Small-world Structure Research Articles

Network analysis reveals context-dependent structural complexity of social calls in serrate-legged small treefrogs.

Vocal communication plays an important role in survival, reproduction, and animal social association. Birds and mammals produce complex vocal sequence to convey context-dependent information. Vocalizations are conspicuous features of the behavior of most anuran species (frogs and toads), and males usually alter their calling strategies according to ecological context to improve the attractiveness/competitiveness. However, very few studies have focused on the variation of vocal sequence in anurans. In the present study, we used both conventional method and network analysis to investigate the context-dependent vocal repertoire, vocal sequence, and call network structure in serrate-legged small treefrogs Kurixalus odontotarsus. We found that male K. odontotarsus modified their vocal sequence by switching to different call types and increasing repertoire size in the presence of a competitive rival. Specifically, compared with before and after the playback of advertisement calls, males emitted fewer advertisement calls, but more aggressive calls, encounter calls, and compound calls during the playback period. Network analysis revealed that the mean degree, mean closeness, and mean betweenness of the call networks significantly decreased during the playback period, which resulted in lower connectivity. In addition, the increased proportion of one-way motifs and average path length also indicated that the connectivity of the call network decreased in competitive context. However, the vocal sequence of K. odontotarsus did not display a clear small-world network structure, regardless of context. Our study presents a paradigm to apply network analysis to vocal sequence in anurans and has important implications for understanding the evolution and function of sequence patterns.

Condensates formed by prion-like low-complexity domains have small-world network structures and interfaces defined by expanded conformations

Biomolecular condensates form via coupled associative and segregative phase transitions of multivalent associative macromolecules. Phase separation coupled to percolation is one example of such transitions. Here, we characterize molecular and mesoscale structural descriptions of condensates formed by intrinsically disordered prion-like low complexity domains (PLCDs). These systems conform to sticker-and-spacers architectures. Stickers are cohesive motifs that drive associative interactions through reversible crosslinking and spacers affect the cooperativity of crosslinking and overall macromolecular solubility. Our computations reproduce experimentally measured sequence-specific phase behaviors of PLCDs. Within simulated condensates, networks of reversible inter-sticker crosslinks organize PLCDs into small-world topologies. The overall dimensions of PLCDs vary with spatial location, being most expanded at and preferring to be oriented perpendicular to the interface. Our results demonstrate that even simple condensates with one type of macromolecule feature inhomogeneous spatial organizations of molecules and interfacial features that likely prime them for biochemical activity.

Maritime accidents in the Yangtze River: A time series analysis for 2011–2020

The theoretical analysis of maritime accidents is a hot topic, but the time characteristics and dynamics of maritime accidents time series are still unclear. It is difficult to draw a clear conclusion from the cause analysis, so the accident is difficult to be predicted. To bridge this gap, this research analyzes the characteristics and evolution mechanism of maritime accidents time series from the perspective of complex network theory. The visual graph algorithm is used to model the complex network of maritime accidents data in 22 jurisdictions of the Yangtze River, map the time series into a complex network, and reveal the time characteristics and dynamics of maritime accidents time series based on the complex system theory. In the empirical analysis, degree distribution, clustering coefficient and network diameter are used to analyze the characteristics of time series. The results show that the degree distribution of maritime accidents time series network presents power-law characteristics in the macro and micro levels, which shows that the maritime accidents time series is scale-free. In addition, according to the clustering coefficient and network diameter, maritime accidents time series in the Yangtze River has the characteristics of small-world and hierarchical structure. The research of this manuscript shows that the occurrence of maritime accidents is not random events and does not follow specific patterns but presents the characteristics of complex systems, and this phenomenon is common. The analysis of maritime accidents time series by complex network theory can provide theoretical support for maritime traffic safety management.

Efficient SfM for Large-Scale UAV Images Based on Graph-Indexed BoW and Parallel-Constructed BA Optimization

Structure from Motion (SfM) for large-scale UAV (Unmanned Aerial Vehicle) images has been widely used in the fields of photogrammetry and computer vision. Its efficiency, however, decreases dramatically as well as with the memory occupation rising steeply due to the explosion of data volume and the iterative BA (bundle adjustment) optimization. In this paper, an efficient SfM solution is proposed to solve the low-efficiency and high memory consumption problems. First, an algorithm is designed to find UAV image match pairs based on a graph-indexed bag-of-words (BoW) model (GIBoW), which treats visual words as vertices and link relations as edges to build a small-world graph structure. The small-world graph structure can be used to search the nearest-neighbor visual word for query features with extremely high efficiency. Reliable UAV image match pairs can effectively improve feature matching efficiency. Second, a central bundle adjustment with object point-wise parallel construction of the Schur complement (PSCBA) is proposed, which is designed as the combination of the LM (Levenberg–Marquardt) algorithm with the preconditioned conjugate gradients (PCG). The PSCBA method can dramatically reduce the memory consumption in both error and normal equations, as well as improve efficiency. Finally, by using four UAV datasets, the effectiveness of the proposed SfM solution is verified through comprehensive analysis and comparison. The experimental results show that compared with Colmap-Bow and Dbow2, the proposed graph index BoW retrieval algorithm improves the efficiency of image match pair selection with an acceleration ratio ranging from 3 to 7. Meanwhile, the parallel-constructed BA optimization algorithm can achieve accurate bundle adjustment results with an acceleration ratio by 2 to 7 times and reduce memory occupation by 2 to 3 times compared with the BA optimization using Ceres solver. For large-scale UAV images, the proposed method is an effective and reliable solution.

Shortening the distance between firms for M&A: the influence of small-world network structure on mergers and acquisitions

PurposeA small-world network is a type of network structure in which nodes are highly clustered and at short distances without being directly linked. This article analyzes whether the network of interlocking directorates among the largest Brazilian corporations follows a small-world network structure and if the small-world properties (high clustering and short distance between nodes) influence the occurrence of M&A at the domestic and international level.Design/methodology/approachThe authors tested hypotheses regarding the relationship between small-world network properties and M&A based on a sample of large publicly-listed corporations in Brazil for the time series of 2000–2015 and using network analysis and regression techniques (probit and OLS).FindingsThe results show that while the Brazilian corporate network fits the small-world features of high clustering and short path lengths, only the distance among connected firms has a significant effect on international M&A: the shorter the distance between firms, the more likely firms undertake M&A abroad. Moreover, being integrated into the main component has a significant positive effect on national and international M&A. These findings suggest that the information and knowledge to undertake M&A can be better acquired by belonging to large business communities and not local cohesive clusters.Originality/valueThis research contributes to theories and ongoing debates about the network effects on organizational decisions and the determinants of M&A in emerging markets. In addition, this is the first study to analyze the impact of small-world networks on international M&A while controlling for country-level variables.

Health behavior homophily can mitigate the spread of infectious diseases in small-world networks

Research has repeatedly shown that the spread of infectious diseases is influenced by properties of our social networks. Small-world like structures with densely connected clusters bridged by only a few connections, for example, are not only known to diminish disease spread, but also to increase the chance for a disease to spread to any part of the network. Clusters composed of individuals who show similar reactions to avoid infections (health behavior homophily), however, might change the effect of such clusters on disease spread. To study the combined effect of health behavior homophily and small-world network properties on disease spread, we extend a previously developed ego-centered network formation model and agent-based simulation. Based on more than 80,000 simulated epidemics on generated networks varying in clustering and homophily, as well as diseases varying in severity and infectivity, we predict that the existence of health behavior homophilous clusters reduce the number of infections, lower peak size, and flatten the curve of active cases. That is because agents perceiving higher risks of infections can protect their cluster from infections comparatively quickly by severing only a few bridging ties. A comparison with epidemics in static network structures shows that the incapability to act upon risk perceptions and the low connectivity between clusters in static networks lead to diametrically opposed effects with comparatively large epidemics and prolonged epidemics. These finding suggest that micro-level behavioral adaptation to health risks mitigate macro-level disease spread to an extent that is not captured by static network models of disease spread. Furthermore, this mechanism can be used to design information campaigns targeting proxies for groups with lower risk perception.

Network analysis of pig movements in Ecuador: Strengthening surveillance of classical swine fever.

The analysis of domestic pig movements has become useful to understand the disease spread patterns and epidemiology, which facilitates the development of more effective animal diseases control strategies. The aim of this work was to analyse the static and spatial characteristics of the pig network, to identify its trading communities and to study the contribution of the network to the transmission of classical swine fever. In this regard, we used the pig movement records from the National Veterinary Service of Ecuador (2017-2019), using social network analysis and spatial analysis to construct a network with registered premises as nodes and their movements as edges. Furthermore, we also created a network of parishes as its nodes by aggregating their premises movements as edges. The annual network metrics showed an average diameter of 20.33, a number of neighbours of 2.61, a shortest path length of 4.39 and a clustering coefficient of 0.38 (small-world structure). The most frequent movements were to or from markets (55%). Backyard producers made up 89% of the network premises, and the top 2% of parishes (highest degree) contributed to 50% of the movements. The highest frequencies of movements between parishes were in the centre of the country, while the highest frequency of movements to abattoirs was in the south-west. Finally, the pattern of classical swine fever (CSF) disease outbreaks within the Ecuador network was likely the result of network transmission processes. In conclusion, our results represented the first exploratory analysis of domestic pig movements at premise and parish levels. The surveillance system could consider these results to improve its procedures and update the disease control and management policy, and allow the implementation of targeted or risk-based surveillance.

Small world can alleviate the social dilemmas originating from self-regulation or community policing issues

Self-regulation and government regulation, community policing and external powerful forces, respectively, form vital part of today’s global economy and social management. The implementation of regulation from self-regulation or community policing requires considerable disciplined members to share the costs of execution, while government or external powerful forces are more likely to make an strong intervention as the industry or community is being widely eroded, which are thus two definitely different feedback regulation schemes. Together with those wrong-doers, the organizations of the two feedback regulation schemes consist of both first-order and novel second-order social dilemma. It still remains a significant challenge to identify an optimal structure to simultaneously alleviate the two dilemmas, and even to additionally favor the altruistic individuals. To address this issue, in this paper we adopt an evolutionary game model within the framework of the public good model, by considering defector-driven and cooperator-driven punishment to map the two feedback regulation schemes, respectively. Through a series of extensive Monte Carlo simulation experiments and mean-field analysis based on replicator dynamics, we finally uncover the combination of clustering and shortcuts, say the small-world structure, is required to largely alleviate both first-order and novel second-order dilemma, which can not only yield optimal public goods by successfully eliminating defectors through strong network reciprocity from dense local connections, but also provide sufficient shortcuts to promote the dominance of defector-driven punishers. Our findings can be beneficial for improving self-regulation or community policing systems by constructing novel monitoring networks.

STRUCTURAL EVOLUTION OF KNOWLEDGE NETWORK AND BEHAVIOR VARIATION OF KNOWLEDGE SUBJECT DRIVEN BY EMOTIONAL BEHAVIOR

BackgroundThe perception behavior of the knowledge subjects can get certain feedback and enlightenment from the trend variation of knowledge level. The change of the psychological expectation and behavior of the knowledge subjects is of great significance to the evolution and utilization of the knowledge network structure in the complex system. At present, there have been many mature researches on the content of knowledge flow and knowledge diffusion between organizations based on network structure. This article combines the relevant theories of micro-behavioral psychology from the perspective of complex system evolution, to further explore the influences of four typical complex network structures evolution on the knowledge transfer behavior of the knowledge subjects and the knowledge level of the subjects.Subjects and MethodsIn this paper, a simulation research process is designed based on a complex knowledge network model. The calculation indicators mainly include three aspects: network value, node coupling costs, and weight values of relational edges. The node coupling costs are composed of three dimensions: the psychological distance cost between network nodes, the difference in cooperation value between network nodes, and the difference in connectivity between network nodes. Using programming, four programs are used to simulate and analyze the evolution of complex network structure based on the coupling cost of the knowledge subjects and the psychological expectations and behavior changes of the knowledge subjects.ResultsThe results of this paper show that the more frequent the interaction between network subjects, which is not necessarily greater beneficial to the knowledge subjects. In addition, the fair distribution of knowledge resources is not conducive to the growth of knowledge level. Among the four types of typical network evolution, the development of the small-world network structure is relatively stable, and the adaptive behavior of the knowledge subjects has produced relatively high expected value.ConclusionsThe stability of the network environment has an exogenous impact on the expectation and value level of the knowledge subjects. The development environment of the evolution of the small-world network structure is relatively stable, which can generate high psychological expectations for the adaptive behavior of the knowledge subjects. It provides a reference value for the knowledge transfer of the knowledge subjects in the evolution of the knowledge network. This article discusses the behavioral changes of network subjects from the perspective of the psychology of knowledge subjects, which has certain practical significance. However, it does not consider the initial endowment differences between the nodes and the alternate evolution process between complex networks. Therefore, follow-up researches can further optimize the understanding on the differences between subjects in the complex network and analyze the adaptive behavior of knowledge subjects.AcknowledgementsSupported by projects grant from National Natural Science Foundation of China (Grant No. 71602012); Humanities and Social Sciences Fund of the Ministry of Education (Grant No. 21YJC790122); and Philosophy and Social Science Fund Project of CDUT (Grant No. YJ2020-JX002).

Structural stability of the evolving developer collaboration network in the OSS community.

The structural stability of the developer collaboration network is critical to the success of the OSS (Open Source Software) community. However, research on the structural stability of the evolving developer collaboration network in OSS communities is relatively insufficient. In this paper, according to the software version sequence, we construct the corresponding developer collaboration network of the Angular OSS community and then analyse this network’s structural stability during network evolution. The results show that the network always presents an economical modular small-world structure during its evolution. The maintenance of the structure is related to a cohesive core, which is composed of two types of nodes (i.e., hubs and connectors). The hubs organize noncore nodes to form modules, while connectors facilitate the formation of inter-module connections. The overall results highlight the important role of core developers in the sustainable development of OSS communities and may provide a reference for community initiators to implement protection strategies for core developers.

Evolution of Cortical Functional Networks in Healthy Infants.

During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.

Adaptive rewiring in nonuniform coupled oscillators.

Structural plasticity of the brain can be represented in a highly simplified form as adaptive rewiring, the relay of connections according to the spontaneous dynamic synchronization in network activity. Adaptive rewiring, over time, leads from initial random networks to brain-like complex networks, that is, networks with modular small-world structures and a rich-club effect. Adaptive rewiring has only been studied, however, in networks of identical oscillators with uniform or random coupling strengths. To implement information-processing functions (e.g., stimulus selection or memory storage), it is necessary to consider symmetry-breaking perturbations of oscillator amplitudes and coupling strengths. We studied whether nonuniformities in amplitude or connection strength could operate in tandem with adaptive rewiring. Throughout network evolution, either amplitude or connection strength of a subset of oscillators was kept different from the rest. In these extreme conditions, subsets might become isolated from the rest of the network or otherwise interfere with the development of network complexity. However, whereas these subsets form distinctive structural and functional communities, they generally maintain connectivity with the rest of the network and allow the development of network complexity. Pathological development was observed only in a small proportion of the models. These results suggest that adaptive rewiring can robustly operate alongside information processing in biological and artificial neural networks.

Can weak diversity help in propagating cooperation? Invasion of cooperators at the conformity-conflict boundary

The influence of social diversity on cooperation promotion has been frequently studied in the literature. This work classifies the diversity into strong and weak based on its impact on payoffs. Strong diversity is typically known to the players and relates to, for example, the scope of the players’ neighborhoods. In contrast, weak diversity's impact is irrelevant to the payoff and thus may be ignored by the agents. In this work, a model with both strong and weak diversity is set up to study the impact of weak diversity in the presence of strong diversity. Additionally, a conformity-conflict model (i.e., a model with a designed boundary of weak diversity) is used to study the weak diversity's impact on the level of cooperation among agents. While strong diversity defines basic cooperation patterns, weak diversity adds extra influence on the cooperation but depends on the players’ payoff sensitivity. When the dilemma strength is low, weak diversity enables the cooperation level to increase gradually over a long period. Simulation results highlight a specific pattern: the clusters formed at the equilibrium may be “distorted” into expanding ones due to the impact of the weak diversity boundary. Such propagation of cooperation explains the impact of weak diversity on the global change in cooperation. Thus, weak diversity allows modeling a gradual variation in features using deterministic evolutionary games. Such an impact of weak diversity can also be observed in the network structure with long-range connections (such as the small-world structure).

Compression and symmetry of small-world graphs and structures

For various purposes and, in particular, in the context of data compression, a graph can be examined at three levels. Its structure can be described as the unlabeled version of the graph; then the labeling of its structure can be added; and finally, given then structure and labeling, the contents of the labels can be described. Determining the amount of information present at each level and quantifying the degree of dependence between them, requires the study of symmetry, graph automorphism, entropy, and graph compressibility. In this paper, we focus on a class of small-world graphs. These are geometric random graphs where vertices are first connected to their nearest neighbors on a circle and then pairs of non-neighbors are connected according to a distance-dependent probability distribution. We establish the degree distribution of this model, and use it to prove the model's asymmetry in an appropriate range of parameters. Then we derive the relevant entropy and structural entropy of these random graphs, in connection with graph compression.

Analysis and Optimization of Network Properties for Bionic Topology Hopfield Neural Network Using Gaussian-Distributed Small-World Rewiring Method

The fully connected topology, which coordinates the connection of each neuron with all other neurons, remains the most commonly used structure in Hopfield-type neural networks. However, fully connected neurons may form a highly complex network, resulting in a high training cost and making the network biologically unrealistic. Biologists have observed a small-world topology with sparse connections in the actual brain cortex. The bionic small-world neural network structure has inspired various application scenarios. However, in previous studies, the long-range wirings in the small-world network have been found to cause network instability. In this study, we investigate the influence of neural network training on the small-world topology. The role of the path length and clustering coefficient of neurons is expounded in the neural network training process. We employ Watt and Strogatz’s small-world model as the topology for the Hopfield neural network and conduct computer simulations. We observe that the random existence of neuron connections may cause unstable network energies and generate oscillations during the training process. A new method is proposed to mitigate the instability of small-world networks. The proposed method starts with a neuron as the pattern centroid along the radial, which arranges its wirings in compliance with the Gaussian distribution. The new method is tested on the MNIST handwritten digit dataset. The simulation confirms that the new small-world series has higher stability in terms of the learning accuracy and a higher convergence speed compared with Watt and Strogatz’s small-world model.

Risk spillover analysis across worldwide ESG stock markets: New evidence from the frequency-domain

With the increasing global awareness of green environmental protection, the international environmental, social, and governance (ESG) stock markets are developing rapidly together with rising risk linkages across worldwide markets. Therefore, this study explores the risk spillover characteristics of international ESG stock markets in the time and frequency domains and constructs a risk linkage network to further explore the risk contagion mechanism. The results show that in most cases, the developed North American market is the core of outward risk spillover in international ESG stock markets. The entire system presents a small-world structure, and the internal regions display different risk spillover characteristics. Moreover, international ESG markets generally have strong time–frequency spillover and medium-frequency (a month to a year) spillover. In contrast, thehigh- (a day to a month) and low-frequency (more than one year) spillovers are located at relatively low levels, but they will rise significantly under sudden financial events. The empirical results expand the ESG stock market's theoretical framework and provide a reference for investors and market regulators to reduce the investment risk of ESG.

Structural analysis of relevance propagation models

Relevance relations constitute the core of information retrieval. Topical ontologies, such as collaborative webpage classification projects, can provide a basis for identifying and analyzing such relations. New meaningful relevance relations can be automatically inferred from these ontologies by composing existing ones. In this work, several relevance propagation models are analyzed in terms of complex network theory. Structural properties such as Characteristic path length, Clustering coefficient and Degree distribution are computed over the models in order to understand the nature of each underlying network. This analysis raises interesting points about the Small-world and Scale-free structure of some relevance propagation models. Moreover, other connectivity and centrality measures are computed to gain additional insight into the topology of relevance. Finally, the analysis is complemented by providing visualizations of the k-core decomposition of different relevance propagation models. To illustrate the generalizability of the proposed methodology the analysis is carried out on an ontology from a different domain. The major theoretical implication of this analysis is the derivation of new instruments to typify semantic networks derived from relevance relations. The results can be exploited in a pragmatic way, as the parameters and properties derived by this analysis can serve as prior knowledge to algorithms for the automatic or semi-automatic construction of semantic networks.

Small-world Structure in Children’s Featured Semantic Networks

Background: Knowing the development pattern of children’s language is applicable in developmental psychology. Network models of language are helpful for the identification of these patterns. Objectives: We examined the small-world properties of featured semantic networks of developing children. Materials & Methods: In this longitudinal study, the featured semantic networks of children aged 18-30 months were obtained using R software version 3.5.2 and the igraph software package. The data of 2000 English (British)-speaking children, half boy and half girls, were gathered from existing databases of MCDI (between 2000 and 2007) and McRae feature norms. The growth pattern of these networks was illustrated by graph measures. Comparing these measures with those of the reference random networks, the small-world structure can be examined. Results: To have a comparison between path length and clustering coefficient of featured semantic networks with those of random networks, we computed the Q quotient. The results showed that the values of the Q quotient at 18, 22, 26, and 30 months of age were all more than 1, which confirms the small-world characteristic of the networks. Conclusion: Featured semantic networks of children exhibited a small-world structure, in which there was a local structure in the form of clusters of words. For global access, some words act as bridges connecting semantically distant clusters. These networks possess small-world property from the early months of age. The small-world structure cannot be seen in the less dense networks built with a higher cut-off threshold.

THE GENERAL OWNERSHIP STRUCTURE OF THE EUROPEAN AEROSPACE INDUSTRY A STATISTICAL AND NETWORK ANALYSIS

In this paper, we investigate the ownership structure of the 3143 EU28 aerospace companies in 2019, and extend the analysis to the 2428 neighbor partners outside EU28 and/or aerospace. Different from the previous studies, we consider all equity capital flows regardless of their size, and their monetary value instead of the corresponding ownership share. We further innovate by applying new methods to measure degree of influence power and hierarchical structure. The resulting picture shows that between the pure EU28 aerospace companies: (i) ownership relationships concern only relatively few companies (10%), which trigger horizontal and vertical structures; (ii) density is extremely low; (iii) relationships are fully hierarchical with no cross-ownership; (iv) capital is seldom transferred across business groups; (v) most of the main topological parameters have a typically polarized scale-free structure. When including also the ownership neighbors, some of those traits change substantially: (i) the share of connected companies substantially grows up to 63%; (ii) size and length of the largest pyramidal structures will grow remarkably, reaching a top of 874 companies; (iii) the industry becomes a full small-world structure, thus allowing huge capital transfer across business groups. Finally, a dramatic financialization, meant as a pivotal and quantitatively heavy role of financial operators, emerges also as a clear characteristic of the extended network.

Smooth graph learning for functional connectivity estimation

Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) signals is important in understanding neural representation and information processing in cortical networks. However, due to a lack of ”ground truth” FC pattern, the reliability and robustness of FC estimates are usually examined in downstream FC analysis tasks, such as performing participant’s identification (also known as ”fingerprinting”). In this paper, we propose to learn FC via a smooth graph learning framework. In particular, we treat each time frame of the fMRI time series as a graph signal on an underlying functional brain graph, and estimate the smooth graph functional connectivity (SGFC) by learning the weighted graph adjacency matrix based on graph signal smoothness assumption. We demonstrate that our approach gives rise to a natural and sparse graph representation of FC from which reliable graph measures can be extracted. Reliability of SGFC is evaluated in the context of fingerprinting and compared to correlation FC (CFC). SGFC achieves higher fingerprinting accuracy across several different experiment settings; the improvement is even more significant when a shorter fMRI scanning length is used for FC estimation. In addition to being reliable, we also validate the cognitive relevance of SGFC by using it to predict fluid intelligence. Finally, in evaluating topological measures of the sparse graph, SGFC reveals a more small-world and modular structure compared to CFC. Together, our results suggest that the smooth graph learning framework produces a naturally sparse, reliable, and cognitive-relevant representation of functional connectivity.