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The construction of minimal metabolic networks contributes to our understanding of metabolism and biotechnological processes. Here, Jansen et al. have defined a class of genes that must be retained to ensure cell viability and rapid growth.

Group interactions can lead to explosive onsets of collective behaviors in biological and sociotechnological systems. Here, the authors show that it is the overlap between these kind of higher-order interactions that drives whether emergence of synchrony and epidemics shows up smoothly or abruptly.

We explore the world in search of novelties, which are traditionally defined as the first appearance of a new element in a sequence of exploration. The authors show that novelty can also arise from combining already known elements, introducing the concept of higher-order novelties as the first appearance of such combinations in the sequence. They propose a measure and model to study the dynamics of these higher-order novelties.

The evolution of networks with structure changing in time is dependent on their past states and relevant to diffusion and spreading processes. The authors show that temporal network’s memory is described by multidimensional patterns at a microscopic scale, and cannot be reduced to a scalar quantity.

Higher-order interactions are broadly present in biological and social networks, however patterns of such interaction are challenging to recover from observed data. The authors propose a method to infer the high-order structural connectivity of a complex system from its time evolution.

Network memory impacts dynamical processes emerging in real-world social systems, however little is known about memory of temporal networks beyond pairwise interactions. The authors develop a framework to characterize the temporal organization of higher-order networks and propose a model of temporal hypergraphs with higher-order memory to reproduce the patterns emerging in real-world complex systems.

Social contagion cannot only be understood in terms of pairwise interactions among individuals. Here, the authors include higher-order social interactions, the effects of groups, in their model of social contagion, enabling insight into why critical masses are required to initiate social changes.

For most actors sustained productivity defines success. Here the authors study the careers of actors and identify a "rich-get-richer" mechanism with respect to productivity, the emergence of hot streaks and the presence of gender bias, and are able to predict whether the most productive year of an actor is yet to come.

Communication networks and power grids may be subject to cascading failures which can lead to outages. Here the authors propose to investigate cascades using dynamical transients of electrical power grids, thereby identifying possible vulnerabilities that might remain undetected with any static approach.

Multilayer networks have been used to capture the structure of complex systems with different types of interactions, but often contain redundant information. Here, De Domenico et al. present a method based on quantum information, to identify the minimal configuration of layers to retain.

Here the authors introduce dual communities, characterized by strong connections at their boundaries, and show that they are formed as a trade-off between efficiency and resilience in supply networks.

Alvarez-Rodriguez et al. examine group interactions by means of higher-order social networks. They propose a theoretical framework for studying real-world interactions and provide a case study of collaboration in science and technology.

A single damage can lead to a complete collapse of supply networks due to a cascading failure mechanism. Kaiser et al. show that by adding new connections network isolators can be created, that can inhibit failure spreading relevant for power grids and water transmission systems.

Systemic risk and bank bailout approaches have been the source of discussions on scientific, financial and governmental forums. An artificial intelligence technique is proposed to inform equitable bailout decisions that minimise taxpayers’ losses.

Network representations of complex systems are limited to pairwise interactions, but real-world systems often involve higher-order interactions. This Perspective looks at the new physics emerging from attempts to characterize these interactions.

There is increasing evidence that many higher-order interactions in complex systems are directed. Here, the authors provide a tensorial formalism for directed hypergraphs and investigate their synchronization properties generalizing the Master Stability Function approach.

Friends making eye-contact have higher inter-brain synchronization than strangers. Eye-contact affects neural synchronization between brains more than within a brain, highlighting that eye-contact is an inherently social signal.

While interdisciplinary research is a growing trend among scientists in all disciplines, recent evidence suggests such a career choice is usually penalized by lower citations and funding. Here, the authors look into research funding data from the UK research councils, and show that interdisciplinary researchers actually outperform monodisciplinary researchers in terms of long-term funding performance.

Group interactions can dramatically alter social contagion dynamics and lead to the emergence of new phenomena like abrupt transitions and critical mass effects. The authors develop an approximate master equation framework to analytically describe contagion in heterogeneous hypergraphs and study the impact of large influential groups in seeding and sustaining epidemics.

Recent studies have shown that complex systems are often best represented by generalized networks such as hypergraphs, multilayer networks, and temporal networks. Here, the authors propose a unified framework to investigate cluster synchronization patterns in generalized networks and demonstrate the existence of chimera states that emerge exclusively in the presence of higher-order interactions.