Link Information Research Articles

Improving community detection in social networks using enhanced BSO by exploring network structure

Community detection in social networks plays a crucial role in understanding various social phenomena. However, accurately identifying communities is challenging due to the complex nature of social networks. To address this challenge, we propose CD-BSO, an innovative metaheuristic based on brainstorming. CD-BSO leverages network knowledge through specialized initialization and solution generation operators designed to capture social network characteristics. In the initialization phase, a technique combining Depth-First Search (DFS) is employed to consider both connectivity and information diffusion, ensuring accurate community representation. The generation step introduces search operators that consider link formation and node similarity, facilitating convergence towards a community structure that closely resembles the network's actual structure. The evaluation of CD-BSO outperforms recent and well-known community detection methods. The evaluation results were further analyzed using visual analysis, providing valuable insights into the network structure. CD-BSO exhibits significant potential for accurately identifying communities and extracting meaningful information from social networks.

The Dissolution Behavior of Pyrite and Chalcopyrite During Low-Temperature Pressure Oxidation: Chalcopyrite Influence on Pyrite Oxidation

The research of this paper was carried out on the low-temperature (100 ± 2 °C) pressure (0.2–0.8 MPa) leaching of pyrite, chalcopyrite and their mixture. According to experiments on chalcopyrite dissolution, increasing the oxygen pressure from 0.2 up to 0.8 MPa had a slight effect on chalcopyrite dissolution. Oxygen pressure and initial sulfuric acid concentration in the range of 10–50 g/L had the greatest positive effect on the pyrite oxidation. The SEM and EDX mappings indicate the chalcopyrite and pyrite surfaces to be passivated by elemental sulfur. The oxidation degree of pyrite in its mixture with chalcopyrite increased significantly from 54.5 up to 80.3% in 0–240 min. The reaction time is relative to the dissolution of the individual mineral, while the dissolution of chalcopyrite remained virtually unchanged. The addition of Cu (II) and Fe (III) ions does not influence pyrite dissolution when chalcopyrite is added in a leaching process, which can be explained by the formation of an electrochemical link between the minerals. The positive effect of chalcopyrite addition is associated with a decreased formation of elemental sulfur on the surface of pyrite. The described method can be used for the hydrometallurgical processing of copper raw materials with increased pyrite content, as well as for the pretreatment of copper concentrates with gold-rich pyrite concentrates to increase the recovery of gold and silver.

Empowering super-stars or rising-stars? Dynamics of the high-speed rail network planning in China

ABSTRACT The development of high-speed rail (HSR) systems as a critical component of the global 'infrastructure turn' is deeply embedded in regional socioeconomic contexts. Drawing on advances in actor-oriented network modelling and longitudinal inter-city HSR data in China during 2008-2022, this study explores how the evolution of the HSR network is dynamically and differentially underpinned by attribute-based effects of socioeconomic scale and growth rate to balance the short-term efficiency and long-term equity of nationwide development. After controlling for structure-based effects (transitive triads and balance) as well as attribute-based effects related to geographic, natural and cultural contexts, the results provide evidence that while the scale and the growth rate of economy and population both positively impact HSR link formation, the impacts of scale-related effects significantly diminish over time whereas the impacts of growth-rate-related effects exhibit a significant strengthening trend. The results further illustrate that while the scale-related and growth-rate-related effects of economy function as substitutes in driving HSR link formation, the two corresponding effects of population complement each other, providing evidence for the heterogeneous roles of economic and social contexts in underpinning HSR network planning. The findings provide implications for more effective and equitable regional socioeconomic development through strategic HSR network planning.

From expectation to action: an empirical study of the role of confidence

We explore formation of expectations and links between expectations and action among Croatian micro-producers of organic fruits and vegetables. Twenty-nine interviews with entrepreneurs from all Croatian counties and the City of Zagreb were conducted. Thematic analysis reveals four recurring themes linking participants’ expectations and action: positive feedback, coordination devices, proximal vs. distal bias and retrospecting prospects. We relate these themes to the valence of expectations (positive, negative or neutral) and situate our findings within broader theoretical frameworks of Barbalet’s theory of confidence and Emirbayer and Mische’s theory of agency. Enjoying acceptance and recognition as members of community-supported agriculture groups, and having access to specific resources as members of associations of organic producers are identified as key drivers of optimism and confidence among the participants.

Working Memory Workload When Making Complex Decisions: A Behavioral and EEG Study.

Working memory (WM) is crucial for adequate performance execution in effective decision-making, enabling individuals to identify patterns and link information by focusing on current and past situations. This work explored behavioral and electrophysiological (EEG) WM correlates through a novel decision-making task, based on real-life situations, assessing WM workload related to contextual variables. A total of 24 participants performed three task phases (encoding, retrieval, and metacognition) while their EEG activity (delta, theta, alpha, and beta frequency bands) was continuously recorded. From the three phases, three main behavioral indices were computed: Efficiency in complex Decision-making, Tolerance of Decisional Complexity, and Metacognition of Difficulties. Results showed the central role of alpha and beta bands during encoding and retrieval: decreased alpha/beta activity in temporoparietal areas during encoding might indicate activation of regions related to verbal WM performance and a load-related effect, while decreased alpha activity in the same areas and increased beta activity over posterior areas during retrieval might indicate, respectively, active information processing and focused attention. Evidence from correlational analysis between the three indices and EEG bands are also discussed. Integration of behavioral and metacognitive data gathered through this novel task and their interrelation with EEG correlates during task performance proves useful to assess WM workload during complex managerial decision-making.

A Transformative Topological Representation for Link Modeling, Prediction and Cross-Domain Network Analysis.

Many complex social, biological, or physical systems are characterized as networks, and recovering the missing links of a network could shed important lights on its structure and dynamics. A good topological representation is crucial to accurate link modeling and prediction, yet how to account for the kaleidoscopic changes in link formation patterns remains a challenge, especially for analysis in cross-domain studies. We propose a new link representation scheme by projecting the local environment of a link into a "dipole plane", where neighboring nodes of the link are positioned via their relative proximity to the two anchors of the link, like a dipole. By doing this, complex and discrete topology arising from link formation is turned to differentiable point-cloud distribution, opening up new possibilities for topological feature-engineering with desired expressiveness, interpretability and generalization. Our approach has comparable or even superior results against state-of-the-art GNNs, meanwhile with a model up to hundreds of times smaller and running much faster. Furthermore, it provides a universal platform to systematically profile, study, and compare link-patterns from miscellaneous real-world networks. This allows building a global link-pattern atlas, based on which we have uncovered interesting common patterns of link formation, i.e., the bridge-style, the radiation-style, and the community-style across a wide collection of networks with highly different nature.

Borromean hypergraph formation in dense random rectangles.

We develop a minimal model to study the stochastic formation of Borromean links within topologically entangled networks without requiring the use of knot invariants. Borromean linkages may form in entangled solutions of open polymer chains or in Olympic gel systems such as kinetoplast DNA, but it is challenging to investigate this due to the difficulty of computing three-body link invariants. Here, we investigate rectangles randomly oriented in three Cartesian planes and densely packed within a volume, and evaluate them for Hopf linking and Borromean link formation. We show that dense packings of rectangles can form Borromean triplets and larger clusters, and that in high enough density the combination of Hopf and Borromean linking can create a percolating hypergraph through the network. We present data for the percolation threshold of Borromean hypergraphs, and discuss implications for the existence of Borromean connectivity within kinetoplast DNA.

Optimal Resilience in Multitier Supply Chains

Abstract Forward-looking investments determine the resilience of firms’ supply chains. Such investments confer externalities on other firms in the production network. We compare the equilibrium and optimal allocations in a general equilibrium model with an arbitrary number of vertical production tiers. Our model features endogenous investments in protective capabilities, endogenous formation of supply links, and sequential bargaining over quantities and payments between firms in successive tiers. We derive policies that implement the first-best allocation, allowing for subsidies to input purchases, network formation, and investments in protective capabilities. The first-best policies depend only on production function parameters of the pertinent tier. When subsidies to transactions are infeasible, the second-best subsidies for resilience depend on production function parameters throughout the network, and subsidies are larger upstream than downstream whenever the bargaining weights of buyers are nonincreasing along the chain.

HTFSMMA: Higher-Order Topological Guided Small Molecule-MicroRNA Associations Prediction.

Small molecules (SMs) play a pivotal role in regulating microRNAs (miRNAs). Existing prediction methods for associations between SM-miRNA have overlooked crucial aspects: the incorporation of local topological features between nodes, which represent either SMs or miRNAs, and the effective fusion of node features with topological features. This study introduces a novel approach, termed high-order topological features for SM-miRNA association prediction (HTFSMMA), which specifically addresses these limitations. Initially, an association graph is formed by integrating SM-miRNA association data, SM similarity, and miRNA similarity. Subsequently, we focus on the local information of links and propose target neighborhood graph convolutional network for extracting local topological features. Then, HTFSMMA employs graph attention networks to amalgamate these local features, thereby establishing a platform for the acquisition of high-order features through random walks. Finally, the extracted features are integrated into the multilayer perceptron to derive the association prediction scores. To demonstrate the performance of HTFSMMA, we conducted comprehensive evaluations including five-fold cross-validation, leave-one-out cross-validation (LOOCV), SM-fixed local LOOCV, and miRNA-fixed local LOOCV. The area under receiver operating characteristic curve values were 0.9958 ± 0.0024 (0.8722 ± 0.0021), 0.9986 (0.9504), 0.9974 (0.9111), and 0.9977 (0.9074), respectively. Our findings demonstrate the superior performance of HTFSMMA over existing approaches. In addition, three case studies and the DeLong test have confirmed the effectiveness of the proposed method. These results collectively underscore the significance of HTFSMMA in facilitating the inference of associations between SMs and miRNAs.

Production factors, regional economic development and agriculture in brazil: A spatial econometrics approach

Agriculture has a great effect on the development process of a region and production land, labor, and technology are determinant factors in its dynamism. This work carries out a spatial analysis for each production factor of Brazilian municipalities in 2006 and 2017 and intends to verify if there are patterns of association and agglomerations. This work is applied research of quantitative and exploratory nature. The Exploratory Analysis of Spatial Data (ESDA) was used to perform spatial analysis for each production factor in Brazilian municipalities in 2006 and 2017 and to check the patterns of association and agglomerations. The main agricultural activities and practice are related to exports as proposed by North. The formation of clusters verified both in the use of the factors of land, labor and technology, and in the value of agricultural production has a polarized role in the Brazilian space, as described by Perroux. This polarization and the formation of clusters expanded at certain points over time, indicating the formation of new links with neighboring municipalities (a la Hirschman). The spatial association pattern was significant in all analyzed variables, with emphasis on the non-physical technology factor, presenting the highest spatial autocorrelation coefficient in both periods analyzed.

Structural Basis for Substrate Binding, Catalysis and Inhibition of Breast Cancer Target Mitochondrial Creatine Kinase by Covalent Inhibitor via Cryo-EM.

Mitochondrial creatine kinases are key players in maintaining energy homeostasis in cells by working in conjunction with cytosolic creatine kinases for energy transport from mitochondria to cytoplasm. High levels of MtCK observed in Her2+ breast cancer and inhibition of breast cancer cell growth by substrate analog, cyclocreatine, indicate dependence of cancer cells on the 'energy shuttle' for cell growth and survival. Hence, understanding the key mechanistic features of creatine kinases and their inhibition plays an important role in the development of cancer therapeutics. Herein, we present the mutational and structural investigation on understudied ubiquitous mitochondrial creatine kinase (uMtCK). Our cryo-EM structures and biochemical data on uMtCK showed closure of the loop comprising residue His61 is specific to and relies on creatine binding and the reaction mechanism of phosphoryl transfer depends on electrostatics in the active site. In addition, the previously identified covalent inhibitor CKi showed inhibition in breast cancer BT474 cells, however our biochemical and structural data indicated that CKi is not a potent inhibitor for breast cancer due to strong dependency on the covalent link formation and inability to induce conformational changes upon binding.

Hydrogen-bond linking is crucial for growing ice VII embryos.

We use molecular dynamics simulations to examine the homogeneous nucleation of ice VII from metastable liquid water. An unsupervised machine learning classification identifies two distinct local structures composing Ice VII nuclei. The seeding method, combined with the classical nucleation theory (CNT), predicts the solid-liquid interfacial free energy, consistent with the value from the mold integration method. Meanwhile, the nucleation rates estimated from the CNT framework and brute force spontaneous nucleations are inconsistent, and we discuss the reasons for this discrepancy. Structural and dynamical heterogeneities suggest that the potential birthplace for an ice VII embryo is relatively ordered, although not necessarily relatively immobile. Moreover, we demonstrate that without the formation of hydrogen-bond links, ice VII embryos do not grow.

Optical routing: link failure detection-aided optimal routing in elastic optical networks using hybrid deep learning with target-based feature pool formation

ABSTRACT The Elastic Optical Network (EON) is a kind of optical network where a large amount of data signals are given into the channel of optical fibre by modulating with the optical signal. One of the most crucial tasks for optical networks is resource allocation and routing, which has a significant impact on resource effectiveness and network capacity. Deep learning-based routing and resource allocation have piqued the interest of researchers to a considerable extent. Thus, this paper aims to develop efficient link failure detection in EONs with hybrid deep learning strategies. Initially, the data aggregation is performed to combine the multiple link information from EONs. The aggregated link data is given into the hybrid detection model named as Enhanced Deep Learning Network (EDLNet), where the One-Dimensional Convolutional Neural Network (1D-CNN) is used for extracting the features and used for generating the target-based feature pool. This feature pool is utilised in the Long Short-Term Memory (LSTM) network for detecting link failures. The parameter optimization is done by the hybrid 1DCNN-LSTM network with the support of the Hybrid Red Deer with Sewing Training-based Optimisation (HRD-STO) to achieve accurate detection results. The detected failed link is mitigated at the time of routing, and only reliable links are used to attain certain objective constraints. These objective constraints include shortest path, link reliability and stability, throughput and delay for enhancing the efficiency of the EONs. The replication analysis reveals the effectiveness of the proposed link failure detection model by comparing it with the conventional models.

Robust feature selection via central point link information and sparse latent representation

Before conducting unsupervised feature selection, it is usually assumed that these data are independent of each other. On the contrary, real data will influence each other. Therefore, traditional feature selection methods may lose information related to each other between data. This can lead to inaccurately generated pseudo-label information and may result in poor feature selection results. To find solutions to this issue, this paper proposes robust feature selection via central point link information and sparse latent representation (CPSLR). Firstly, structure a link graph by calculating the center matrix to store the distance information from the sample to the center point. If two samples have similar distances to the center point, it can be determined that they belong to the same class. Therefore, the similarity between samples is preserved, and more accurate pseudo-label information is obtained. Secondly, CPSLR uses data graph and link graph to form a dual graph structure. It can not only retain the link information between samples but also retain the manifold structures of the samples. Then, CPSLR saves the interconnection contents between samples by sparse latent representation. That is, the constraint l2,1-norm is exerted on the expression of latent representation, and sparse non-redundant interconnection information is preserved. And by combining central point link information with sparse latent representation makes the interconnections between data reserved more comprehensive. That is to say, the pseudo-labels obtained are more like the real labels of the classes. Finally, CPSLR constrains the feature transformation matrix by l2,1/2-norm constraint so as to select robust and sparse features. CPSLR uses l2,1/2-norm constraint to assure that the feature transformation matrix is sparse, selecting more discriminative features, thereby obtaining the feature selection that can improve its efficiency. The experiments demonstrate that the clustering result of CPSLR outperform six classical or latest compared algorithms on eight datasets.

Data-driven traffic sensor location and path flow estimation using Wasserstein metric

This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality.

Machine Learning based Data Sensing Device Network with Optimized Group Formation and Group Head Selection

Sensor devices for data sensing (referred to as DSDs) are used in use cases such as border control and vehicle tracking. The architecture of the Data Sensing Device Network (DSDN) is established by integrating numerous DSDs across a given region, forming multiple groups. Within each group, a specific DSD is designated to facilitate communication between independent groups. The multi attribute values are captured and these attribute values effect the selection of head DSDs. For each of DSD this value ranges between 0.1 to 1. The DSD which has the highest value of range will be treated as Group Head in LEACH. The attributes are namely distance, battery level for each DSD. From the source DSD to destination DSD the link formation will happen end to end by making use of DSDs and base station, generally the end-to-end link communication has larger hops. This will have a ripple effect on battery level for DSDs and can cause reduced lifetime. The Energy based LEACH is modified on top of LEACH by computing the battery level for DSDs and picking DSD with highest battery level. The Energy based LEACH will have two DSDs in each group acting like head DSDs. Machine Learning Data Sensing Device Network (ML-DSDN) is proposed which will first create group DSDNs based on k means machine learning algorithm. ML-DSDN will find the head group DSD based on combination of random forest and SVM algorithm with set theory. The comparison is done of ML-DSDN with respect to ELEACH and LEACH method and it is proved that ML-DSDN performs better with respect to delay, link count, energy consumption, alive DSD count, dead DSD count, lifetime ratio, routing overhead.

Intra-community link formation and modularity in ultracold growing hyperbolic networks

Hyperbolic network models offer a straightforward yet powerful method for understanding the small-world, scale-free, highly clustered, and modular characteristics typical of complex systems that are often called as real-world networks. These models involve randomly positioning nodes in a hyperbolic space and connecting them based on a probability that diminishes with distance. In this study, we examine the community structure within networks created by the Popularity Similarity Optimization model, a fundamental hyperbolic model, when the temperature parameter (responsible for tuning the clustering coefficient) is set to the limiting value of zero. We focus on link formation within communities and show their close relation with non-linear preferential attachment processes. Based on this, we derive analytical expressions that significantly improve previous estimates of the expected modularity for partitions formed by equally sized angular sectors in the 2d hyperbolic space. Our formulas can now predict average modularity, confirmed by numerical simulations, with high accuracy over a broader range of model parameters and community sizes relative to the whole network. These results advance our understanding of module formation in hyperbolic networks, highlighting the surprising emergence of communities despite the lack of explicit community formation steps in the model definition.

In Situ TEM Tracking of Disconnection Motion and Atomic Cooperative Reorientation in the Oriented Attachment of Pt Nanoparticles.

The oriented attachment (OA) of nanoparticles (NPs) is an important crystal growth mechanism in many materials. However, a comprehensive understanding of the atomic-scale alignment and attachment processes is still lacking. We conducted in situ atomic resolution studies using high-resolution transmission electron microscopy to reveal how two Pt NPs coalesce into a single particle via OA, which involves the formation of atomic-scale links and a grain boundary (GB) between the NPs, as well as GB migration. Density functional theory calculations showed that the system energy changes as a function of the number of disconnections during the coalescence process. Additionally, the formation and annihilation processes of disconnection are always accompanied by the cooperative reorientation motion of atoms. These results further elucidate the growth mechanism of OA at the atomic scale, providing microscopic insights into OA dynamics and a framework for the development of processing strategies for nanocrystalline materials.

Unpublished Sources in the Archive of the Samizdat and Non-traditional Printing Sector of the State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences

The aim of the article is to present the structure of the common archive of the Samizdat and Non-traditional Printing Sector of the Laboratory of Book Studies of SPSTL SB RAS formation on the ­example of the main storage units – personal collections of writers, artists, cultural figures and musicians. It provides classification of the existing unpublished sources in the sector’s collections, analyzes authorial materials, dedications, autographs, drawings and markings, mentions of the alternative printing process authors and participants in periodicals and correspondence. The article describes the sources of samizdat and non-traditional printing, existing in the sector, reveals the content of the main thematic directions. It characterizes archival units on the examples of alternative book publishing, various forms of samizdat, ephemera, ego-documents, fragments of personal libraries, examples of periodical printing. It also offers the classification of personal collections based on temporal periodization, thematic and territorial characteristics. The analysis of the content of the presented sources bases on the personal collections of M. Baturin, I. Akhmetiev, V. Ivaniv, O. Volov, E. Shurygin, E. Iordansky, A. Chekanov, as well as various unofficial creative communities and groups. Special attention is paid to the formation of communication chains and links between authors in the informal cultural environment, in the field of samizdat and alternative book publishing from the 1960s to the 2010s that clarifies the criteria of such ties.

Development and experimental characterization study of cesium doped zinc oxide polymer conductive films for sensing applications

Development and experimental characterization study of cesium doped zinc oxide polymer conductive films for sensing applications