The probability of edge existence due to node degree: a baseline for network-based predictions.

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections using network permutation to generate features that depend only on degree. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Researchers seeking to predict new or missing edges in biological networks should use our permutation approach to obtain a baseline for performance that may be nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).

Joint masking and self-supervised strategies for inferring small molecule-miRNA associations.

Inferring small molecule-miRNA associations (MMAs) is crucial for revealing the intricacies of biological processes and disease mechanisms. Deep learning, renowned for its exceptional speed and accuracy, is extensively used for predicting MMAs. However, given their heavy reliance on data, inaccuracies during data collection can make these methods susceptible to noise interference. To address this challenge, we introduce the joint masking and self-supervised (JMSS)-MMA model. This model synergizes graph autoencoders with a probability distribution-based masking strategy, effectively countering the impact of noisy data and enabling precise predictions of unknown MMAs. Operating in a self-supervised manner, it deeply encodes the relationship data of small molecules and miRNA through the graph autoencoder, delving into its latent information. Our masking strategy has successfully reduced data noise, enhancing prediction accuracy. To our knowledge, this is the pioneering integration of a masking strategy with graph autoencoders for MMA prediction. Furthermore, the JMSS-MMA model incorporates a node-degree-based decoder, deepening the understanding of the network's structure. Experiments on two mainstream datasets confirm the model's efficiency and precision, and ablation studies further attest to its robustness. We firmly believe that this model will revolutionize drug development, personalized medicine, and biomedical research.

Thinner inner retinal layers are associated with lower cognitive performance, lower brain volume, and altered white matter network structure-The Maastricht Study.

INTRODUCTION: The retina may provide non-invasive, scalable biomarkers for monitoring cerebral neurodegeneration. METHODS: We used cross-sectional data from The Maastricht study (n = 3436; mean age 59.3 years; 48% men; and 21% with type 2 diabetes [the latter oversampled by design]). We evaluated associations of retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses with cognitive performance and magnetic resonance imaging indices (global grey and white matter volume, hippocampal volume, whole brain node degree, global efficiency, clustering coefficient, and local efficiency). RESULTS: After adjustment, lower thicknesses of most inner retinal layers were significantly associated with worse cognitive performance, lower grey and white matter volume, lower hippocampal volume, and worse brain white matter network structure assessed from lower whole brain node degree, lower global efficiency, higher clustering coefficient, and higher local efficiency. DISCUSSION: The retina may provide biomarkers that are informative of cerebral neurodegenerative changes in the pathobiology of dementia.

Multi-Copy Relay Node Selection Strategy Based on Reinforcement Learning.

Delay tolerant networks (DTNs), are characterized by their difficulty in establishing end-to-end paths and and large message propagation delays. To control network overhead costs, reduce message delays, and improve delivery rates in DTNs, it is essential to not only delete messages that have reached their destination but also to more precisely determine appropriate relay nodes. Based on the above goals, this paper constructs a multi-copy relay node selection router algorithm based on Q-lambda reinforcement learning with reference to the idea of community division (QLCR). In community division, if a node has the highestdegree, it is considered the core node, and nodes with similar interests and structural properties are divided into a community. Node degree refers to the number of nodes associated with the node, indicating its importance in the network. Structural similarity determines the distance between nodes. The selection of relay nodes considers node degree, interests, and structural similarity. The Q-lambda reinforcement learning algorithm enables each node to learn from the entire network, setting corresponding reward values based on encountered nodes meeting the specified conditions. Through iterative processes, the node with the most cumulative reward value is chosen as the final relay node. Experimental results demonstrate that the proposed algorithm achieves a high delivery rate while maintaining low network overhead and delay.

NAToRA, a relatedness-pruning method to minimize the loss of dataset size in genetic and omics analyses.

Genetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness cannot be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to complex datasets. When compared with two other popular population genetics methodologies (PLINK and KING), NAToRA shows the best combination of removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar effects on the allele frequency spectrum and Principal Component Analysis than PLINK and KING. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also release a genealogies simulator software used for different tests performed in this study.

Probing the association between resting-state brain network dynamics and psychological resilience.

This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience, that is, the ability to retain mental health in the face of stress and adversity. To this end, we used multiband resting-state BOLD fMRI (TR = .675 sec) from 52 participants who had filled out three psychological questionnaires assessing resilience. Time-resolved functional connectivity was calculated by performing a sliding window approach on averaged time series parcellated according to different established atlases. Multilayer modularity detection was performed to track network reconfigurations over time, and node flexibility was calculated as the number of times a node changes community assignment. In addition, node promiscuity (the fraction of communities a node participates in) and node degree (as proxy for time-varying connectivity) were calculated to extend previous work. We found no substantial correlations between resilience and node flexibility. We observed a small number of correlations between the two other brain measures and resilience scores that were, however, very inconsistently distributed across brain measures, differences in temporal sampling, and parcellation schemes. This heterogeneity calls into question the existence of previously postulated associations between resilience and brain network flexibility and highlights how results may be influenced by specific analysis choices.

MEG Node Degree Differences in Patients with Focal Epilepsy vs. Controls-Influence of Experimental Conditions.

Drug-resistant epilepsy can be most limiting for patients, and surgery represents a viable therapy option. With the growing research on the human connectome and the evidence of epilepsy being a network disorder, connectivity analysis may be able to contribute to our understanding of epilepsy and may be potentially developed into clinical applications. In this magnetoencephalographic study, we determined the whole-brain node degree of connectivity levels in patients and controls. Resting-state activity was measured at five frequency bands in 15 healthy controls and 15 patients with focal epilepsy of different etiologies. The whole-brain all-to-all imaginary part of coherence in source space was then calculated. Node degree was determined and parcellated and was used for further statistical evaluation. In comparison to controls, we found a significantly higher overall node degree in patients with lesional and non-lesional epilepsy. Furthermore, we examined the conditions of high/reduced vigilance and open/closed eyes in controls, to analyze whether patient node degree levels can be achieved. We evaluated intraclass-correlation statistics (ICC) to evaluate the reproducibility. Connectivity and specifically node degree analysis could present new tools for one of the most common neurological diseases, with potential applications in epilepsy diagnostics.

Identifying cancer specific signaling pathways based on the dysregulation between genes.

A large collection of studies has shown that the occurrence of cancer is related to the functional dysfunction of the pathways. Identification of cancer-related pathways could help researchers understand the mechanisms of complex diseases well. Whereas, most current signaling pathway analysis methods take no account of the gene interaction variations within pathways. Furthermore, considering that some pathways have connection with two or more cancer types, while some are likely to be cancer-type specific pathways. Identifying cancer-type specific pathways contributes to interpreting the different mechanisms of different cancer types. In this study, we first proposed a pathway analysis method named Pathway Analysis of Intergenic Regulation (PAIGR) to identify pathways with dysregulation between genes and compared the performance of this method with four existing methods on four colorectal cancer (CRC) datasets. The results showed that PAIGR could find cancer-related pathways more accurately. Moreover, in order to explore the relationship between the identified pathways and the cancer type, we constructed a pathway interaction network, in which nodes and edges represented pathways and interactions between pathways respectively. Highly connected pathways were considered to play a central role in an extensive range of biological processes, while sparsely connected pathways are considered to have certain specificity. Our results showed that pathways identified by PAIGR had a low nodal degree (i.e., a few numbers of interactions), which suggested that most of these pathways were cancer-type specific.

Evolution of Protein Domain Architectures.

This chapter reviews current research on how protein domain architectures evolve. We begin by summarizing work on the phylogenetic distribution of proteins, as this will directly impact which domain architectures can be formed in different species. Studies relating domain family size to occurrence have shown that they generally follow power law distributions, both within genomes and larger evolutionary groups. These findings were subsequently extended to multi-domain architectures. Genome evolution models that have been suggested to explain the shape of these distributions are reviewed, as well as evidence for selective pressure to expand certain domain families more than others. Each domain has an intrinsic combinatorial propensity, and the effects of this have been studied using measures of domain versatility or promiscuity. Next, we study the principles of protein domain architecture evolution and how these have been inferred from distributions of extant domain arrangements. Following this, we review inferences of ancestral domain architecture and the conclusions concerning domain architecture evolution mechanisms that can be drawn from these. Finally, we examine whether all known cases of a given domain architecture can be assumed to have a single common origin (monophyly) or have evolved convergently (polyphyly). We end by a discussion of some available tools for computational analysis or exploitation of protein domain architectures and their evolution.

Connectome-Based Biomarkers Predict Subclinical Depression and Identify Abnormal Brain Connections With the Lateral Habenula and Thalamus.

Subclinical depression (SD) has been considered as the precursor to major depressive disorder. Accurate prediction of SD and identification of its etiological origin are urgent. Bursts within the lateral habenula (LHb) drive depression in rats, but whether dysfunctional LHb is associated with SD in human is unknown. Here we develop connectome-based biomarkers which predict SD and identify dysfunctional brain regions and connections. T1 weighted images and resting-state functional MRI (fMRI) data were collected from 34 subjects with SD and 40 healthy controls (HCs). After the brain is parcellated into 48 brain regions (246 subregions) using the human Brainnetome Atlas, the functional network of each participant is constructed by calculating the correlation coefficient for the time series of fMRI signals of each pair of subregions. Initial candidates of abnormal connections are identified by the two-sample t-test and input into Support Vector Machine models as features. A total of 24 anatomical-region-based models, 231 sliding-window-based models, and 100 random-selection-based models are built. The performance of these models is estimated through leave-one-out cross-validation and evaluated by measures of accuracy, sensitivity, confusion matrix, receiver operating characteristic curve, and the area under the curve (AUC). After confirming the region with the highest accuracy, subregions within the thalamus and connections associated with subregions of LHb are compared. It is found that five prediction models using connections of the thalamus, posterior superior temporal sulcus, cingulate gyrus, superior parietal lobule, and superior frontal gyrus achieve an accuracy >0.9 and an AUC >0.93. Among 90 abnormal connections associated with the thalamus, the subregion of the right posterior parietal thalamus where LHb is located has the most connections (n = 18), the left subregion only has 3 connections. In SD group, 10 subregions in the thalamus have significantly different node degrees with those in the HC group, while 8 subregions have lower degrees ( p < 0.01), including the one with LHb. These results implicate abnormal brain connections associated with the thalamus and LHb to be associated with SD. Integration of these connections by machine learning can provide connectome-based biomarkers to accurately diagnose SD.

Diversity in the structure of action potential-mediated neural connectivity within rat supra chiasmatic nucleus.

Action potential (AP)-mediated cell-to-cell communication is essential for the frequency-locking and phase-synchronization of the clock cells within the biological master clock, suprachiasmatic nucleus (SCN). Nevertheless, the morphology of its network connectivity is largely unexplored. Here, with an optimized optogenetic light-stimulation and scanning protocol, we report some key characteristics of the inhibitory receptive field (IRF), the area which brings inhibitory synaptic currents to a given target cell, and basic statistics of the inhibitory network connections of rat SCN clock cells. ChR2 transfected, slice cultures of rat SCN were stimulated by a blue power LED light in a repetitive box-scanning modes, while a target cell was whole-cell patched. The registered inhibitory postsynaptic currents, which were brought by light-induced APs of presynaptic neurons, were mostly GABAergic. The sizes and shapes of IRFs of SCN cells were very diverse, and the number of presynaptic cells making up the IRF of a given target cell followed an exponential distribution with an average value of 8.9 approximately, according to our clustering analysis which is based on a hybrid measure D, combining the physical distance r and the difference in the current amplitudes of two different sites. Although this estimate inevitably depends on the construct of the measure D, it is found not so sensitive on the parameter w, which weighs the relative significance of the current amplitude different with respect to the physical distance r: The average number of presynaptic neurons varies < 26% over a significant range of 0 < w < 30. On average, the presynaptic connection number density around a target cell falls off as an exponentially decreasing function of r. But, its space constant (~210.7 µm) is quite large that long-range (>210.7 µm) neural connections are abundant (>66.9%) within the SCN.

Improving network inference: The impact of false positive and false negative conclusions about the presence or absence of links.

BACKGROUND: A reliable inference of networks from data is of key interest in the Neurosciences. Several methods have been suggested in the literature to reliably determine links in a network. To decide about the presence of links, these techniques rely on statistical inference, typically controlling the number of false positives, paying little attention to false negatives. NEW METHOD: In this paper, by means of a comprehensive simulation study, we analyse the influence of false positive and false negative conclusions about the presence or absence of links in a network on the network topology. We show that different values to balance false positive and false negative conclusions about links should be used in order to reliably estimate network characteristics. We propose to run careful simulation studies prior to making potentially erroneous conclusion about the network topology. RESULTS: Our analysis shows that optimal values to balance false positive and false negative conclusions about links depend on the network topology and characteristic of interest. COMPARISON WITH EXISTING METHODS: Existing methods rely on a choice of the rate for false positive conclusions. They aim to be sure about individual links rather than the entire network. The rate of false negative conclusions is typically not investigated. CONCLUSIONS: Our investigation shows that the balance of false positive and false negative conclusions about links in a network has to be tuned for any network topology that is to be estimated. Moreover, within the same network topology, the results are qualitatively the same for each network characteristic, but the actual values leading to reliable estimates of the characteristics are different.

Multisensory stimulation improves functional recovery and resting-state functional connectivity in the mouse brain after stroke.

Stroke causes direct structural damage to local brain networks and indirect functional damage to distant brain regions. Neuroplasticity after stroke involves molecular changes within perilesional tissue that can be influenced by regions functionally connected to the site of injury. Spontaneous functional recovery can be enhanced by rehabilitative strategies, which provides experience-driven cell signaling in the brain that enhances plasticity. Functional neuroimaging in humans and rodents has shown that spontaneous recovery of sensorimotor function after stroke is associated with changes in resting-state functional connectivity (RS-FC) within and across brain networks. At the molecular level, GABAergic inhibitory interneurons can modulate brain plasticity in peri-infarct and remote brain regions. Among this cell-type, a decrease in parvalbumin (PV)-immunoreactivity has been associated with improved behavioral outcome. Subjecting rodents to multisensory stimulation through exposure to an enriched environment (EE) enhances brain plasticity and recovery of function after stroke. Yet, how multisensory stimulation relates to RS-FC has not been determined. In this study, we investigated the effect of EE on recovery of RS-FC and behavior in mice after stroke, and if EE-related changes in RS-FC were associated with levels of PV-expressing neurons. Photothrombotic stroke was induced in the sensorimotor cortex. Beginning 2 days after stroke, mice were housed in either standard environment (STD) or EE for 12 days. Housing in EE significantly improved lost tactile-proprioceptive function compared to mice housed in STD environment. RS-FC in the mouse was measured by optical intrinsic signal imaging 14 days after stroke or sham surgery. Stroke induced a marked reduction in RS-FC within several perilesional and remote brain regions. EE partially restored interhemispheric homotopic RS-FC between spared motor regions, particularly posterior secondary motor. Compared to mice housed in STD cages, EE exposure lead to increased RS-FC between posterior secondary motor regions and contralesional posterior parietal and retrosplenial regions. The increased regional RS-FC observed in EE mice after stroke was significantly correlated with decreased PV-immunoreactivity in the contralesional posterior motor region. In conclusion, experimental stroke and subsequent housing in EE induces dynamic changes in RS-FC in the mouse brain. Multisensory stimulation associated with EE enhances RS-FC among distinct brain regions relevant for recovery of sensorimotor function and controlled movements that may involve PV/GABA interneurons. Our results indicate that targeting neural circuitry involving spared motor regions across hemispheres by neuromodulation and multimodal sensory stimulation could improve rehabilitation after stroke.

Integrative Structural Brain Network Analysis in Diffusion Tensor Imaging.

In diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length, and FA values into the connectivity model. Using various node-degree-based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in postinstitutional settings before being adopted by families in the United States.

Correlation of abnormal topological properties of the white matter fibers connecting the left amygdale with psychogenic erectile dysfunction / 中华男科学杂志

Objective@#To explore the topological properties of the degree and strength of nodes in the binary and weighted brain white matter networks of the patients with psychogenic erectile dysfunction (pED) and analyze the changes of myelin integrity, number and length of the white matter fibers in the topological space.@*METHODS@#Diffusion tensor imaging data were obtained from 21 patients with pED and 24 healthy controls matched in sex, age, and years of education and subjected to preprocessing. The whole cerebral cortex was divided into 90 regions, followed by fiber tracking, construction of the binary and weighted white matter networks, and calculation of the node degrees and connectivity strengths in different brain regions. The property values were compared between the two groups using the two-sample t-test, the results were corrected by multiple testing correction, and the correlation of the property values with the erectile function of the patients was subjected to Pearson's correlation analysis.@*RESULTS@#Compared with the healthy controls, the pED patients showed significantly decreased node degree of the left triangular part of inferior frontal gyrus (IFG) (7.54±1.44 vs 5.95±1.28, t = －3.88, corrected P = 0.02), medial orbital part of superior frontal gyrus (SFG) (10.08±3.60 vs 6.29±3.30, t = －3.67, corrected P = 0.02), and amygdala (6.50±2.11 vs 4.29±1.31, t = －4.16, corrected P = 0.01) in the binary networks, as well as the connectivity strength of the left triangular part of IFG (2.50±0.68 vs 1.72±0.50, t = －4.35, corrected P = 0.01), medial orbital part of SFG (3.17±0.97 vs 2.08±1.10, t = －3.53, corrected P = 0.03), and amygdala (1.80±0.69 vs 1.11±0.39, t = －4.03, corrected P = 0.01) in the fractional anisotropy (FA) weighted networks. The node degree of the left amygdala was negatively correlated with the total score (r = －0.47,P = 0.04), second item score (r = －0.46, P = 0.03), and third item score of IIEF-5 (r = －0.45, P = 0.04) in the pED patients.@*CONCLUSIONS@#The myelin integrity of the white matter fibers in the left frontal lobe and amygdale is impaired in pED patients, which leads to the aberrant generation, processing and regulation of their emotions. The decreased pivotal role and importance of the white matter fibers connecting the left amygdale may be associated with pED.

[Correlation of abnormal topological properties of the white matter fibers connecting the left amygdale with psychogenic erectile dysfunction].

OBJECTIVE: To explore the topological properties of the degree and strength of nodes in the binary and weighted brain white matter networks of the patients with psychogenic erectile dysfunction (pED) and analyze the changes of myelin integrity, number and length of the white matter fibers in the topological space. METHODS: Diffusion tensor imaging data were obtained from 21 patients with pED and 24 healthy controls matched in sex, age, and years of education and subjected to preprocessing. The whole cerebral cortex was divided into 90 regions, followed by fiber tracking, construction of the binary and weighted white matter networks, and calculation of the node degrees and connectivity strengths in different brain regions. The property values were compared between the two groups using the two-sample t-test, the results were corrected by multiple testing correction, and the correlation of the property values with the erectile function of the patients was subjected to Pearson's correlation analysis. RESULTS: Compared with the healthy controls, the pED patients showed significantly decreased node degree of the left triangular part of inferior frontal gyrus (IFG) (7.54±1.44 vs 5.95±1.28, t = －3.88, corrected P = 0.02), medial orbital part of superior frontal gyrus (SFG) (10.08±3.60 vs 6.29±3.30, t = －3.67, corrected P = 0.02), and amygdala (6.50±2.11 vs 4.29±1.31, t = －4.16, corrected P = 0.01) in the binary networks, as well as the connectivity strength of the left triangular part of IFG (2.50±0.68 vs 1.72±0.50, t = －4.35, corrected P = 0.01), medial orbital part of SFG (3.17±0.97 vs 2.08±1.10, t = －3.53, corrected P = 0.03), and amygdala (1.80±0.69 vs 1.11±0.39, t = －4.03, corrected P = 0.01) in the fractional anisotropy (FA) weighted networks. The node degree of the left amygdala was negatively correlated with the total score (r = －0.47,P = 0.04), second item score (r = －0.46, P = 0.03), and third item score of IIEF-5 (r = －0.45, P = 0.04) in the pED patients. CONCLUSIONS: The myelin integrity of the white matter fibers in the left frontal lobe and amygdale is impaired in pED patients, which leads to the aberrant generation, processing and regulation of their emotions. The decreased pivotal role and importance of the white matter fibers connecting the left amygdale may be associated with pED.

The effect of the neural activity on topological properties of growing neural networks.

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

Co-expressed miRNAs in gastric adenocarcinoma.

Co-expression networks may provide insights into the patterns of molecular interactions that underlie cellular processes. To obtain a better understanding of miRNA expression patterns in gastric adenocarcinoma and to provide markers that can be associated with histopathological findings, we performed weighted gene correlation network analysis (WGCNA) and compare it with a supervised analysis. Integrative analysis of target predictions and miRNA expression profiles in gastric cancer samples was also performed. WGCNA identified a module of co-expressed miRNAs that were associated with histological traits and tumor condition. Hub genes were identified based on statistical analysis and network centrality. The miRNAs 100, let-7c, 125b and 99a stood out for their association with the diffuse histological subtype. The 181 miRNA family and miRNA 21 highlighted for their association with the tumoral phenotype. The integrated analysis of miRNA and gene expression profiles showed the let-7 miRNA family playing a central role in the regulatory relationships.

Large-scale network-level processes during entrainment.

Visual rhythmic stimulation evokes a robust power increase exactly at the stimulation frequency, the so-called steady-state response (SSR). Localization of visual SSRs normally shows a very focal modulation of power in visual cortex and led to the treatment and interpretation of SSRs as a local phenomenon. Given the brain network dynamics, we hypothesized that SSRs have additional large-scale effects on the brain functional network that can be revealed by means of graph theory. We used rhythmic visual stimulation at a range of frequencies (4-30 Hz), recorded MEG and investigated source level connectivity across the whole brain. Using graph theoretical measures we observed a frequency-unspecific reduction of global density in the alpha band "disconnecting" visual cortex from the rest of the network. Also, a frequency-specific increase of connectivity between occipital cortex and precuneus was found at the stimulation frequency that exhibited the highest resonance (30 Hz). In conclusion, we showed that SSRs dynamically re-organized the brain functional network. These large-scale effects should be taken into account not only when attempting to explain the nature of SSRs, but also when used in various experimental designs.

A hybrid behavioural rule of adaptation and drift explains the emergent architecture of antagonistic networks.

Ecological processes that can realistically account for network architectures are central to our understanding of how species assemble and function in ecosystems. Consumer species are constantly selecting and adjusting which resource species are to be exploited in an antagonistic network. Here we incorporate a hybrid behavioural rule of adaptive interaction switching and random drift into a bipartite network model. Predictions are insensitive to the model parameters and the initial network structures, and agree extremely well with the observed levels of modularity, nestedness and node-degree distributions for 61 real networks. Evolutionary and community assemblage histories only indirectly affect network structure by defining the size and complexity of ecological networks, whereas adaptive interaction switching and random drift carve out the details of network architecture at the faster ecological time scale. The hybrid behavioural rule of both adaptation and drift could well be the key processes for structure emergence in real ecological networks.