina Szczepanowska, Norma Dahdah, Sonia Moreno-Gomez, Ana Latorre-Pellicer, Rebeca Fernandez-Ruiz, Antoni Aguilar-Mogas, Anne Hoffman, Erika Monelli, Sara Samino, Joan Miro-Blanch, Gregor Oemer, Xavier Duran, Estrella Sanchez-Rebordelo, Marc Schneeberger, Merce Obach, Joel Montane, Giancarlo Castellano, Vicente Chapaprieta, Wenfei Sun, Lourdes Navarro, Ignacio Prieto, Carlos Castano, Anna Novials, Ramon Gomis, Maria Monsalve, Marc Claret, Mariona Graupera, Guadalupe Soria, Christian Wolfrum, Joan Vendrell, Sonia Fernandez-Veledo, Jose Antonio Enriquez, Angel Carracedo, Jose Carlos Perales, Ruben Nogueiras, Laura Herrero, Aleksandra Trifunovic, Markus A. Keller, Oscar Yanes, Marta Sales-Pardo, Roger Guimera, Matthias Blueher, Jose Ignacio Martin-Subero, Pablo M. Garcia-Roves
Summary: This study systematically assessed metabolic plasticity in diet-induced obese mice after a combined nutritional and exercise intervention, and found that there is a significant metabolic dysfunction in visceral white adipose tissue, which leads to a breakdown of metabolic plasticity.
Article
Chemistry, Multidisciplinary
Valentina Negri, Daniel Vazquez, Marta Sales-Pardo, Roger Guimera, Gonzalo Guillen-Gosalbez
Summary: This research demonstrates that Bayesian symbolic learning can simplify process modeling tasks, making process models easier to use. Compared to conventional models, this method provides analytical expressions that are easier to communicate and manipulate algebraically.
Article
Mathematics, Interdisciplinary Applications
Lluc Font-Pomarol, Angelo Piga, Rosa Maria Garcia-Teruel, Sergio Nasarre-Aznar, Marta Sales-Pardo, Roger Guimera
Summary: Laws and legal decision-making continuously adapt to new social paradigms, reflecting changes in culture and social norms. Using an information-theoretic approach, we track trends in judicial decisions to identify periods of disruptive topics. Analyzing over 100,000 Spanish court decisions, we detect an abrupt change in housing-related decisions around 2016. Our approach allows us to interpret the results in terms of legislative changes, landmark decisions, and social movements.
Article
Multidisciplinary Sciences
Toni Valles-Catala, Ramon Palau
Summary: Collaborative learning has been advocated as an effective learning methodology for its positive effects on effectiveness, learning types, and educational and social values. Researchers have developed an algorithm called Minimum Entropy Collaborative Groupings (MECG) based on complex network theory to form heterogeneous groups more effectively. The results show that groups created with MECG are more effective, have lower uncertainty, and are more interrelated and mature.
Article
Multidisciplinary Sciences
Oscar Fajardo-Fontiveros, Ignasi Reichardt, Harry R. De Los Rios, Jordi Duch, Marta Sales-Pardo, Roger Guimera
Summary: Learning analytical models from noisy data is challenging and depends on the noise level. The authors analyze the transition of the model-learning problem from a low-noise phase to a phase where the noise is too high for the model to be learned. They also estimate upper bounds for the transition noise.
NATURE COMMUNICATIONS
(2023)
Article
Mathematics, Interdisciplinary Applications
Lluis Danus, Carles Muntaner, Alexander Krauss, Marta Sales-Pardo, Roger Guimera
Summary: Scientists collaborate through intricate networks, which are influenced by funding, institutional arrangements, and cultural factors. We compared the collaboration networks of prominent researchers in North America and Europe and found that European researchers have denser networks, while those in North America have more decentralized networks. The impact of publications by North American researchers is significantly higher than that of European researchers, even when collaborating with other prominent researchers.
Article
Multidisciplinary Sciences
Sergio Cobo-Lopez, Vinod K. Gupta, Jaeyun Sung, Roger Guimera, Marta Sales-Pardo
Summary: This study reveals the robust structural patterns underlying the human gut microbiome using whole metagenomic datasets. The taxonomic composition of the gut microbiome is associated with a combination of generalist and specialist species, which play distinct ecological roles. The findings suggest that there is a nested structure within the gut microbiomes of individuals.
