Cliques In Networks Research Articles

Analysis of Correlation Effects of Double Mutations in Enzymes: A Revised Residual-Contact Network Clique Model.

The relationship between amino acid mutations and enzyme bioactivity is a significant challenge in modern bio-industrial applications. Despite many successful designs relying on complex correlations among mutations at different enzyme sites, the underlying mechanisms of these correlations still need to be explored. In this study, we introduced a revised version of the residual-contact network clique model to investigate the additive effect of double mutations based on the mutation occurrence topology, secondary structures, and physicochemical properties. The model was applied to a set of 182 double mutations reported in three extensively studied enzymes, and it successfully identified over 90% of additive double mutations and a majority of non-additive double mutations. The calculations revealed that the mutation additivity depends intensely on the studied mutation sites' topology and physicochemical properties. For example, double mutations on irregular secondary structure regions tend to be non-additive. Our method provides valuable tools for facilitating enzyme design and optimization. The code and relevant data are available at Github.

Prior based Pyramid Residual Clique Network for human body image super-resolution

Recent research in the analysis of human images, such as human parsing and pose estimation, usually requires input images to have a sufficiently high resolution. However, small images of people are commonly encountered in our daily lives, particularly in surveillance applications. This paper aims to ultra-resolve a tiny person image to its high-resolution counterpart by learning effective feature representations and exploiting useful human body prior knowledge. First, we propose the Residual Clique Block (RCB) to fully exploit compact feature representations for image Super-Resolution (SR). Second, a series of RCBs are cascaded in a coarse-to-fine manner to construct the Pyramid Residual Clique Network (PRCN), which simultaneously reconstructs multiple SR results (e.g. 2×, 4×, and 8×) in one feed-forward pass. Third, we utilize the human parsing map as the shape prior, and the high-frequency sub-bands of Uniform Discrete Curvelet Transform (UDCT) as the texture prior to enhance the details of reconstructed human body image. Experimental results demonstrate that our proposed method achieves state-of-the-art performance with superior visual quality and PSNR/SSIM scores. Moreover, we show that our results can considerably enhance the performance of human parsing and pose estimation tasks.

How Women Use Network Strategies to Break Male Elite Cliques and to be Elected on Venture Boards

Based upon a qualitative study of 120 interviews and a statistical analysis of women active in the board scene around VC-backed ventures, we contrast previous claims and the agenda of network programs by and for women who highlight fighting network disadvantages by homophily. By forming cliques with other women, minority disadvantages persist, and these networks are losing rather than gaining efficiency. We outline how women networking with males, and how alternative networking styles break network cliques of men that act as barriers to women. Such networking strategies also contribute to progressive change in the total network reducing stereotypes that are restricting women entrepreneurs.

Gender, care and kinships networks: Family forms in Santiago, Chile

This paper aims to understand how Chileans in Santiago define their families. This study used a mixed methods approach, combining qualitative and quantitative techniques in different stages of the research. First, it was done 16 in-deep-interviews, using name generators in ego-networks as a technique to process the data of the kinship networks of each person interviewed. Later, a metrical network analysis was done to describe the cognitive structures and the relations in the families using a mixed methods approach. The results show that families in Santiago can be described in four typologies: clique networks, cohesive families, families with extended and/or branched networks, and family centre-periphery networks. Furthermore, it concluded that the network families of women have a significant internal cohesion compared to the family networks described by men. In addition, the social network analysis approach was used to demonstrate diversity in the conformation of family forms, defined by the interviewees with their understanding of kinship structures.

Multiscale Alternately Updated Clique Network for Hyperspectral Image Classification

Recently, deep learning has drawn significant attention in hyperspectral image (HSI) classification. With the growth of network depth and feature integration, deep learning demands abundant labeled samples to optimize many parameters. Unfortunately, most hyperspectral data are unlabeled and the available labeled samples are extremely limited. How to obtain richer features under limited training samples is a challenge for HSI classification. To tackle this issue, a new supervised multiscale alternately updated clique network (MSCN) is proposed for HSI classification to fully employ HSI features in different scales. Based on the Clique Block, we design the multiscale alternately updated clique block (MSCB) that applies convolution kernels of various sizes to adaptively exploit the multiscale HSI information and merge them within the block. Meanwhile, the recurrent feedback architecture is introduced to reuse high-level visual information and network parameters. The proposed MSCN includes two MSCBs to capture the multiscale spectral and spatial information in turn. The MSCN improves the information flow and the efficiency of parameter tuning through the feedback mechanism and the cross-utilization of multiscale feature. It not only obtains more abstract HSI information, but also reduces the network depth and the number of parameters, thereby improving the classification accuracy under limited samples. To certify the validity of the proposed MSCN, experiments are conducted on three real HSI datasets and compared with multiple state-of-the-art deep learning-based approaches. The experimental results demonstrate that the presented multiscale network achieves superior performance, especially in the case of a small number of training samples.

Both simulation and sequencing data reveal coinfections with multiple SARS-CoV-2 variants in the COVID-19 pandemic

SARS-CoV-2 is a single-stranded RNA betacoronavirus with a high mutation rate. The rapidly emerging SARS-CoV-2 variants could increase transmissibility and diminish vaccine protection. However, whether coinfection with multiple SARS-CoV-2 variants exists remains controversial. This study collected 12,986 and 4,113 SARS-CoV-2 genomes from the GISAID database on May 11, 2020 (GISAID20May11), and Apr 1, 2021 (GISAID21Apr1), respectively. With single-nucleotide variant (SNV) and network clique analyses, we constructed single-nucleotide polymorphism (SNP) coexistence networks and discovered maximal SNP cliques of sizes 16 and 34 in the GISAID20May11 and GISAID21Apr1 datasets, respectively. Simulating the transmission routes and SNV accumulations, we discovered a linear relationship between the size of the maximal clique and the number of coinfected variants. We deduced that the COVID-19 cases in GISAID20May11 and GISAID21Apr1 were coinfections with 3.20 and 3.42 variants on average, respectively. Additionally, we performed Nanopore sequencing on 42 COVID-19 patients and discovered recurrent heterozygous SNPs in twenty of the patients, including loci 8,782 and 28,144, which were crucial for SARS-CoV-2 lineage divergence. In conclusion, our findings reported SARS-CoV-2 variants coinfection in COVID-19 patients and demonstrated the increasing number of coinfected variants.

Improving Bitcoin Transaction Propagation Efficiency through Local Clique Network

AbstractBitcoin is a popular decentralized cryptocurrency, and the Bitcoin network is essentially an unstructured peer-to-peer (P2P) network that can synchronize distributed database of replicated ledgers through message broadcasting. In the Bitcoin network, the average clustering coefficient of nodes is very high, resulting in low message propagation efficiency. In addition, average node degree in the Bitcoin network is also considerably large, causing high message redundancy when nodes use the gossip protocol to broadcast messages. These may affect message propagation speed, hindering Bitcoin from being applied to scenarios of high transactional throughputs. To illustrate, we have collected single-hop propagation data of transactions of 366 blocks from Bitcoin Core. The analysis results show that transaction verification and network delay are two major causes of low transaction propagation efficiency. In this paper, we propose a novel P2P network structure, called local clique network (LCN), for message broadcasting in the Bitcoin network. Specifically, to reduce transaction validation latency and message redundancy, in LCN local nodes (logically) form cliques, and only a few nodes in a clique broadcast messages to the other cliques, instead of each node sending messages to its neighboring nodes. We have conducted extensive experiments, and the results show that message redundancy is low in LCN, and message propagation speed increases significantly. Meanwhile, LCN exhibits excellent robustness when average node degree remains high in the Bitcoin network.

Network clique cover approximation to analyze complex contagions through group interactions

Contagion processes have been proven to fundamentally depend on the structural properties of the interaction networks conveying them. Many real networked systems are characterized by clustered substructures representing either collections of all-to-all pair-wise interactions (cliques) and/or group interactions, involving many of their members at once. In this work, focusing on interaction structures represented as simplicial complexes, we present a discrete-time microscopic model of complex contagion for a susceptible-infected-susceptible dynamics. Introducing a particular edge clique cover and a heuristic to find it, the model accounts for the higher-order dynamical correlations among the members of the substructures (cliques/simplices). The analytical computation of the critical point reveals that higher-order correlations are responsible for its dependence on the higher-order couplings. While such dependence eludes any mean-field model, the possibility of a bi-stable region is extended to structured populations.

Shannon entropy in time-varying semantic networks of titles of scientific paper

Recent work has employed information theory in social and complex networks. Studies often discuss entropy in the degree distributions of a network. However, no specific work on entropy exists in clique networks. This work is an extension of a previous study that discussed this topic. We propose a method for calculating the entropy of a clique network and its minimum and maximum values in temporal semantic networks based on titles of scientific papers. In addition, the critical network of moments was extracted. We use the titles of scientific papers published in Nature and Science over ten-year period. The results show the diversity of vocabulary over time, based on the entropy values of vertices and edges. In each critical network, we discover the paths that connect important words and an interesting modular structure.

Network Cliques and Organizational level of Influence among Managers: A Social Network Analysis

Responding to demands for transformed networking practices requires new forms of knowledge. Given their scale and complexity, networking problems can no longer be solved by way of extending intermediaries. Recent research on alternative approaches has focused on the understanding network structures formed by interactions between heterogeneous actors. Rather than linear extensions, network theory highlights cliques structure as a major determinant of performance derives largely from the frequent finding that managers that are embedded with influential connections are at increased likelihood of becoming influential themselves thus opening rooms for new knowledge and innovative technology and practices. It is suggested that the strong and consistent cliques are at least partially responsible to managing network complexity. This paper contributes to our understanding of such facilitation by investigating the networks in which managers exchange information. We report findings based on the study of managers in a maritime industry. Network surveys identified who the managers contacted for inputs and who they had talked to about the information by 6 months earlier. Snow-balling interviews collected farmer statements about their most valuable contacts and these statements have been analysed. The network analysis shows that managers with densely tied and occupationally homogeneous contacts grew their networks more than did managers with contacts that are loosely tied and diverse. The network analysis reveals an important principle: managers’s value knowledge delivered by persons of homogenous connections rather distance ones. It is argued that social network analysis is an appropriate method for studying influence development use in the context of networked organizations.

Joint Energy Sustainability and Quality of Service Framework Providing Soft Guarantees for Energy Harvesting Wireless Mesh Networks

We propose a framework of joint energy sustainability and QoS provisioning for energy harvesting Wireless Mesh Networks (WMN) equipped with battery. We consider end-to-end delay and throughput as QoS parameters in the proposed framework. Our framework extends the concept of virtualization to energy resources of a node and dedicates a virtual battery to each connection independently. More importantly, we present a novel approach for coupling delay and throughput requirements to their equivalent virtual battery specification, providing a trade-off between QoS and energy characteristics of a connection. Furthermore, we propose admission tests based on average and instantaneous energy requirements of a connection in relation to its QoS needs. These admission tests ensure an end-to-end soft delay bound which according to our simulations, is violated by merely 2%. Using our framework we also show the strikingly different energy policies that should be adopted for routing interactive and streaming as well as bursty and constant bit-rate applications over energy harvesting WMNs. Obtaining an upper bound on the amount of energy resources required within any network clique, we show how energy constrained networks can benefit from increasing transmission power or even reduced bit-rates, despite intuition. All our claims and discussions are backed both by simulations and analysis.

Wireless Scheduling with Delayed CSI: When Distributed Outperforms Centralized

The performance of wireless scheduling algorithms directly depends on the availability and accuracy of channel state information (CSI) at the scheduler. As CSI updates must propagate across the network, they are delayed as they arrive at the controller. In this paper, we analyze the effect that delayed CSI has on the throughput performance of scheduling in wireless networks. By accounting for the delays in CSI as they relate to the network topology, we revisit the comparison between centralized and distributed scheduling. We explore the tradeoff between optimal centralized scheduling using delayed CSI and imperfect distributed scheduling using timely CSI. In particular, we show that under certain conditions distributed scheduling outperforms the optimal centralized scheduling policy, and we characterize the point at which distributed scheduling outperforms centralized scheduling for tree and clique networks. Lastly, we propose a partially distributed scheme that achieves high throughput amidst delayed CSI.

Equilibrium Distributions of Populations of Biological Species on Networks of Social Sites

ABSTRACTWe investigate the problem of how a population of biological species would distribute over a given network of social sites so that their social contacts through the connected sites can be maximized (or minimized). This problem has applications in modelling the behaviours of social (or solitary) species such as the development of social groups in human society and the spread of solitary animals in distant habitats. We show that this problem can be formulated as an evolutionary game, with the equilibrium state of the game corresponding to a strategy for choosing the residing sites, each with a certain probability, or equivalently, to a distribution of the population on these sites. The game has a symmetric payoff matrix, and can therefore be analyzed via the solution of a corresponding quadratic programme: An equilibrium strategy of the game is a KKT point of the quadratic programme, which may be a local maximizer, local minimizer, or saddle point, but it is evolutionarily stable if and only if it is a strict local maximizer. In general, with a goal to maximize the social contacts, the species tend to spread on network sites where there are dense connections such as a complete subnetwork or in other words, a network clique. We show that at equilibrium, the population may or may not distribute on a network clique, but the stability of the equilibrium state does depend on the structure of the selected subnetwork. In particular, we show that the distribution of the population on a maximal network clique is evolutionarily stable unless the clique is ‘attached’ to another clique of the same or larger size, when the population may be able to switch or expand to the neighbouring clique to increase or at least maintain its total amount of contacts. However, the distribution of the population on a non-clique subnetwork is always evolutionarily unstable or weakly evolutionarily stable at the very best, for the population can always move away from its current distribution without decreasing its total amount of contacts. We conclude that the strategies to spread on maximal network cliques are not only equilibrium strategies but also evolutionarily more stable than those on non-clique subnetworks, thus theoretically reaffirming the evolutionary advantages of joining social cliques in social networks for social species.

Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease analysis

Discovering disease-associated genes (DG) is strategic for understanding pathological mechanisms. DGs form modules in protein interaction networks and diseases with common phenotypes share more DGs or have more closely interacting DGs. This prompted the development of Specific Betweenness (S2B) to find genes associated with two related diseases. S2B prioritizes genes frequently and specifically present in shortest paths linking two disease modules. Top S2B scores identified genes in the overlap of artificial network modules more than 80% of the times, even with incomplete or noisy knowledge. Applied to Amyotrophic Lateral Sclerosis and Spinal Muscular Atrophy, S2B candidates were enriched in biological processes previously associated with motor neuron degeneration. Some S2B candidates closely interacted in network cliques, suggesting common molecular mechanisms for the two diseases. S2B is a valuable tool for DG prediction, bringing new insights into pathological mechanisms. More generally, S2B can be applied to infer the overlap between other types of network modules, such as functional modules or context-specific subnetworks. An R package implementing S2B is publicly available at https://github.com/frpinto/S2B.

Higher-order clustering in networks.

A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucial to understanding complex networks, and the clustering behavior with respect to such higher-order network structures is not well understood. Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network cliques and provide a more comprehensive view of how the edges of complex networks cluster. Our higher-order clustering coefficients are a natural generalization of the traditional clustering coefficient. We derive several properties about higher-order clustering coefficients and analyze them under common random graph models. Finally, we use higher-order clustering coefficients to gain new insights into the structure of real-world networks from several domains.

Amino-Acid Network Clique Analysis of Protein Mutation Non-Additive Effects: A Case Study of Lysozme.

Optimizing amino-acid mutations in enzyme design has been a very challenging task in modern bio-industrial applications. It is well known that many successful designs often hinge on extensive correlations among mutations at different sites within the enzyme, however, the underpinning mechanism for these correlations is far from clear. Here, we present a topology-based model to quantitively characterize non-additive effects between mutations. The method is based on the molecular dynamic simulations and the amino-acid network clique analysis. It examines if the two mutation sites of a double-site mutation fall into to a 3-clique structure, and associates such topological property of mutational site spatial distribution with mutation additivity features. We analyzed 13 dual mutations of T4 phage lysozyme and found that the clique-based model successfully distinguishes highly correlated or non-additive double-site mutations from those additive ones whose component mutations have less correlation. We also applied the model to protein Eglin c whose structural topology is significantly different from that of T4 phage lysozyme, and found that the model can, to some extension, still identify non-additive mutations from additive ones. Our calculations showed that mutation non-additive effects may heavily depend on a structural topology relationship between mutation sites, which can be quantitatively determined using amino-acid network k-cliques. We also showed that double-site mutation correlations can be significantly altered by exerting a third mutation, indicating that more detailed physicochemical interactions should be considered along with the network clique-based model for better understanding of this elusive mutation-correlation principle.

Redes de parentesco en las familias de Santiago de Chile

El siguiente artículo busca caracterizar las familias chilenas a través del análisis de redes. Para ello, se aplican técnicas de análisis que combinan entrevistas cualitativas, análisis de redes ego-centradas y análisis narrativos de los individuos de tal forma de identificar las redes que se constituyen según el género y el parentesco que adoptan las familias, ampliando las concepciones tradicionales sobre la estructura familiar. A través de los datos cualitativos, las cartas de parentesco realizadas y la conformación de redes, se propone una estrategia de análisis a través de técnicas mixtas de investigación. Los resultados indican que las familias pueden ser categorizadas y comprendidas utilizando este tipo de acercamiento. Mientras que las familias de Santiago pueden ser descritas en cuatro tipologías: redes de camarilla, de familia cohesionada, de familias con redes extendidas y/o ramificadas, y red familiar de centro-periferia.

Clique Structure and Enterprise Innovation: an Empirical Research on China's High-end Equipment Manufacturing Industry

Clique problem has been fully researched in the social relations network of sociology，But the cliques in inter-enterprise networks have attracted people's attention in recent years. Based on the alliance data of the high-end equipment manufacturing industry in China in 2000-2013, we construct innovation network，and use negative binomial regression models to analyze the impact of the clique structure on enterprise innovation. The results show that the more the clique numbers in the alliance innovation network, the stronger the enterprise innovation capability. Whether the enterprises listed have a negative moderating effect on the impact of clique numbers on enterprise innovation performance. That is, for the listed enterprises, more clique numbers cannot significantly promote enterprise innovation, but for non-listed enterprises, more clique numbers are conducive to enterprise innovation. Innovation accumulation has no significant positive moderating effect on the impact of clique numbers on enterprise innovation performance. The impact of the coreness values on the enterprise innovative output in an inverted-U curvilinear way, and the coreness values also moderates the effect of innovation accumulation on enterprise innovation capability in an inverted-U curvilinear way. The conclusions of the research can provide the basis for the enterprise to embed the network cliques and for the relevant government departments to formulate the alliance policy.

Clique to Win? Impact of Simmelian Ties on Collaborative Project Performance

Large infrastructure and public works projects require resources not often found in individual firms. Competitive bidding motivates contractors to form partnerships with subcontractors to win and implement such projects. Projects with no prior interactions between partners tend to develop structural holes, providing the coordinating contractors with brokering privileges. But through repeated project interactions communication and trust develops giving rise to cohesive clique networks and Simmelian ties. Instead of asymmetric power structures, cliques develop group norms and constrain opportunistic behavior by members. How do these two different collaborative arrangements engaging distinct network microstructures impact project outcomes? This study examines the role of network structures on collaborative project outcomes in a novel setting where multiple firms collaborate and compete to win and execute projects. Multi-year longitudinal network analysis of public infrastructure projects implemented in the State of California, reveal that compared to dyadic ties, Simmelian ties and network closure between team members help thwart external competition and internal opportunistic behavior leading to favorable project outcomes. Our empirical results contribute to network theory and multipartner alliance formation by examining the dynamics of inter-firm collaborative network structures and subsequent effect on collaborative work.

Statistical properties of random clique networks

In this paper, a random clique network model to mimic the large clustering coefficient and the modular structure that exist in many real complex networks, such as social networks, artificial networks, and protein interaction networks, is introduced by combining the random selection rule of the Erd\"os and R\'enyi (ER) model and the concept of cliques. We find that random clique networks having a small average degree differ from the ER network in that they have a large clustering coefficient and a power law clustering spectrum, while networks having a high average degree have similar properties as the ER model. In addition, we find that the relation between the clustering coefficient and the average degree shows a non-monotonic behavior and that the degree distributions can be fit by multiple Poisson curves; we explain the origin of such novel behaviors and degree distributions.