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With numerous nodes on online heterogeneous networks, how to reach and extract target nodes of our specific interests is a pressing problem. In this paper, we propose a novel heterogeneous network crawler, MCrawl. It addresses the problem via iterative online heterogeneous network crawling by navigating its available APIs, starting from a set of target nodes, i.e., seed nodes. We are facing two challenges towards addressing the problem. First, to navigate within a vast network, how do we start from a small set of target nodes? In other words, which nodes in the “current frontier” and which direction shall we expand, to reach promising target nodes quickly? We propose motif-based crawling to exploit the complex structures and rich semantics of heterogeneous networks. Second, in many scenarios, we do not have a classifier to assess the quality of the harvested nodes and thus the motifs to expand. We develop a probabilistic inference framework to estimate the yield and harvest rates of motifs, achieving principled bootstrapping for crawling. Our experiment on real networks of MCrawl achieves significant margins over baselines. [ABSTRACT FROM AUTHOR]

Abstract Understanding actor collaboration networks and their evolution is essential to promoting collective action in resilience planning and management of interdependent infrastructure systems. Local interactions and choice homophily are two important network evolution mechanisms. Network motifs encode the information of network formation, configuration, and the local structure. Homophily effects, on the other hand, capture whether the network configurations have significant correlations with node properties. The objective of this paper is to explore the extent to which local interactions and homophily effects influence actor collaboration in resilience planning and management of interdependent infrastructure systems. We mapped bipartite actor collaboration network based on a post-Hurricane Harvey stakeholder survey that revealed actor collaborations for hazard mitigation. We examined seven bipartite network motifs for the mapped collaboration network and compared the mapped network to simulated random models with same degree distributions. Then we examined whether the network configurations had significant statistics for node properties using exponential random graph models. The results provide insights about the two mechanisms—local interactions and homophily effect—influencing the formation of actor collaboration in resilience planning and management of interdependent urban systems. The findings have implications for improving network cohesion and actor collaborations from diverse urban sectors.

Germ layer formation is among the earliest differentiation events in metazoan embryos. In triploblasts, three germ layers are formed, among which the endoderm gives rise to the epithelial lining of the gut tube and associated organs including the liver, pancreas and lungs. In frogs (Xenopus), where early germ layer formation has been studied extensively, the process of endoderm specification involves the interplay of dozens of transcription factors. Here, we review the interactions between these factors, summarized in a transcriptional gene regulatory network (GRN). We highlight regulatory connections conserved between frog, fish, mouse, and human endodermal lineages. Especially prominent is the conserved role and regulatory targets of the Nodal signaling pathway and the T-box transcription factors, Vegt and Eomes. Additionally, we highlight network topologies and motifs, and speculate on their possible roles in development.

Abstract Motifs represent local subgraphs that are overrepresented in networks. Several disciplines document multiple instances in which motifs appear in graphs and provide insight into the structure and processes of these networks. In the current paper, we focus on social networks and examine the prevalence of dyad, triad, and symmetric tetrad motifs among 24 networks that represent six types of social interactions: friendship, legislative co-sponsorship, Twitter messages, advice seeking, email communication, and terrorist collusion. Given that the correct control distribution for detecting motifs is a matter of continuous debate, we propose a novel approach that compares the local patterns of observed networks to random graphs simulated from exponential random graph models. Our proposed technique can produce conditional distributions that control for multiple, lower-level structural patterns simultaneously. We find evidence for five motifs using our approach, including the reciprocated dyad, three triads, and one symmetric tetrad. Results highlight the importance of mutuality, hierarchy, and clustering across multiple social interactions, and provide evidence of “structural signatures” within different genres of graph. Similarities also emerge between our findings and those in other disciplines, such as the preponderance of transitive triads.

In this paper, we argue that discourse representations can be mapped to networks and analyzed by tools provided in network theory so that deep properties of discourse structure are revealed. Two discourse-annotated corpora, C58 and STAC, that belong to different discourse types and languages were compared and analyzed. Various key network indices were used for the discourse representations of both corpora and show the different network profiles of the two discourse types. Moreover, both network motifs and antimotifs were discovered for the discourse networks in the two corpora that shed light on strong tendencies in building or avoiding to build discourse relations between utterances for permissible three-node discourse subgraphs. These results may lead to new types of discourse structure rules that draw on the properties of the networks that lie behind discourse representation. Another important aspect is that the second version of the STAC corpus, which includes nonlinguistic discourse units and their relations, exhibits similar trends in terms of network subgraphs compared to its first version. This suggests that the nonlinguistic context has a significant impact on discourse structure.

BackgroundRobustness to genetic and environmental perturbation is a salient feature of multicellular organisms. Loss of developmental robustness can lead to severe phenotypic defects and fitness loss. However, perfect robustness, i.e. no variation at all, is evolutionarily unfit as organisms must be able to change phenotype to properly respond to changing environments and biotic challenges. Plasticity is the ability to adjust phenotypes predictably in response to specific environmental stimuli, which can be considered a transient shift allowing an organism to move from one robust phenotypic state to another. Plants, as sessile organisms that undergo continuous development, are particularly dependent on an exquisite fine-tuning of the processes that balance robustness and plasticity to maximize fitness.Scope and conclusionsThis paper reviews recently identified mechanisms, both systems-level and molecular, that modulate robustness, and discusses their implications for the optimization of plant fitness. Robustness in living systems arises from the structure of genetic networks, the specific molecular functions of the underlying genes, and their interactions. This very same network responsible for the robustness of specific developmental states also has to be built such that it enables plastic yet robust shifts in response to environmental changes. In plants, the interactions and functions of signal transduction pathways activated by phytohormones and the tendency for plants to tolerate whole-genome duplications, tandem gene duplication and hybridization are emerging as major regulators of robustness in development. Despite their obvious implications for plant evolution and plant breeding, the mechanistic underpinnings by which plants modulate precise levels of robustness, plasticity and evolvability in networks controlling different phenotypes are under-studied.

We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define ”good” patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning.

As the first decentralized peer-to-peer (P2P) cryptocurrency system allowing people to trade with pseudonymous addresses, Bitcoin has become increasingly popular in recent years. However, the P2P and pseudonymous nature of Bitcoin make transactions on this platform very difficult to track, thus triggering the emergence of various illegal activities in the Bitcoin ecosystem. Particularly, mixing services in Bitcoin, originally designed to enhance transaction anonymity, have been widely employed for money laundering to complicate the process of trailing illicit fund. In this article, we focus on the detection of the addresses belonging to mixing services, which is an important task for anti-money laundering in Bitcoin. Specifically, we provide a feature-based network analysis framework to identify statistical properties of mixing services from three levels, namely, network level, account level, and transaction level. To better characterize the transaction patterns of different types of addresses, we propose the concept of attributed temporal heterogeneous motifs (ATH motifs). Moreover, to deal with the issue of imperfect labeling, we tackle the mixing detection task as a positive and unlabeled learning (PU learning) problem and build a detection model by leveraging the considered features. Experiments on real Bitcoin datasets demonstrate the effectiveness of our detection model and the importance of hybrid motifs including ATH motifs in mixing detection. [ABSTRACT FROM AUTHOR]

Computing subgraph frequencies is a fundamental task that lies at the core of several network analysis methodologies, such as network motifs and graphlet-based metrics, which have been widely used to categorize and compare networks from multiple domains. Counting subgraphs is, however, computationally very expensive, and there has been a large body of work on efficient algorithms and strategies to make subgraph counting feasible for larger subgraphs and networks. This survey aims precisely to provide a comprehensive overview of the existing methods for subgraph counting. Our main contribution is a general and structured review of existing algorithms, classifying them on a set of key characteristics, highlighting their main similarities and differences. We identify and describe the main conceptual approaches, giving insight on their advantages and limitations, and we provide pointers to existing implementations. We initially focus on exact sequential algorithms, but we also do a thorough survey on approximate methodologies (with a trade-off between accuracy and execution time) and parallel strategies (that need to deal with an unbalanced search space). [ABSTRACT FROM AUTHOR]

The behaviour of individual users in an online social network is a major contributing factor in determining the outcome of multiple network phenomenon. Group formation, growth of the network, information propagation, and rumour blocking are some of the many network behavioural traits that are influenced by the interaction patterns of the users in the network. Network motifs capture one such interaction pattern between users in online social networks (OSNs). For this work, four second-order (two-edged) network motifs have been considered, namely, message receiving pattern, message broadcasting pattern, message passing pattern, and reciprocal message pattern, to analyse user behaviour in online social networks. This work provides and utilizes a node interaction pattern-finding algorithm to identify the frequency of aforementioned second-order network motifs in six real-life online social networks (Facebook, GPlus, GNU, Twitter, Enron Email, and Wiki-vote). The frequency of network motifs participated in by a node is considered for the relative ranking of all nodes in the online social networks. The highest-rated nodes are considered seeds for information propagation. The performance of using network motifs for ranking nodes as seeds for information propagation is validated using statistical metrics Z-score, concentration, and significance profile and compared with baseline ranking methods in-degree centrality, out-degree centrality, closeness centrality, and PageRank. The comparative study shows the performance of centrality measures to be similar or better than second-order network motifs as seed nodes in information diffusion. The experimental results on finding frequencies and importance of different interaction patterns provide insights on the significance and representation of each such interaction pattern and how it varies from network to network. [ABSTRACT FROM AUTHOR]

Identifying and tracking community structures in complex networks are one of the cornerstones of network studies, spanning multiple disciplines, from statistics to machine learning to social sciences, and involving even a broader range of application areas, from biology to politics to blockchain. This survey paper aims to provide an overview of some most popular approaches in statistical network community detection as well as the newly emerging research directions such as community extraction with higher‐order features and community discovery in multilayer and multiscale networks. Our goal is to offer a unified view at methodological interconnections and the wide spectrum of interdisciplinary data science applications of network community analysis. This article is categorized under:Data: Types and Structure > Graph and Network DataStatistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification [ABSTRACT FROM AUTHOR]

The cell stress‐responsive transcription factor p53 influences the expression of its target genes and subsequent cellular responses based in part on its dynamics (changes in level over time). The mechanisms decoding p53 dynamics into subsequent target mRNA and protein dynamics remain unclear. We systematically quantified p53 target mRNA and protein expression over time under two p53 dynamical regimes, oscillatory and rising, using RNA‐sequencing and TMT mass spectrometry. Oscillatory dynamics allowed for a greater variety of dynamical patterns for both mRNAs and proteins. Mathematical modeling of empirical data revealed three distinct mechanisms that decode p53 dynamics. Specific combinations of these mechanisms at the transcriptional and post‐transcriptional levels enabled exclusive induction of proteins under particular dynamics. In addition, rising induction of p53 led to higher induction of proteins regardless of their functional class, including proteins promoting arrest of proliferation, the primary cellular outcome under rising p53. Our results highlight the diverse mechanisms cells employ to distinguish complex transcription factor dynamics to regulate gene expression. SYNOPSIS: Time‐series transcriptomics and proteomics reveal that different dynamics of the transcription factor p53 generate diverse p53 target mRNA and protein expression patterns. Mathematical modeling uncovers distinct mechanisms that decode p53 dynamics. Complementary RNA‐Seq and proteomics experiments were performed at high temporal resolution under oscillating or rising p53 dynamical regime following DNA damage.Oscillatory p53 dynamics enabled greater diversity of target RNA and protein induction profiles than rising p53 dynamics, largely due to the degradation properties of its targets.Gene regulatory network motifs, such as feed forward loops, can explain exclusive induction under a given p53 dynamical regime.Cells with rising p53 dynamics exhibited a higher induction of proteins, which likely contributes to the loss of proliferative potential in these cells. [ABSTRACT FROM AUTHOR]

Chaotic resonance (CR) is the response of a nonlinear system to weak signals enhanced by internal or external chaotic activity (such as the signal derived from Lorenz system). The triple-neuron feed-forward loop (FFL) Izhikevich neural network motifs with eight types are constructed as the nonlinear systems in this paper, and the effects of EMI on CR phenomenon in FFL neuronal network motifs are studied. It is found that both the single Izhikevich neural model under electromagnetic induction (EMI) and its network motifs exhibit CR phenomenon depending on the chaotic current intensity. There exists an optimal chaotic current intensity ensuring the best detection of weak signal in single Izhikevich neuron or its network motifs via CR. The EMI can enhance the ability of neuron to detect weak signals. For T1-FFL and T2-FFL motifs, the adjustment of EMI parameters makes T2-FFL show a more obvious CR phenomenon than that for T1-FFL motifs, which is different from the impact of system parameters (e.g., the weak signal frequency, the coupling strength, and the time delay) on CR. Another interesting phenomenon is that the variation of CR with time delay exhibits quasi-periodic characteristics. Our results showed that CR effect is a robust phenomenon which is observed in both single Izhikevich neuron and network motifs, which might help one understand how to improve the ability of weak signal detection and propagation in neuronal system. [ABSTRACT FROM AUTHOR]

Abstract Human mobility connects urban dwellers and neighborhoods and impacts social equity. An in-depth understanding of human mobility helps to enhance urban resilience. However, limited research has focused on mobility resilience. Building on previous research, this study looks at the neighborhood connectivity enabled by urban mobility. We analyze the aggregated mobility patterns in Boston through the coupling of network structure and social characteristics. Geocoded twitter data combined with socioeconomic datasets were used to create a mobility-based urban network. Through the quantitative analysis, we found that the social segregation in Boston shapes its mobility network. Network communities identified by the Louvain modularity algorithm are often self-containing, meaning that their residents are more likely to move within their communities. A multinomial regression reveals that spatial racial and income segregation has a strong impact on the dynamic segregation of the network. The beneficial network characteristics –e.g. higher density and well-connected motifs– are less present in areas with bolder presence of minorities. Thus, the resilience state is not equitable among neighborhoods of different income levels and races, indicating that the resilience measures of urban networks need to be adapted according to sociodemographic characteristics.

Abstract Background Network motifs are connectivity structures that occur with significantly higher frequency than chance, and are thought to play important roles in complex biological networks, for example in gene regulation, interactomes, and metabolomes. Network motifs may also become pivotal in the rational design and engineering of complex biological systems underpinning the field of synthetic biology. Distinguishing true motifs from arbitrary substructures, however, remains a challenge. Results Here we demonstrate both theoretically and empirically that implicit assumptions present in mainstream methods for motif identification do not necessarily hold, with the ramification that motif studies using these mainstream methods are less able to effectively differentiate between spurious results and events of true statistical significance than is often presented. We show that these difficulties cannot be overcome without revising the methods of statistical analysis used to identify motifs. Conclusions Present-day methods for the discovery of network motifs, and, indeed, even the methods for defining what they are, are critically reliant on a set of incorrect assumptions, casting a doubt on the scientific validity of motif-driven discoveries. The implications of these findings are therefore far-reaching across diverse areas of biology.

Temporal networks are graphs where each edge is linked with a timestamp, denoting when an interaction between two nodes happens. According to the most recently proposed definitions of the problem, motif search in temporal networks consists in finding and counting all connected temporal graphs Q (called motifs) occurring in a larger temporal network T, such that matched target edges follow the same chronological order imposed by edges in Q. In the last few years, several algorithms have been proposed to solve motif search, but most of them are limited to very small or specific motifs due to the computational complexity of the problem. In this paper, we present MODIT (MOtif DIscovery in Temporal Networks), an algorithm for counting motifs of any size in temporal networks, inspired by a very recent algorithm for subgraph isomorphism in temporal networks, called TemporalRI. Experiments show that for big motifs (more than 3 nodes and 3 edges) MODIT can efficiently retrieve them in reasonable time (up to few hours) in many networks of medium and large size and outperforms state-of-the art algorithms.

Most plant primary transcripts undergo alternative splicing (AS), and its impact on protein diversity is a subject of intensive investigation. Several studies have uncovered various mechanisms of how particular protein splice isoforms operate. However, the common principles behind the AS effects on protein function in plants have rarely been surveyed. Here, on the selected examples, we highlight diverse tissue expression patterns, subcellular localization, enzymatic activities, abilities to bind other molecules and other relevant features. We describe how the protein isoforms mutually interact to underline their intriguing roles in altering the functionality of protein complexes. Moreover, we also discuss the known cases when these interactions have been placed inside the autoregulatory loops. This review is particularly intended for plant cell and developmental biologists who would like to gain inspiration on how the splice variants encoded by their genes of interest may coordinately work.

It is not uncommon for one or a few species, and their interactions, to have disproportionate effects on other species in ecological communities. Such keystone interactions might affect how communities respond to the invasion of non‐native species by preventing or inhibiting the establishment, spread or impact of non‐native species.We explore whether a keystone mutualism among a hummingbird–mistletoe–marsupial promotes ecological resistance to an invasive pollinator, the bumblebee Bombus terrestris, by comparing data collected at sites prior to bumblebee invasion to data collected 11 years after the invasion in sites with and without the keystone mutualism.We built pollination networks and focused on network motifs, regarded as building blocks of networks, to identify the central pollinators and estimate the change in their interactions after invasion of B. terrestris. We also estimated the interaction rewiring across the season in post‐invasion networks and tested it as a possible mechanism explaining how the keystone mutualism increased ecological resistance to invasion.We found two times more species in post‐invasion sites with the keystone mutualism than in post‐invasion sites without the keystone mutualism. Moreover, we found that invasive bumblebee reduced the strength and interaction niche of the five central pollinator species while increasing its own strength and interaction niche, suggesting a replacement of interactions. Also, we found that the keystone mutualism promoted resistance to B. terrestris invasion by reducing its negative impacts on central species. In the presence of the keystone mutualism, central species had three times more direct interactions than in sites without this keystone mutualism. The higher interaction rewiring, after invasion of B. terrestris, in sites with the keystone mutualism indicates greater chances of central pollinators to form new interactions and reduces their competence for resources with the non‐native bumblebee.Our results demonstrate that a keystone mutualism can enhance community resistance against the impacts of a non‐native invasive pollinator by increasing species diversity and promoting interaction rewiring in the community. This study suggests that the conservation of mutualisms, especially those considered keystone, could be essential for long‐term preservation of natural communities under current and future impacts of global change. [ABSTRACT FROM AUTHOR]

Understanding the impacts of global change on ecological communities is a major challenge in modern ecology. The gain or loss of particular species and the disruption of key interactions are both consequences and drivers of global change that can lead to the disassembly of ecological networks. We examined whether the disruption of a hummingbird–mistletoe–marsupial mutualism by the invasion of non‐native species can have cascading effects on both pollination and seed dispersal networks in the temperate forest of Patagonia, Argentina. We focused on network motifs, subnetworks composed of a small number of species exhibiting particular patterns of interaction, to examine the structure and diversity of mutualistic networks. We found that the hummingbird–mistletoe–marsupial mutualism plays a critical role in the community by increasing the complexity of pollination and seed dispersal networks through supporting a high diversity of interactions. Moreover, we found that the disruption of this tripartite mutualism by non‐native ungulates resulted in diverse indirect effects that led to less complex pollination and seed dispersal networks. Our results demonstrate that the gains and losses of particular species and the alteration of key interactions can lead to cascading effects in the community through the disassembly of mutualistic networks. [ABSTRACT FROM AUTHOR]

Temporal graphs are structures which model relational data between entities that change over time. Due to the complex structure of data, mining statistically significant temporal subgraphs, also known as temporal motifs, is a challenging task. In this work, we present an efficient technique for extracting temporal motifs in temporal networks. Our method is based on the novel notion of egocentric temporal neighborhoods, namely multi-layer structures centered on an ego node. Each temporal layer of the structure consists of the first-order neighborhood of the ego node, and corresponding nodes in sequential layers are connected by an edge. The strength of this approach lies in the possibility of encoding these structures into a unique bit vector, thus bypassing the problem of graph isomorphism in searching for temporal motifs. This allows our algorithm to mine substantially larger motifs with respect to alternative approaches. Furthermore, by bringing the focus on the temporal dynamics of the interactions of a specific node, our model allows to mine temporal motifs which are visibly interpretable. Experiments on a number of complex networks of social interactions confirm the advantage of the proposed approach over alternative non-egocentric solutions. The egocentric procedure is indeed more efficient in revealing similarities and discrepancies among different social environments, independently of the different technologies used to collect data, which instead affect standard non-egocentric measures. [ABSTRACT FROM AUTHOR]

Most plant primary transcripts undergo alternative splicing (AS), and its impact on protein diversity is a subject of intensive investigation. Several studies have uncovered various mechanisms of how particular protein splice isoforms operate. However, the common principles behind the AS effects on protein function in plants have rarely been surveyed. Here, on the selected examples, we highlight diverse tissue expression patterns, subcellular localization, enzymatic activities, abilities to bind other molecules and other relevant features. We describe how the protein isoforms mutually interact to underline their intriguing roles in altering the functionality of protein complexes. Moreover, we also discuss the known caseswhen these interactions have been placed inside the autoregulatory loops. This review is particularly intended for plant cell and developmental biologists who would like to gain inspiration on how the splice variants encoded by their genes of interest may coordinately work. [ABSTRACT FROM AUTHOR]

The study of motifs can help researchers uncover links between the structure and function of networks in biology, sociology, economics, and many other areas. Empirical studies of networks have identified feedback loops, feedforward loops, and several other small structures as "motifs"" that occur frequently in real-world networks and may contribute by various mechanisms to important functions in these systems. However, these mechanisms are unknown for many of these motifs. We propose to distinguish between "structure motifs"" (i.e., weakly connected graphlets) in networks and "process motifs"" (which we define as structured sets of walks) on networks and consider process motifs as building blocks of processes on networks. Using steady-state covariance and steady-state correlation in a multivariate Ornstein--Uhlenbeck process on a network as examples, we demonstrate that distinguishing between structure motifs and process motifs makes it possible to gain quantitative insights into mechanisms that contribute to important functions of dynamical systems on networks. [ABSTRACT FROM AUTHOR]

This paper introduces a framework for understanding complex temporal interaction patterns in large-scale scientific collaboration networks. In particular, we investigate how two key concepts in science studies, scientific collaboration and scientific mobility, are related and possibly differ between fields. We do so by analyzing multilayer temporal motifs: small recurring configurations of nodes and edges. Driven by the problem that many papers share the same publication year, we first provide a methodological contribution: an efficient counting algorithm for multilayer temporal motifs with concurrent edges. Next, we introduce a systematic categorization of the multilayer temporal motifs, such that each category reflects a pattern of behavior relevant to scientific collaboration and mobility. Here, a key question concerns the causal direction: does mobility lead to collaboration or vice versa? Applying this framework to scientific collaboration networks extracted from Web of Science (WoS) consisting of up to 7.7 million nodes (authors) and 94million edges (collaborations), we find that international collaboration and international mobility reciprocally influence one another. Additionally, we find that Social sciences & Humanities (SSH) scholars co-author to a greater extent with authors at a distance, while Mathematics & Computer science (M&C) scholars tend to continue to collaborate within the established knowledge network and organization. [ABSTRACT FROM AUTHOR]

Predicting gene expression from DNA sequence remains a major goal in the field of gene regulation. A challenge to this goal is the connectivity of the network, whose role in altering gene expression remains unclear. Here, we study a common autoregulatory network motif, the negative single-input module, to explore the regulatory properties inherited from the motif. Using stochastic simulations and a synthetic biology approach in E. coli, we find that the TF gene and its target genes have inherent asymmetry in regulation, even when their promoters are identical; the TF gene being more repressed than its targets. The magnitude of asymmetry depends on network features such as network size and TF-binding affinities. Intriguingly, asymmetry disappears when the growth rate is too fast or too slow and is most significant for typical growth conditions. These results highlight the importance of accounting for network architecture in quantitative models of gene expression.

Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs. Graph energy, reciprocity and cyclomatic complexity can optimally specify network subgraphs with some degree of degeneracy. Seventy-one molecular networks were analyzed from three network types: cancer networks, signal transduction networks, and cellular processes. Molecular networks are built from a finite number of subgraph patterns and subgraphs with large graph energies are not present, which implies a graph energy cutoff. In addition, certain subgraph patterns are absent from the three network types. Thus, the Shannon entropy of the subgraph frequency distribution is not maximal. Furthermore, frequently-observed subgraphs are irreducible graphs. These novel findings warrant further investigation and may lead to important applications. Finally, we observed that cancer-related cellular processes are enriched with subgraph-associated driver genes. Our study provides a systematic approach for dissecting biological networks and supports the conclusion that there are organizational principles underlying molecular networks.

Summary: The network topology of a protein interactome is shaped by the function of each protein, making it a resource of functional knowledge in tissues and in single cells. Today, this resource is underused, as complete network topology characterization has proved difficult for large protein interactomes. We apply a matrix visualization and decoding approach to a physical protein interactome of a dendritic cell, thereby characterizing its topology with no prior assumptions of structure. We discover 294 proteins, each forming topological motifs called “bow-ties” that tie together the majority of observed protein complexes. The central proteins of these bow-ties have unique network properties, display multifunctional capabilities, are enriched for essential proteins, and are widely expressed in other cells and tissues. Collectively, the bow-tie motifs are a pervasive and previously unnoted topological trend in cellular interactomes. As such, these results provide fundamental knowledge on how intracellular protein connectivity is organized and operates.

Complex networks often display network motifs and these can be described as subgraphs. Methods for analyzing complex networks promise to be of great benefit to almost all scientific disciplines including sports. In football if we want to disrupt the opponent's game format, we must first be aware of the pass motifs that the team often uses. Determining how to break these motifs will make an important contribution to the success of a team. In this study, 3-nodes and 4-nodes pass motifs of the teams were examined within the frame of a data set of ten games and the most frequent repetitions of these motifs were determined. In addition, we suggest that in a match, the balance can be measured by the correlation between the frequencies of the motif types and there may be an inverse relationship between this correlation and the difference in the goals of the match.

Abstract In corporate networks, firms are connected through links of corporate ownership and shared directors, connecting the control over major economic actors in our economies in meaningful and consequential ways. Most research thus far focused on the connectedness of firms as a result of one particular link type, analyzing node-specific metrics or global network-based methods to gain insights in the modelled corporate system. In this paper, we aim to understand multiplex corporate networks with multiple types of connections, specifically investigating the network’s essential building blocks: multiplex network motifs. Motifs, which are small subgraph patterns occurring at significantly higher frequencies than in similar random networks, have demonstrated their usefulness in understanding the structure of many types of real-world networks. However, detecting motifs in multiplex networks is nontrivial for two reasons. First of all, there are no out-of-the-box subgraph enumeration algorithms for multiplex networks. Second, existing null models to test network motif significance, are unable to incorporate the interlayer dependencies in the multiplex network. We solve these two issues by introducing a layer encoding algorithm that incorporates the multiplex aspect in the subgraph enumeration phase. In addition, we propose a null model that is able to preserve the interlayer connectedness, while taking into account that one of the link types is actually the result of a projection of an underlying bipartite network. The experimental section considers the corporate network of Germany, in which tens of thousands of firms are connected through several hundred thousand links. We demonstrate how incorporating the multiplex aspect in motif detection is able to reveal new insights that could not be obtained by studying only one type of relationship. In a general sense, the motifs reflect known corporate governance practices related to the monitoring of investments and the concentration of ownership. A substantial fraction of the discovered motifs is typical for an industrialized country such as Germany, whereas others seem specific for certain economic sectors. Interestingly, we find that motifs involving financial firms are over-represented amongst the larger and more complex motifs. This demonstrates the prominent role of the financial sector in Germany’s largely industry-oriented corporate network.

We present a low-dimensional morphospace of fMRI brain networks, where axes are defined in a data-driven manner based on the network motifs. The morphospace allows us to identify the key variations in healthy fMRI networks in terms of their underlying motifs, and we observe that two principal components (PCs) can account for 97% of the motif variability. The first PC of the motif distribution is correlated with efficiency and inversely correlated with transitivity. Hence this axis approximately conforms to the well-known economical small-world trade-off between integration and segregation in brain networks. Finally, we show that the economical clustering generative model proposed by Vértes et al. (2012) can approximately reproduce the motif morphospace of the real fMRI brain networks, in contrast to other generative models. Overall, the motif morphospace provides a powerful way to visualize the relationships between network properties and to investigate generative or constraining factors in the formation of complex human brain functional networks. Motifs have been described as the building blocks of complex networks. Meanwhile, a morphospace allows networks to be placed in a common space and can reveal the relationships between different network properties and elucidate the driving forces behind network topology. We combine the concepts of motifs and morphospaces to create the first motif morphospace of fMRI brain networks. Crucially, the morphospace axes are defined by the motifs, in a data-driven manner. We observe strong correlations between the networks’ positions in morphospace and their global topological properties, suggesting that motif morphospaces are a powerful way to capture the topology of networks in a low-dimensional space and to compare generative models of brain networks. Motif morphospaces could also be used to study other complex networks’ topologies.

A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. Mapping the routing of spikes through local circuitry is crucial for understanding neocortical computation. Under appropriate experimental conditions, these maps can be used to infer likely patterns of synaptic recruitment, linking activity to underlying anatomical connections. Such inferences help to reveal the synaptic implementation of population dynamics and computation. We compare a number of standard functional measures to infer underlying connectivity. We find that regularization impacts measures heterogeneously, and that individual algorithms have unique biases that impact their interpretation. These biases are nonoverlapping, and thus have the potential to mitigate one another. Combining individual algorithms into a single ensemble method results in a stronger inference algorithm than the best individual component measure. Ensemble-based inference can yield higher sensitivity to underlying connections and an improved estimate of the true statistics of synaptic recruitment.

A recurring set of small sub-networks have been identified as the building blocks of biological networks across diverse organisms. These network motifs are associated with certain dynamic behaviors and define key modules that are important for understanding complex biological programs. Besides studying the properties of motifs in isolation, current algorithms typically evaluate the occurrence frequency of a specific motif in a given biological network compared to that in random networks of similar structure. However, it remains challenging to relate the structure of motifs to the observed and expected behavior of the larger, more complex network they are contained within. This problem is compounded as even the precise structure of most biological networks remains largely unknown. Previously, we developed a formal reasoning approach enabling the synthesis of biological networks capable of reproducing some experimentally observed behavior. Here, we extend this approach to allow reasoning over the requirement for specific network motifs as a way of explaining how these behaviors arise. We illustrate the approach by analyzing the motifs involved in sign-sensitive delay and pulse generation. We demonstrate the scalability and biological relevance of the approach by studying the previously defined networks governing myeloid differentiation, the yeast cell cycle, and naïve pluripotency in mouse embryonic stem cells, revealing the requirement for certain motifs in these systems. [ABSTRACT FROM AUTHOR]

We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define "good" patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning. [ABSTRACT FROM AUTHOR]

Cryptocurrencies and the underpinning blockchain technology have gained unprecedented public attention recently. In contrast to fiat currencies, transactions of cryptocurrencies, such as Bitcoin and Litecoin, are permanently recorded on distributed ledgers to be seen by the public. As a result, public availability of all cryptocurrency transactions allows us to create a complex network of financial interactions that can be used to study not only the blockchain graph, but also the relationship between various blockchain network features and cryptocurrency risk investment. We introduce a novel concept of chainlets, or blockchain motifs, to utilize this information. Chainlets allow us to evaluate the role of local topological structure of the blockchain on the joint Bitcoin and Litecoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on cryptocurrency price and investment risk. More generally, while statistical aspects of blockchain data analytics remain virtually unexplored, the paper aims to highlight various emerging theoretical, methodological and applied research challenges of blockchain data analysis that will be of interest to the broad statistical community. The Canadian Journal of Statistics 48: 561–581; 2020 © 2020 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

We critique the organizational account of biological functions by showing how its basis in the closure of constraints fails to be objective. While the account treats constraints as objective features of physical systems, the number and relationship of potential constraints are subject to potentially arbitrary redescription by investigators. For example, we show that self-maintaining systems such as candle flames can realize closure on a more thorough analysis of the case, contradicting the claim that these "simple" systems lack functional organization. This also raises problems for Moreno and Mossio's associated theory of biological autonomy, which asserts that living beings are distinguished by their possession of a closed system of constraints that channel and regulate their metabolic processes. [ABSTRACT FROM AUTHOR]

Background: Network motifs are connectivity structures that occur with significantly higher frequency than chance, and are thought to play important roles in complex biological networks, for example in gene regulation, interactomes, and metabolomes. Network motifs may also become pivotal in the rational design and engineering of complex biological systems underpinning the field of synthetic biology. Distinguishing true motifs from arbitrary substructures, however, remains a challenge. Results: Here we demonstrate both theoretically and empirically that implicit assumptions present in mainstream methods for motif identification do not necessarily hold, with the ramification that motif studies using these mainstream methods are less able to effectively differentiate between spurious results and events of true statistical significance than is often presented. We show that these difficulties cannot be overcome without revising the methods of statistical analysis used to identify motifs. Conclusions: Present-day methods for the discovery of network motifs, and, indeed, even the methods for defining what they are, are critically reliant on a set of incorrect assumptions, casting a doubt on the scientific validity of motif-driven discoveries. The implications of these findings are therefore far-reaching across diverse areas of biology. [ABSTRACT FROM AUTHOR]