An ecological approach to structural flexibility in online communication systems

Structural dynamics of plant-pollinator mutualistic networks

The discourse surrounding the structural organization of mutualistic interactions mostly revolves around modularity and nestedness. The former is known to enhance the stability of communities, while the latter is related to their feasibility, albeit compromising the stability. However, it has recently been shown that the joint emergence of these structures poses challenges that can eventually lead to limitations in the dynamic properties of mutualistic communities. We hypothesize that considering compound arrangements-modules with internal nested organization-can offer valuable insights in this debate. We analyze the temporal structural dynamics of 20 plant-pollinator interaction networks and observe large structural variability throughout the year. Compound structures are particularly prevalent during the peak of the pollination season, often coexisting with nested and modular arrangements in varying degrees. Motivated by these empirical findings, we synthetically investigate the dynamics of the structural patterns across two control parameters-community size and connectance levels-mimicking the progression of the pollination season. Our analysis reveals contrasting impacts on the stability and feasibility of these mutualistic communities. We characterize the consistent relationship between network structure and stability, which follows a monotonic pattern. But, in terms of feasibility, we observe nonlinear relationships. Compound structures exhibit a favorable balance between stability and feasibility, particularly in mid-sized ecological communities, suggesting they may effectively navigate the simultaneous requirements of stability and feasibility. These findings may indicate that the assembly process of mutualistic communities is driven by a delicate balance among multiple properties, rather than the dominance of a single one.

Review on strategies for improving the added value and expanding the scope of CO2 electroreduction products

The electrochemical reduction of CO2 into value-added chemicals has been explored as a promising solution to realize carbon neutrality and inhibit global warming. This involves utilizing the electrochemical CO2 reduction reaction (CO2RR) to produce a variety of single-carbon (C1) and multi-carbon (C2+) products. Additionally, the electrolyte solution in the CO2RR system can be enriched with nitrogen sources (such as NO3-, NO2-, N2, or NO) to enable the synthesis of organonitrogen compounds via C-N coupling reactions. However, the electrochemical conversion of CO2 into valuable chemicals still faces challenges in terms of low product yield, poor faradaic efficiency (FE), and unclear understanding of the reaction mechanism. This review summarizes the promising strategies aimed at achieving selective production of diverse carbon-containing products, including CO, formate, hydrocarbons, alcohols, and organonitrogen compounds. These approaches involve the rational design of electrocatalysts and the construction of coupled electrocatalytic reaction systems. Moreover, this review presents the underlying reaction mechanisms, identifies the existing challenges, and highlights the prospects of the electrosynthesis processes. The aim is to offer valuable insights and guidance for future research on the electrocatalytic conversion of CO2 into carbon-containing products of enhanced value-added potential.

An Entropic Analysis of Social Demonstrations

Social media has dramatically influenced how individuals and groups express their demands, concerns, and aspirations during social demonstrations. The study of X or Twitter hashtags during those events has revealed the presence of some temporal points characterised by high correlation among their participants. It has also been reported that the connectivity presents a modular-to-nested transition at the point of maximum correlation. The present study aims to determine whether it is possible to characterise this transition using entropic-based tools. Our results show that entropic analysis can effectively find the transition point to the nested structure, allowing researchers to know that the transition occurs without the need for a network representation. The entropic analysis also shows that the modular-to-nested transition is characterised not by the diversity in the number of hashtags users post but by how many hashtags they share.

Signs of criticality in social explosions

The success of an on-line movement could be defined in terms of the shift to large-scale and the later off-line massive street actions of protests. The role of social media in this process is to facilitate the transformation from small or local feelings of disagreement into large-scale social actions. The way how social media achieves that effect is by growing clusters of people and groups with similar effervescent feelings, which otherwise would not be in touch with each other. It is natural to think that these kinds of macro social actions, as a consequence of the spontaneous and massive interactions, will attain the growth and divergence of those clusters, like the correlation length of statistical physics, giving rise to important simplifications on several statistics. In this work, we report the presence of signs of criticality in social demonstrations. Namely, similar power-law exponents are found whenever the distributions are calculated either considering time windows of the same length or with the same number of hashtag usages. In particular, the exponents for the distributions during the event were found to be smaller than before the event, and this is also observed either if we count the hashtags only once per user or if all their usages are considered. By means of network representations, we show that the systems present two kinds of high connectedness, characterised by either high or low values of modularity. The importance of analysing systems near a critical point is that any small disturbance can escalate and induce large-scale-nationwide-chain reactions.

Empirical social triad statistics can be explained with dyadic homophylic interactions

Social stability is often associated with triangular interactions between people. Various possible social triangles appear in peculiar ratios. The triangles “The friend of my friend is my friend” and “The enemy of my friend is my enemy” are strongly overrepresented, which plays an important role for social balance. A standard explanation for these characteristic triangle fractions is that people consider triadic information before forming social relations. This assumption often contradicts everyday experience. We propose an explanation of the observed overrepresentations without individuals having to consider triangles. A society where individuals minimize their social stress self-organizes toward the empirically observed triangular structures. We demonstrate this with data from a society of computer game players, where triangle formation can be directly observed.

The remarkable robustness of many social systems has been associated with a peculiar triangular structure in the underlying social networks. Triples of people that have three positive relations (e.g., friendship) between each other are strongly overrepresented. Triples with two negative relations (e.g., enmity) and one positive relation are also overrepresented, and triples with one or three negative relations are drastically suppressed. For almost a century, the mechanism behind these very specific (“balanced”) triad statistics remained elusive. Here, we propose a simple realistic adaptive network model, where agents tend to minimize social tension that arises from dyadic interactions. Both opinions of agents and their signed links (positive or negative relations) are updated in the dynamics. The key aspect of the model resides in the fact that agents only need information about their local neighbors in the network and do not require (often unrealistic) higher-order network information for their relation and opinion updates. We demonstrate the quality of the model on detailed temporal relation data of a society of thousands of players of a massive multiplayer online game where we can observe triangle formation directly. It not only successfully predicts the distribution of triangle types but also explains empirical group size distributions, which are essential for social cohesion. We discuss the details of the phase diagrams behind the model and their parameter dependence, and we comment on to what extent the results might apply universally in societies.