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We examine the structural patterns in the cognitive representation of social networks by systematically classifying false positives and negatives. Although existing literature on Cognitive Social Structures (CSS) has begun exploring false positives and negatives by comparing actual and perceived networks, it has not differentiated simultaneous occurrences of true and false positives and negatives on network motifs, such as reciprocity and triadic closure. Here, we propose a theoretical framework to categorize three classes of errors we call imaginary network motifs as combinations of accurately and erroneously perceived ties: (a) partially false, (b) completely false, and (c) mixed false. Using four published CSS data sets, we empirically test which imaginary network motifs are significantly more or less present in different types of perceived networks than the corresponding actual networks. Our results confirm that people not only fill in the blanks as suggested in the prior research but also conceive other imaginary structures. The findings advance our understanding of perception gaps between actual and perceived networks and have implications for designing more accurate network modeling and sampling. • The dyad census approach of a multiplex motif method is developed and applied to Cognitive Social Structure data. • The approach identifies a hidden category of mixed false where false positives and negatives co-occur. • The method reveals false positives and negatives occur with true positives and negatives. • Most false positives and negatives happen in proxy ties rather than the ones people report about themselves. • Perceived network density can partially explain the patterns of false positives and negatives. [ABSTRACT FROM AUTHOR]

At present, network representation learning methods are mostly aimed at homogeneous networks, ignoring the particularity of attributed bipartite networks and the motifs structure of networks. In order to solve the above problems, this paper proposes an attributed bipartite graph neural network with motifs information for network representation learning (MABG). MABG adjusts the edge weights by the number of butterfly motifs formed by two nodes in the network, to construct the motifs weight matrix and obtain the attributed bipartite network adjacency matrix with motifs information. Then two different strategies are adopted to capture the explicit and implicit messages in the bipartite network. For explicit relationships, a message-passing mechanism is operated between different types of nodes. For implicit relationships, a message alignment mechanism is used in nodes of the same type. An adversarial model is implemented to minimize the difference between input attributes and explicit relationship representations. Finally, a cascaded framework is proposed to capture high-order network information and obtain the final node representation. Extensive experiments are conducted in recommended tasks on four real-world datasets. The results demonstrate the effectiveness of MABG compared with other state-of-art methods. [ABSTRACT FROM AUTHOR]

The development of embryonic stem (ES) cells to extraembryonic trophectoderm and primitive endoderm lineages manifests distinct steady‐state expression patterns of two key transcription factors—Oct4 and Nanog. How dynamically such kind of steady‐state expressions are maintained remains elusive. Herein, we demonstrate that steady‐state dynamics involving two bistable switches which are interlinked via a stepwise (Oct4) and a mushroom‐like (Nanog) manner orchestrate the fate specification of ES cells. Our hypothesis qualitatively reconciles various experimental observations and elucidates how different feedback and feedforward motifs orchestrate the extraembryonic development and stemness maintenance of ES cells. Importantly, the model predicts strategies to optimize the dynamics of self‐renewal and differentiation of embryonic stem cells that may have therapeutic relevance in the future. [ABSTRACT FROM AUTHOR]

This study explores the impacts of multiple factors (noise, intra-motif coupling, and critical bifurcation parameter) on noise-induced motif synchrony and output regularity in three-node feed-forward-loops (FFLs), distinguishing between coherent FFLs with purely excitatory connections and incoherent FFLs formed by transitioning the intermediate layer to inhibitory connections. Our model utilizes the normal form of Hopf bifurcation (HB), which captures the generic structure of excitability observed in real systems. We find that the addition of noise can optimize motif synchrony and output regularity at the intermediate noise intensities. Our results also suggest that transitioning the excitatory coupling between the intermediate and output layers of the FFL to inhibitory coupling -- i.e., moving from the coherent to the incoherent FFL -- enhances output regularity but diminishes motif synchrony. This shift towards inhibitory connectivity highlights a trade-off between motif synchrony and output regularity and suggests that the structure of the intermediate layer plays a pivotal role in determining the motif's overall dynamics. Surprisingly, we also discover that both motifs achieve their best output regularity at a moderate level of intra-motif coupling, challenging the common assumption that stronger coupling, especially of the excitatory type, results in improved regularity. Our study provides valuable insights into functional differences in network motifs and offers a direct perspective relevant to the field of complex systems as we consider a normal-form model that pertains to a vast number of individual models experiencing HB. [ABSTRACT FROM AUTHOR]

Synchronous dynamics play a pivotal role in various cognitive processes. Previous studies extensively investigate noise-induced synchrony in coupled neural oscillators, with a focus on scenarios featuring uniform noise and equal coupling strengths between neurons. However, real-world or experimental settings frequently exhibit heterogeneity, including deviations from uniformity in coupling and noise patterns. This study investigates noise-induced synchrony in a pair of coupled excitable neurons operating in a heterogeneous environment, where both noise intensity and coupling strength can vary independently. Each neuron is an excitable oscillator, represented by the normal form of Hopf bifurcation (HB). In the absence of stimulus, these neurons remain quiescent but can be triggered by perturbations, such as noise. Typically, noise and coupling exert opposing influences on neural dynamics, with noise diminishing coherence and coupling promoting synchrony. Our results illustrate the ability of asymmetric noise to induce synchronization in such coupled neural oscillators, with synchronization becoming increasingly pronounced as the system approaches the excitation threshold (i.e., HB). Additionally, we find that uneven coupling strengths and noise asymmetries are factors that can promote in-phase synchrony. Notably, we identify an optimal synchronization state when the absolute difference in coupling strengths ismaximized, regardless of the specific coupling strengths chosen. Furthermore, we establish a robust relationship between coupling asymmetry and the noise intensity required to maximize synchronization. Specifically, when one oscillator (receiver neuron) receives a strong input from the other oscillator (source neuron) and the source neuron receives significantly weaker or no input from the receiver neuron, synchrony is maximized when the noise applied to the receiver neuron is much weaker than that applied to the source neuron. These findings reveal the significant connection between uneven coupling and asymmetric noise in coupled neuronal oscillators, shedding light on the enhanced propensity for in-phase synchronization in two-neuron motifs with one-way connections compared to those with two-way connections. This research contributes to a deeper understanding of the functional roles of network motifs that may serve within neuronal dynamics. [ABSTRACT FROM AUTHOR]

We propose a method for characterizing the local structure of weighted multivariate time series networks. We draw intensity and coherence of network motifs, i.e. statistically recurrent subgraphs, to characterize the system behavior via higher-order structures derived upon effective transfer entropy networks. The latter consists of a model-free methodology enabling to correct for small sample biases affecting Shannon transfer entropy, other than conducting inference on the estimated directional time series information flows. We demonstrate the usefulness of our proposed method with an application to a set of global commodity prices. Our main result shows that, despite simple triadic structures are the most intense, coherent and statistically recurrent over time, their intensity suddenly decreases after the Global Financial Crisis, in favor of most complex triadic structures, while all types of subgraphs tend to become more coherent thereafter. [ABSTRACT FROM AUTHOR]

The prevalent approach to motif analysis seeks to describe the local connectivity structure of networks by identifying subgraph patterns that appear significantly more often in a network then expected under a null model that conserves certain features of the original network. In this article we advocate for an alternative approach based on statistical inference of generative models where nodes are connected not only by edges but also copies of higher order subgraphs. These models naturally lead to the consideration of latent states that correspond to decompositions of networks into higher order interactions in the form of subgraphs that can have the topology of any simply connected motif. Being based on principles of parsimony the method can infer concise sets of motifs from within thousands of candidates allowing for consistent detection of larger motifs. The inferential approach yields not only a set of statistically significant higher order motifs but also an explicit decomposition of the network into these motifs, which opens new possibilities for the systematic study of the topological and dynamical implications of higher order connectivity structures in networks. After briefly reviewing core concepts and methods, we provide example applications to empirical data sets and discuss how the inferential approach addresses current problems in motif analysis and explore how concepts and methods common to motif analysis translate to the inferential framework.

Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator for these networks. This is achieved through data depth-based core decomposition and centered motif discovery, ensuring scalability. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness by analyzing and detecting three recent real-world incidents from our datasets: the catastrophic collapse of LunaTerra, the Proof-ofStake switch of Ethereum, and the temporary peg loss of USDC–while also verifying our results against external ground truth. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis in a scalable manner, while being more effective and efficient than baselines and state-of-the-art attributed change detection approach in dynamic graphs. [ABSTRACT FROM AUTHOR]

Network motifs are recurrent, small-scale patterns of interactions observed frequently in a system. They shed light on the interplay between the topology and the dynamics of complex networks across various domains. In this work, we focus on the problem of counting occurrences of small sub-hypergraph patterns in very large hypergraphs, where higher-order interactions connect arbitrary numbers of system units. We show how directly exploiting higher-order structures speeds up the counting process compared to traditional data mining techniques for exact motif discovery. Moreover, with hyperedge sampling, performance is further improved at the cost of small errors in the estimation of motif frequency. We evaluate our method on several real-world datasets describing face-to-face interactions, co-authorship and human communication. We show that our approximated algorithm allows us to extract higher-order motifs faster and on a larger scale, beyond the computational limits of an exact approach. [ABSTRACT FROM AUTHOR]

This study explores the impacts of multiple factors (noise, intra-motif coupling, and critical bifurcation parameter) on noise-induced motif synchrony and output regularity in three-node feed-forward-loops (FFLs), distinguishing between coherent FFLs with purely excitatory connections and incoherent FFLs formed by transitioning the intermediate layer to inhibitory connections. Our model utilizes the normal form of Hopf bifurcation (HB), which captures the generic structure of excitability observed in real systems. We find that the addition of noise can optimize motif synchrony and output regularity at the intermediate noise intensities. Our results also suggest that transitioning the excitatory coupling between the intermediate and output layers of the FFL to inhibitory coupling—i.e., moving from the coherent to the incoherent FFL—enhances output regularity but diminishes motif synchrony. This shift towards inhibitory connectivity highlights a trade-off between motif synchrony and output regularity and suggests that the structure of the intermediate layer plays a pivotal role in determining the motif’s overall dynamics. Surprisingly, we also discover that both motifs achieve their best output regularity at a moderate level of intra-motif coupling, challenging the common assumption that stronger coupling, especially of the excitatory type, results in improved regularity. Our study provides valuable insights into functional differences in network motifs and offers a direct perspective relevant to the field of complex systems as we consider a normal-form model that pertains to a vast number of individual models experiencing HB.

Abstract Bots in online social networks can be used for good or bad but their presence is unavoidable and will increase in the future. To investigate how the interaction networks of bots and humans evolve, we created six social bots on Twitter with AI language models and let them carry out standard user operations. Three different strategies were implemented for the bots: a trend-targeting strategy (TTS), a keywords-targeting strategy (KTS) and a user-targeting strategy (UTS). We examined the interaction patterns such as targeting users, spreading messages, propagating relationships, and engagement. We focused on the emergent local structures or motifs and found that the strategies of the social bots had a significant impact on them. Motifs resulting from interactions with bots following TTS or KTS are simple and show significant overlap, while those resulting from interactions with UTS-governed bots lead to more complex motifs. These findings provide insights into human-bot interaction patterns in online social networks, and can be used to develop more effective bots for beneficial tasks and to combat malicious actors.

Bots in online social networks can be used for good or bad but their presence is unavoidable and will increase in the future. To investigate how the interaction networks of bots and humans evolve, we created six social bots on Twitter with AI language models and let them carry out standard user operations. Three different strategies were implemented for the bots: a trend-targeting strategy (TTS), a keywords-targeting strategy (KTS) and a user-targeting strategy (UTS). We examined the interaction patterns such as targeting users, spreading messages, propagating relationships, and engagement. We focused on the emergent local structures or motifs and found that the strategies of the social bots had a significant impact on them. Motifs resulting from interactions with bots following TTS or KTS are simple and show significant overlap, while those resulting from interactions with UTS-governed bots lead to more complex motifs. These findings provide insights into human-bot interaction patterns in online social networks, and can be used to develop more effective bots for beneficial tasks and to combat malicious actors. [ABSTRACT FROM AUTHOR]

Cities are widely considered as the lifeblood of a nations' economy housing the bulk of industries, commercial and trade activities, and employment opportunities. Within this economic context, multinational corporations play an important role in this economic development of cities in particular, and subsequently the countries and regions they belong to, in general. As multinational companies are spread throughout the world by virtue of ownership–subsidiary relationship, these ties create complex inter-dependent networks of cities that shape and define socio-economic status, as well as macro-regional influences impacting the world economy. In this paper, we study these networks of cities formed as a result of ties between multinational firms. We analyze these networks using intra-regional, inter-regional, and hybrid ties (conglomerate integration) as spatial motifs defined by geographic delineation of world's economic regions. We attempt to understand how global cities position themselves in spatial and economic geographies and how their ties promote regional integration along with global expansion for sustainable growth and economic development. We study these networks over four time periods from 2010 to 2019 and discover interesting trends and patterns. The most significant result is the domination of inter-regional motifs representing cross-regional ties among cities rather than national and regional integration. [ABSTRACT FROM AUTHOR]

Network motif analysis approaches provide insights into the complexity of the brain's functional network. In recent years, attention-deficit/hyperactivity disorder (ADHD) has been reported to result in abnormal information interactions in macro- and micro-scale functional networks. However, most existing studies remain limited due to potentially ignoring meso-scale topology information. To address this gap, we aimed to investigate functional motif patterns in ADHD to unravel the underlying information flow and analyze motif-based node roles to characterize the different information interaction methods for identifying the abnormal and changing lesion sites of ADHD. The results showed that the interaction functions of the right hippocampus and the right amygdala were significantly increased, which could lead patients to develop mood disorders. The information interaction of the bilateral thalamus changed, influencing and modifying behavioral results. Notably, the capability of receiving information in the left inferior temporal and the right lingual gyrus decreased, which may cause difficulties for patients in processing visual information in a timely manner, resulting in inattention. This study revealed abnormal and changing information interactions based on network motifs, providing important evidence for understanding information interactions at the meso-scale level in ADHD patients. [ABSTRACT FROM AUTHOR]

Synchronous dynamics play a pivotal role in various cognitive processes. Previous studies extensively investigate noise-induced synchrony in coupled neural oscillators, with a focus on scenarios featuring uniform noise and equal coupling strengths between neurons. However, real-world or experimental settings frequently exhibit heterogeneity, including deviations from uniformity in coupling and noise patterns. This study investigates noise-induced synchrony in a pair of coupled excitable neurons operating in a heterogeneous environment, where both noise intensity and coupling strength can vary independently. Each neuron is an excitable oscillator, represented by the normal form of Hopf bifurcation (HB). In the absence of stimulus, these neurons remain quiescent but can be triggered by perturbations, such as noise. Typically, noise and coupling exert opposing influences on neural dynamics, with noise diminishing coherence and coupling promoting synchrony. Our results illustrate the ability of asymmetric noise to induce synchronization in such coupled neural oscillators, with synchronization becoming increasingly pronounced as the system approaches the excitation threshold (i.e., HB). Additionally, we find that uneven coupling strengths and noise asymmetries are factors that can promote in-phase synchrony. Notably, we identify an optimal synchronization state when the absolute difference in coupling strengths is maximized, regardless of the specific coupling strengths chosen. Furthermore, we establish a robust relationship between coupling asymmetry and the noise intensity required to maximize synchronization. Specifically, when one oscillator (receiver neuron) receives a strong input from the other oscillator (source neuron) and the source neuron receives significantly weaker or no input from the receiver neuron, synchrony is maximized when the noise applied to the receiver neuron is much weaker than that applied to the source neuron. These findings reveal the significant connection between uneven coupling and asymmetric noise in coupled neuronal oscillators, shedding light on the enhanced propensity for in-phase synchronization in two-neuron motifs with one-way connections compared to those with two-way connections. This research contributes to a deeper understanding of the functional roles of network motifs that may serve within neuronal dynamics.

Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator for these networks. This is achieved through data depth-based core decomposition and centered motif discovery, ensuring scalability. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness by analyzing and detecting three recent real-world incidents from our datasets: the catastrophic collapse of LunaTerra, the Proof-of-Stake switch of Ethereum, and the temporary peg loss of USDC–while also verifying our results against external ground truth. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis in a scalable manner, while being more effective and efficient than baselines and state-of-the-art attributed change detection approach in dynamic graphs.

Success or failure of a polycentric system is a function of complex political and social processes, such as coordination between actors and venues to solve specialized policy problems. Yet there is currently no accepted method for isolating distinct processes of coordination, nor to understand how their variance affects polycentric governance performance. We develop and test a building‐blocks approach that uses different patterns or "motifs" for measuring and comparing coordination longitudinally on Australia's Great Barrier Reef. Our approach confirms that polycentric governance comprises an evolving substrate of interdependent venues and actors over time. However, while issue specialization and actor participation can be improved through the mobilization of venues, such a strategy can also fragment overall polycentric capacity to resolve conflict and adapt to new problems. A building‐blocks approach advances understanding and practice of polycentric governance by enabling sharper diagnosis of internal dynamics in complex environmental governance systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cells in diverse organisms can store the information of previous environmental conditions for long periods of time. This form of cellular memory adjusts the cells responses to future challenges, providing fitness advantages in fluctuating environments. Many biological functions, including cellular memory, are mediated by specific recurring patterns of interactions among proteins and genes, known as network motifs. In this review, we focus on three well-characterized network motifs - negative feedback loops, positive feedback loops, and feedforward loops, which underlie different types of cellular memories. We describe the latest studies identifying these motifs in various molecular processes and discuss how the topologies and dynamics of these motifs can enable memory encoding and storage.

GTPases are molecular switches that regulate a wide range of cellular processes, such as organelle biogenesis, position, shape, function, vesicular transport between organelles, and signal transduction. These hydrolase enzymes operate by toggling between an active ("ON") guanosine triphosphate (GTP)-bound state and an inactive ("OFF") guanosine diphosphate (GDP)-bound state; such a toggle is regulated by GEFs (guanine nucleotide exchange factors) and GAPs (GTPase activating proteins). Here we propose a model for a network motif between monomeric (m) and trimeric (t) GTPases assembled exclusively in eukaryotic cells of multicellular organisms. We develop a system of ordinary differential equations in which these two classes of GTPases are interlinked conditional to their ON/OFF states within a motif through coupling and feedback loops. We provide explicit formulae for the steady states of the system and perform classical local stability analysis to systematically investigate the role of the different connections between the GTPase switches. Interestingly, a coupling of the active mGTPase to the GEF of the tGTPase was sufficient to provide two locally stable states: one where both active/inactive forms of the mGTPase can be interpreted as having low concentrations and the other where both m- and tGTPase have high concentrations. Moreover, when a feedback loop from the GEF of the tGTPase to the GAP of the mGTPase was added to the coupled system, two other locally stable states emerged. In both states the tGTPase is inactivated and active tGTPase concentrations are low. Finally, the addition of a second feedback loop, from the active tGTPase to the GAP of the mGTPase, gives rise to a family of steady states that can be parametrized by a range of inactive tGTPase concentrations. Our findings reveal that the coupling of these two different GTPase motifs can dramatically change their steady-state behaviors and shed light on how such coupling may impact signaling mechanisms in eukaryotic cells.

How spiking neuronal networks encode memories in their different time and spatial scales constitute a fundamental topic in neuroscience and neuro-inspired engineering. Much attention has been paid to large networks and long-term memory, for example in models of associative memory. Smaller circuit motifs may play an important complementary role on shorter time scales, where broader network effects may be of less relevance. Yet, compact computational models of spiking neural networks that exhibit short-term volatile memory and actively hold information until their energy source is switched off, seem not fully understood. Here we propose that small spiking neural circuit motifs may act as volatile memory components. A minimal motif consists of only two interconnected neurons – one self-connected excitatory neuron and one inhibitory neuron – and realizes a single-bit volatile memory. An excitatory, delayed self-connection promotes a bistable circuit in which a self-sustained periodic orbit generating spike trains co-exists with the quiescent state of no neuron spiking. Transient external inputs may straightforwardly induce switching between those states. Moreover, the inhibitory neuron may act as an autonomous turn-off switch. It integrates incoming excitatory pulses until a threshold is reached after which the inhibitory neuron emits a spike that then inhibits further spikes in the excitatory neuron, terminating the memory. Our results show how external bits of information (excitatory signal), can be actively held in memory for a pre-defined amount of time. We show that such memory operations are robust against parameter variations and exemplify how sequences of multidimensional input signals may control the dynamics of a many-bits memory circuit in a desired way.

In recent years, international energy investment and energy trade activities have developed rapidly. Because energy has commodity and financial product attributes, there is often a correlation between international energy trade and investment. This correlation has regional specificity due to the uneven geographical distribution of energy production and consumption. The existing literature mainly studies the correlation between the international energy “investment–trade” systems from a macroscopic or microscopic perspective. However, the relationship between them among countries from a mesoscopic perspective has not been fully demonstrated. With the development of economic globalization, we need to pay attention to whether the energy trade model of countries can reflect their preference for choosing investment partners and whether the energy investment model can reflect energy trade cooperation. In this paper, by taking the frontier approach of network motifs, we analyzed the correlation between international energy trade and investment from more than 200 economies worldwide from the macroscopic perspective, microscopic perspective, and local structure from a mesoscopic perspective. Meanwhile, we compared the results of the study in 2018 with those in 2022 to obtain the impact of international events on the international energy “investment–trade” networks. We found that 1) the structures of energy trade and investment networks are similar from a macroscopic perspective, which is the basis for exploring the correlation between energy trade and investment. 2) Bilateral cooperation and transaction transmission are important local structures of energy trade and energy investment activities. 3) The formation of an equal and close local structure among economies in energy trade is more likely to be preferred for investment cooperation, and forming a representative local structure with statistical significance among economies in energy investment is more likely to obtain energy trade cooperation. This work innovatively adopts motifs to study the correlation between energy investment and trade, which can help energy investors predict the direction of investment and provide guidance to governments in formulating energy trade policies.

Networks with absolute concentration robustness (ACR) have the property that a translation of a coordinate hyperplane either contains all steady states (static ACR) or attracts all trajectories (dynamic ACR). The implication for the underlying biological system is robustness in the concentration of one of the species independent of the initial conditions as well as independent of the concentration of all other species. Identifying network conditions for dynamic ACR is a challenging problem. We lay the groundwork in this paper by studying small reaction networks, those with two reactions and two species. We give a complete classification by ACR properties of these minimal reaction networks. The dynamics are rich even within this simple setting. Insights obtained from this work will help illuminate the properties of more complex networks with dynamic ACR. [ABSTRACT FROM AUTHOR]

Ecological processes leave distinct structural imprints on the species interactions that shape the topology of animal–plant mutualistic networks. Detecting how direct and indirect interactions between animals and plants are organised is not trivial since they go beyond pairwise interactions, but may get blurred when considering global network descriptors.Recent work has shown that the meso‐scale, the intermediate level of network complexity between the species and the global network, can capture this important information. The meso‐scale describes network subgraphs representing patterns of direct and indirect interactions between a small number of species, and when these network subgraphs differ statistically from a benchmark, they are often referred to as 'network motifs'. Although motifs can capture relevant ecological information of species interactions, they remain overlooked in natural plant–pollinator networks.By exploring 60 empirical plant–pollinator networks from 18 different studies with wide geographical coverage, we show that some network subgraphs are consistently under‐ or over‐represented, suggesting the presence of worldwide network motifs in plant–pollinator networks. In addition, we found a higher proportion of densely connected network subgraphs that, based on previous findings, could reflect that species relative abundances are the main driver shaping the structure of the meso‐scale on plant–pollinator communities. Moreover, we found that distinct subgraph positions describing species ecological roles (e.g. generalisation and number of indirect interactions) are occupied by different groups of animal and plant species representing their main life‐history strategies (i.e. functional groups). For instance, we found that the functional group of 'bees' was over‐represented in subgraph positions with a lower number of indirect interactions in contrast to the rest of floral visitors groups. Finally, we show that the observed functional group combinations within a subgraph cannot be retrieved from their expected probabilities (i.e. joint probability distributions), indicating that plant and floral visitor associations within subgraphs are not random either.Our results highlight the presence of common network motifs in plant–pollinator communities that are formed by a non‐random association of plants and floral visitors functional groups. [ABSTRACT FROM AUTHOR]

Inverse stochastic resonance (ISR) is a modality of nonlinear response to noise, there is the biggest inhibitory effect of noise on neural electrical activity when the ISR happens. In this paper, the discharge activity of a triple-neuron feed-forward-loop (FFL) motif is investigated under the Gaussian colored noise and electromagnetic induction, where the FFL motif is constructed by Izhikevich neurons and connected by chemical synapse and the FFL motifs are classified into four types by the character of synaptic current. Here, the ISR induced by the Gaussian colored noise and electromagnetic induction is focused and various effects of different system parameters on ISR have been found. The most prominent ISR phenomenon will be seen in the case of low-input current and low cross-correlation ratio. There are no significant differences in the ISR curves for various chemical coupling strengths and chemical synapse delays, which conforms to the dynamic mechanisms of the ISR behavior. Besides, the ISR phenomenon also ensues under low electromagnetic induction levels and the effects of electromagnetic induction on the ISR are discussed. The results found here provide a novel perspective about the inhibitory effect on neural motif, which might help the biologists and pathologists understand some complex physiological phenomena of the nervous systems. [ABSTRACT FROM AUTHOR]

Network motif analysis approaches provide insights into the complexity of the brain’s functional network. In recent years, attention-deficit/hyperactivity disorder (ADHD) has been reported to result in abnormal information interactions in macro- and micro-scale functional networks. However, most existing studies remain limited due to potentially ignoring meso-scale topology information. To address this gap, we aimed to investigate functional motif patterns in ADHD to unravel the underlying information flow and analyze motif-based node roles to characterize the different information interaction methods for identifying the abnormal and changing lesion sites of ADHD. The results showed that the interaction functions of the right hippocampus and the right amygdala were significantly increased, which could lead patients to develop mood disorders. The information interaction of the bilateral thalamus changed, influencing and modifying behavioral results. Notably, the capability of receiving information in the left inferior temporal and the right lingual gyrus decreased, which may cause difficulties for patients in processing visual information in a timely manner, resulting in inattention. This study revealed abnormal and changing information interactions based on network motifs, providing important evidence for understanding information interactions at the meso-scale level in ADHD patients.

Abstract The design of robust supply and distribution systems is one of the fundamental challenges at the interface of network science and logistics. Given the multitude of performance criteria, real-world constraints, and external influences acting upon such a system, even formulating an appropriate research question to address this topic is non-trivial. Here we present an abstraction of a supply and distribution system leading to a minimal model, which only retains stylized facts of the systemic function and, in this way, allows us to investigate the generic properties of robust supply networks. On this level of abstraction, a supply and distribution system is the strategic use of transportation to eliminate mismatches between production patterns (i.e., the amounts of goods produced at each production site of a company) and demand patterns (i.e., the amount of goods consumed at each location). When creating networks based on this paradigm and furthermore requiring the robustness of the system with respect to the loss of transportation routes (edge of the network) we see that robust networks are built from specific sets of subgraphs, while vulnerable networks display a markedly different subgraph composition. Our findings confirm a long-standing hypothesis in the field of network science, namely, that network motifs—statistically over-represented small subgraphs—are informative about the robust functioning of a network. Also, our findings offer a blueprint for enhancing the robustness of real-world supply and distribution systems.

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]

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.

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.

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.

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]