Community Structures in Bipartite Networks: A Dual-Projection Approach

Identifying communities or clusters in networked systems has received much attention across the physical and social sciences. Most of this work focuses on single layer or one-mode networks, including social networks between people or hyperlinks between websites. Multilayer or multi-mode networks, such as affiliation networks linking people to organizations, receive much less attention in this literature. Common strategies for discovering the community structure of multi-mode networks identify the communities of each mode simultaneously. Here I show that this combined approach is ineffective at discovering community structures when there are an unequal number of communities between the modes of a multi-mode network. I propose a dual-projection alternative for detecting communities in multi-mode networks that overcomes this shortcoming. The evaluation of synthetic networks with known community structures reveals that the dual-projection approach outperforms the combined approach when there are a different number of communities in the various modes. At the same time, results show that the dual-projection approach is as effective as the combined strategy when the number of communities is the same between the modes.     

Structure–stability relationships in networks combining mutualistic and antagonistic interactions

The relationship between the structure of ecological networks and community stability has been studied for decades. Recent developments highlighted that this relationship depended on whether interactions were antagonistic or mutualistic. Different structures promoting stability in different types of ecological networks, i.e. mutualistic or antagonistic, have been pointed out. However, these findings come from studies considering mutualistic and antagonistic interactions separately whereas we know that species are part of both types of networks simultaneously. Understanding the relationship between network structure and community stability, when mutualistic and antagonistic interactions are merged in a single network, thus appears as the next challenge to improve our understanding of the dynamics of natural communities. Using a theoretical approach, we test whether the structural characteristics known to promote stability in networks made of a single interaction type still hold for network merging mutualistic and antagonistic interactions. We show that the effects of diversity and connectance remain unchanged. But the effects of nestedness and modularity are strongly weakened in networks combining mutualistic and antagonistic interactions. By challenging the stabilizing mechanisms proposed for networks with a single interaction type, our study calls for new measures of structure for networks that integrate the diversity of interaction.     

Spotlight: Assembly of protein complexes by integrating graph clustering methods

As is generally assumed, clusters in protein–protein interaction (PPI) networks perform specific, crucial functions in biological systems. Various network community detection methods have been developed to exploit PPI networks in order to identify protein complexes and functional modules. Due to the potential role of various regulatory modes in biological networks, a single method may just apply a single graph property and neglect communities highlighted by other network properties.     

Optimized Null Model for Protein Structure Networks

Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models.     

种间互作网络的结构、生态系统功能及稳定性机制研究

生态群落中不同物种间发生多样化的相互作用, 形成了复杂的种间互作网络。复杂生态网络的结构如何影响群落的生态系统功能及稳定性是群落生态学的核心问题之一。种间互作直接影响到物质和能量在生态系统不同组分之间的流动和循环以及群落构建过程, 使得网络结构与生态系统功能和群落稳定性密切相关。在群落及生态系统水平上开展种间互作网络研究将为群落的构建机制、生物多样性维持、生态系统稳定性、物种协同进化和性状分化等领域提供新的视野。当前生物多样性及生态系统功能受到全球变化的极大影响, 研究种间互作网络的拓扑结构、构建机制、稳定性和生态功能也可为生物多样性的保护和管理提供依据。该文从网络结构、构建机制、网络结构和稳定性关系、种间互作对生态系统功能的影响等4个方面综述当前种间网络研究进展, 并提出在今后的研究中利用机器学习和多层网络等来探究环境变化对种间互作网络结构和功能的影响, 并实现理论和实证研究的有效整合。     

Detecting behavioural changes in human movement to inform the spatial scale of interventions against COVID-19

On March 23 2020, the UK enacted an intensive, nationwide lockdown to mitigate transmission of COVID-19. As restrictions began to ease, more localized interventions were used to target resurgences in transmission. Understanding the spatial scale of networks of human interaction, and how these networks change over time, is critical to targeting interventions at the most at-risk areas without unnecessarily restricting areas at low risk of resurgence. We use detailed human mobility data aggregated from Facebook users to determine how the spatially-explicit network of movements changed before and during the lockdown period, in response to the easing of restrictions, and to the introduction of locally-targeted interventions. We also apply community detection techniques to the weighted, directed network of movements to identify geographically-explicit movement communities and measure the evolution of these community structures through time. We found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown, a change that disproportionately affected long distance connections central to the mobility network. We also found that the community structure of areas in which locally-targeted interventions were implemented following epidemic resurgence did not show reorganization of community structure but did show small decreases in indicators of travel outside of local areas. We propose that communities detected using Facebook or other mobility data be used to assess the impact of spatially-targeted restrictions and may inform policymakers about the spatial extent of human movement patterns in the UK. These data are available in near real-time, allowing quantification of changes in the distribution of the population across the UK, as well as changes in travel patterns to inform our understanding of the impact of geographically-targeted interventions.     

Nonparametric sparsification of complex multiscale networks

Many real-world networks tend to be very dense. Particular examples of interest arise in the construction of networks that represent pairwise similarities between objects. In these cases, the networks under consideration are weighted, generally with positive weights between any two nodes. Visualization and analysis of such networks, especially when the number of nodes is large, can pose significant challenges which are often met by reducing the edge set. Any effective "sparsification" must retain and reflect the important structure in the network. A common method is to simply apply a hard threshold, keeping only those edges whose weight exceeds some predetermined value. A more principled approach is to extract the multiscale "backbone" of a network by retaining statistically significant edges through hypothesis testing on a specific null model, or by appropriately transforming the original weight matrix before applying some sort of threshold. Unfortunately, approaches such as these can fail to capture multiscale structure in which there can be small but locally statistically significant similarity between nodes. In this paper, we introduce a new method for backbone extraction that does not rely on any particular null model, but instead uses the empirical distribution of similarity weight to determine and then retain statistically significant edges. We show that our method adapts to the heterogeneity of local edge weight distributions in several paradigmatic real world networks, and in doing so retains their multiscale structure with relatively insignificant additional computational costs. We anticipate that this simple approach will be of great use in the analysis of massive, highly connected weighted networks.     

Dynamics and Control of Diseases in Networks with Community Structure

The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.     

Mesoscopic structure conditions the emergence of cooperation on social networks

Background

We study the evolutionary Prisoner''s Dilemma on two social networks substrates obtained from actual relational data.

Methodology/Principal Findings

We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement with the observations in both real substrates.

Conclusion

Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.     

The specialization and structure of antagonistic and mutualistic networks of beetles on rainforest canopy trees

Different kinds of species interactions can lead to different structures within ecological networks. Antagonistic interactions (such as between herbivores and host plants) often promote increasing host specificity within a compartmentalized network structure, whereas mutualistic networks (such as pollination networks) are associated with higher levels of generalization and form nested network structures. However, we recently showed that the host specificity of flower‐visiting beetles from three different feeding guilds (herbivores, fungivores, and predators) in an Australian rainforest canopy was equal to that of herbivores on leaves, suggesting that antagonistic herbivores on leaves are no more specialized than flower‐visitors. We therefore set out to test whether similarities in the host specificity of these different assemblages reflect similarities in underlying network structures. As shown before at the species level, mutualistic communities on flowers showed levels of specialization at the network scale similar to those of the antagonistic herbivore community on leaves. However, the network structure differed, with flower‐visiting assemblages displaying a significantly more nested structure than folivores, and folivores displaying a significantly more compartmentalized structure than flower‐visitors. These results, which need further testing in other forest systems, demonstrate that both antagonistic and mutualistic interactions can result in equally high levels of host specialization among beetle assemblages in tropical rainforests. If this is a widespread phenomenon, it may alter our current perceptions of food web dynamics, species diversity patterns, and co‐evolution in tropical rainforests. © 2014 The Linnean Society of London, Biological Journal of the Linnean Society, 2015, 114 , 287–295.     

Nested liana-tree network in three distinct neotropical vegetation formations

Despite the increasing number of studies on lianas, few of them have focused on liana and host-tree (phorophyte) interactions from a network perspective. Most studies found some network structure in other systems, such as plant facilitation and host-epiphyte. However, a recent study found no structure in a small network of liana–phorophyte interactions. Our aim was to investigate the hypothesis that rich, highly diverse systems yield large interaction networks with some structure. If so, networks of liana–phorophyte interactions in highly diverse systems will have one or more of the following structures: compartmentalized, nested or compound. We sampled three highly diverse vegetation formations: a tropical rainforest, a tropical seasonally dry forest, and a woodland savanna, all in southeastern Brazil. We used simulated annealing to test compartmentalization and found no compartment in any of the three networks analyzed. By means of a modified classical temperature index, we found a nested structure in all three sites sampled. We inferred that these nested structures might result from phorophyte characteristics and sequential colonization by different liana species and might promote increased diversity in tropical tree formations. We propose that, according to the system complexity and the different variables associated with site and liana–phorophyte characteristics, a network may have a structure, which arises in more complex systems. Since we have investigated highly diverse systems with large networks, nestedness could be clearly detected in our study.     

The utrophin actin-binding domain binds F-actin in two different modes: implications for the spectrin superfamily of proteins

Utrophin, like its homologue dystrophin, forms a link between the actin cytoskeleton and the extracellular matrix. We have used a new method of image analysis to reconstruct actin filaments decorated with the actin-binding domain of utrophin, which contains two calponin homology domains. We find two different modes of binding, with either one or two calponin-homology (CH) domains bound per actin subunit, and these modes are also distinguishable by their very different effects on F-actin rigidity. Both modes involve an extended conformation of the CH domains, as predicted by a previous crystal structure. The separation of these two modes has been largely dependent upon the use of our new approach to reconstruction of helical filaments. When existing information about tropomyosin, myosin, actin-depolymerizing factor, and nebulin is considered, these results suggest that many actin-binding proteins may have multiple binding sites on F-actin. The cell may use the modular CH domains found in the spectrin superfamily of actin-binding proteins to bind actin in manifold ways, allowing for complexity to arise from the interactions of a relatively few simple modules with actin.     

动态神经网络中的同步振荡

目前有一种假设认为同一视觉对象是由一群神经元的同步振荡活动来表征的。这一神经元发放活动的时间特性,是解决视觉信息处理中“结合问题（Ｂｉｎｄｉｎｇｐｒｏｂｌｅｍ）”的可能机制。本文用我们所提出的一种简化现实性神经网络模型［１］所构造的时滞非线性振子网络［２］,模拟生物神经网络的同步振荡活动。并考虑了振子各参数的设置与振荡活动的关系,以及网络振子间耦联对同步活动的影响．     

A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic Lake Mendota,WI, USA

With an unprecedented decade-long time series from a temperate eutrophic lake, we analyzed bacterial and environmental co-occurrence networks to gain insight into seasonal dynamics at the community level. We found that (1) bacterial co-occurrence networks were non-random, (2) season explained the network complexity and (3) co-occurrence network complexity was negatively correlated with the underlying community diversity across different seasons. Network complexity was not related to the variance of associated environmental factors. Temperature and productivity may drive changes in diversity across seasons in temperate aquatic systems, much as they control diversity across latitude. While the implications of bacterioplankton network structure on ecosystem function are still largely unknown, network analysis, in conjunction with traditional multivariate techniques, continues to increase our understanding of bacterioplankton temporal dynamics.     

The overlapping community structure of structural brain network in young healthy individuals

Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human brain networks. Here, using regional gray matter volume, we investigated the structural brain network among 90 brain regions (according to a predefined anatomical atlas) in 462 young, healthy individuals. Overlapped nodes between communities were defined by assuming that nodes (brain regions) can belong to more than one community. We demonstrated that 90 brain regions were organized into 5 overlapping communities associated with several well-known brain systems, such as the auditory/language, visuospatial, emotion, decision-making, social, control of action, memory/learning, and visual systems. The overlapped nodes were mostly involved in an inferior-posterior pattern and were primarily related to auditory and visual perception. The overlapped nodes were mainly attributed to brain regions with higher node degrees and nodal efficiency and played a pivotal role in the flow of information through the structural brain network. Our results revealed fuzzy boundaries between communities by identifying overlapped nodes and provided new insights into the understanding of the relationship between the structure and function of the human brain. This study provides the first report of the overlapping community structure of the structural network of the human brain.     

Structural distance and evolutionary relationship of networks

Exploring common features and universal qualities shared by a particular class of networks in biological and other domains is one of the important aspects of evolutionary study. In an evolving system, evolutionary mechanism can cause functional changes that forces the system to adapt to new configurations of interaction pattern between the components of that system (e.g. gene duplication and mutation play a vital role for changing the connectivity structure in many biological networks. The evolutionary relation between two systems can be retraced by their structural differences). The eigenvalues of the normalized graph Laplacian not only capture the global properties of a network, but also local structures that are produced by graph evolutions (like motif duplication or joining). The spectrum of this operator carries many qualitative aspects of a graph. Given two networks of different sizes, we propose a method to quantify the topological distance between them based on the contrasting spectrum of normalized graph Laplacian. We find that network architectures are more similar within the same class compared to between classes. We also show that the evolutionary relationships can be retraced by the structural differences using our method. We analyze 43 metabolic networks from different species and mark the prominent separation of three groups: Bacteria, Archaea and Eukarya. This phenomenon is well captured in our findings that support the other cladistic results based on gene content and ribosomal RNA sequences. Our measure to quantify the structural distance between two networks is useful to elucidate evolutionary relationships.     

An effective structure learning method for constructing gene networks

MOTIVATION: Bayesian network methods have shown promise in gene regulatory network reconstruction because of their capability of capturing causal relationships between genes and handling data with noises found in biological experiments. The problem of learning network structures, however, is NP hard. Consequently, heuristic methods such as hill climbing are used for structure learning. For networks of a moderate size, hill climbing methods are not computationally efficient. Furthermore, relatively low accuracy of the learned structures may be observed. The purpose of this article is to present a novel structure learning method for gene network discovery. RESULTS: In this paper, we present a novel structure learning method to reconstruct the underlying gene networks from the observational gene expression data. Unlike hill climbing approaches, the proposed method first constructs an undirected network based on mutual information between two nodes and then splits the structure into substructures. The directional orientations for the edges that connect two nodes are then obtained by optimizing a scoring function for each substructure. Our method is evaluated using two benchmark network datasets with known structures. The results show that the proposed method can identify networks that are close to the optimal structures. It outperforms hill climbing methods in terms of both computation time and predicted structure accuracy. We also apply the method to gene expression data measured during the yeast cycle and show the effectiveness of the proposed method for network reconstruction.     

Interactive effects of warming,eutrophication and size structure: impacts on biodiversity and food‐web structure

Warming and eutrophication are two of the most important global change stressors for natural ecosystems, but their interaction is poorly understood. We used a dynamic model of complex, size‐structured food webs to assess interactive effects on diversity and network structure. We found antagonistic impacts: Warming increases diversity in eutrophic systems and decreases it in oligotrophic systems. These effects interact with the community size structure: Communities of similarly sized species such as parasitoid–host systems are stabilized by warming and destabilized by eutrophication, whereas the diversity of size‐structured predator–prey networks decreases strongly with warming, but decreases only weakly with eutrophication. Nonrandom extinction risks for generalists and specialists lead to higher connectance in networks without size structure and lower connectance in size‐structured communities. Overall, our results unravel interactive impacts of warming and eutrophication and suggest that size structure may serve as an important proxy for predicting the community sensitivity to these global change stressors.