Group Of Nodes Research Articles

Effects of simplicial centrality on robustness of higher-order multiplex world trade networks

Abstract World trade networks are exhaustively described by pairwise interactions, and overlook higher-order structure from the outcome of collective interactions at the level of groups of nodes like multilateral trade agreements. To address this limitation, we collect multiplex world trade networks, including the bilateral regional trade agreement network, which represents pairwise interactions; the multilateral regional trade agreement network, which naturally represents a higher-order network structure; and the import and export trade network, which represents pairwise interactions and additional complexities. The analysis of simplicial centrality, including degree, closeness, and subgraph at 0, 1, and 2-simplex levels, reveals that intra-level correlations are high, while inter-levels may exhibit significant disparities. Nodes with low centrality at higher-order levels could influence network robustness due to the diversity of interactions and higher-order dependencies. Simplicial centrality on robustness of multiplex world trade networks under random and targeted attacks reveals that the complex connectivity of higher-order levels renders them more vulnerable post-attack. An optimization strategy of the rebalancing of network centrality is proposed to enhance the robustness, and the simulation shows risks posed to central nodes are minimized and opportunities for peripheral nodes to partake in global trade are broadened.

A General Concave Fairness Framework for Influence Maximization Based on Poverty Reward

Influence maximization (IM) aims to find a group of influential nodes as initial spreaders to maximize the influence spread over a network. Yet, traditional IM algorithms have not been designed with fairness in mind, resulting in discrimination against some groups, like LGBTQ communities and racial minorities, etc. This issue has spurred research on Fair Influence Maximization (FIM). However, existing FIM studies come with some drawbacks. Firstly, most proposed notions of fairness for FIM cannot adjust the trade-off between fairness level and influence spread. Secondly, though a few specific notions of fairness allow such balancing, they are limited to a few specific concave functions, which may not be suitable for various real-world scenarios. Furthermore, none of them have studied the deep relations between the features of concave functions and the level of fairness. Thirdly, existing fairness metrics are limited to their corresponding concepts of fairness. Comparing the level of fairness across different algorithms using existing metrics can be challenging. To tackle the above problems, this paper first proposes a novel fairness notion named Poverty Reward (PR), which achieves fairness by rewarding the enrichment of groups with low utility. Based on PR, we further propose an algorithmic framework called Concave Fairness Framework (CFF) that allows any concave function that satisfies specific requirements. We also systematically clarify how fairness is improved by applying concave functions and provide an in-depth quantitative analysis of how to select appropriate concave functions for different utility distributions. Moreover, we propose the Reward of Fairness (RoF) metric that evaluates the disparity between groups. Based on RoF, an evaluation system is built to uniformly compare FIM algorithms from different fairness notions. Experiments in real-world datasets have demonstrated the validity of the CFF, as well as the proposed fairness notion.

Antifragility of stochastic transport on networks with damage.

A system is called antifragile when damage acts as a constructive element improving the performance of a global function. In this paper, we analyze the emergence of antifragility in the movement of random walkers on networks with modular structures or communities. The random walker hops considering the capacity of transport of each link, whereas the links are susceptible to random damage that accumulates over time. We show that in networks with communities and high modularity, the localization of damage in specific groups of nodes leads to a global antifragile response of the system improving the capacity of stochastic transport to more easily reach the nodes of a network. Our findings give evidence of the mechanisms behind antifragile response in complex systems and pave the way for their quantitative exploration in different fields.

Learning accurate neighborhood- and self-information for higher-order relation prediction in Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) are commonly employed to model complex real-world scenarios with diverse node and edge types. However, due to constraints in data collection and processing, constructed networks often lack certain relations. Consequently, various methods have emerged, particularly recently, leveraging heterogeneous graph neural networks (HGNNs) to predict missing relations. Nevertheless, these methods primarily focus on pairwise relations between two nodes. Real-world interactions, however, often involve multiple nodes and diverse types, extending beyond simple pairwise relations. For instance, academic collaboration networks may entail interactions among authors, papers, and conferences simultaneously. Despite their prevalence, higher-order relations are often overlooked. While HGNNs are effective at learning network structures, they may suffer from over-smoothing, resulting in similar representations for nodes and their neighbors. The learned inaccurate proximity among nodes impedes the discernment of higher-order relations. Furthermore, observed edges among a target group of nodes can provide valuable evidence for predicting higher-order relations. To address these challenges, we propose a novel model called Accurate Neighborhood- and Self-information Enhanced Heterogeneous Graph Neural Network (ANSE-HGN). Building upon HGNNs to encode network structure and attributes, we introduce a relation-based neighborhood encoder to capture information within multi-hop neighborhoods in heterogeneous higher-order relations. This enables the calculation of accurate proximity among target groups of nodes, thereby enhancing prediction accuracy. Additionally, we leverage self-information from observed higher-order relations as an auxiliary loss to reinforce the learning process. Extensive experiments on four real-world datasets demonstrate the superiority of our proposed method in higher-order relation prediction tasks.

RGPBFT: A Reputation-Based PBFT Algorithm with Node Grouping Strategy

RGPBFT: A Reputation-Based PBFT Algorithm with Node Grouping Strategy

Cuckoo Search Optimization-Based Influence Maximization in Dynamic Social Networks

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence in the social network. IM has different applications like viral marketing, campaigning, advertising, and so on. Literature has presented various algorithms based on different approaches to address the IM problem, including nature-inspired algorithms. Most of the work focuses on the static social network. The proposed work first employs nature-inspired Cuckoo Search Optimization to solve the IM problem in dynamic networks. The proposed algorithm applies the fuzzy-logic-based technique to optimize the nests. We also perform statistical tests to show the effectiveness of the proposed algorithm with the benchmark algorithms. The experimental results are performed on five datasets and compare the results with the state-of-the-art algorithms. The results show that the proposed algorithm gives better results than the nature-inspired state-of-the-art algorithms.

Lightning Network Economics: Topology

By design, the Bitcoin protocol has a low throughput. The Lightning Network (LN) is a layer-two solution built to increase throughput by cryptographically securing commitments to transactions and only occasionally converting cumulative balances into on-chain transactions. LN channels enable payments between nodes connected by a path of channels. The payment flow through a channel determines its cost. Different channel topologies can support the same underlying flows but impose different costs. This paper obtains necessary conditions for cost-minimizing topologies by identifying local cost-reducing strategies. The first local strategy entails repositioning of channels. The second entails adding hubs to handle the flows of groups of nodes. The paper also evaluates the efficiency of a global configuration, obtaining bounds on the minimum cost topology and showing the unusual circumstances in which the cost minimal structure is a hub that connects to all other nodes. This paper was accepted by Joshua Gans, business strategy. Funding: This work was partially supported by Science Foundation Ireland [Grants 16/IA/4443, 16/SPP/3347], Columbia-International Business Machines Center for Blockchain and Data Transparency, Chaire Fintech at University Paris Dauphine-Paris Sciences et Lettres, Algorand Foundation, and Simons Institute.

Cartridge-Based Nucleic Acid Amplification Test Compared to Fine Needle Aspiration Cytology in Suspected Cases of Tubercular Lymphadenitis (the Indian Experience)

INTRODUCTION: Diagnosis of tubercular lymphadenitis is daunting as there are varied clinical presentations and no single confirmatory gold standard test. Cartridge-based nucleic acid amplification test (CBNAAT) of the lymph node is a rapid molecular diagnostic test for simultaneously detecting tuberculosis (TB) and rifampicin resistance. AIM: To evaluate the performance of the CBNAAT test for detecting M. tuberculosis in lymph node specimens compared to fine needle aspiration cytology (FNAC). MATERIALS AND METHODS: The study was conducted in a rural tertiary care hospital in central India. A total of 180 patients clinically suspected of tubercular lymphadenitis were included. The male-to-female ratio was 1:1.3. The average age was 33.3 years. The age group 21–40 years had the highest number of cases. The most common complaints among the patients were fever (29.4%), followed by loss of appetite (9.5%), weight loss (9.5%), and cough (6.6%). However, most patients presented to the hospital with only lymphadenopathy (44.4%). The most common site involved was the anterior cervical lymph node (78.8%), followed by the axillary group (10.5%), submandibular (2.8%), inguinal (2.8%), supraclavicular (2.2%), submental (1.7%) and infraclavicular (1.1%) group of lymph nodes. The patients were subjected to both FNAC and CBNAAT testing. Results were reported as positive or negative for M. tuberculosis as CBNAAT gives a semiquantitative estimate of the concentration of bacilli. Rifampicin resistance results were reported as detected or not detected. RESULTS: Cytological examination of the lymph node aspirates revealed that most were tubercular lymphadenitis cases. Cytomorphological analysis of the cases of tubercular lymphadenitis revealed Type 6 (tubercular abscess) as the predominant pattern. CBNAAT testing detected 26 cases of M. tuberculosis and three cases of rifampicin resistance. The study reported a specificity of 92.92% and low sensitivity of 26.86% of combined FNAC and CBNAAT is much higher compared to only CBNAAT. CONCLUSION: CBNAAT, along with FNAC, is a valuable addition in first-line investigations of tubercular lymphadenitis to make a timely diagnosis.

Research progress on the distribution patterns and surgical dissection of central lymph nodes in left-sided colon cancer

Lymphatic metastasis is one of the main pathways of colorectal cancer spread and also a crucial factor in patient long-term prognosis. Lymph node dissection in the possible tumor drainage area, particularly the central group of lymph nodes at the root of the tumor-associated supplying artery, is a key and challenging aspect of surgical techniques. Currently, the patterns of lymphatic drainage and the distribution of central lymph nodes in left-sided colon cancer are not well illustrated, and there is no consensus on the necessity and extent of central lymph node dissection. This has led to significant variability in the extent of lymph node dissection among different surgeons in clinical practice, a lack of quality control standards for surgical procedures, and impacts on postoperative treatment strategy and long-term outcomes. Moreover, current research on lymphatic drainage and metastasis is primarily based on traditional anatomy, whereas individualized, precise approaches to lymph node dissection have not been realized. The application of preoperative and intraoperative lymph node imaging techniques based on functional anatomy in colorectal cancer patients is still under exploration.

Spectrum of Kikuchi-Fujimoto Disease: A 10-Year Study in a Tertiary Care Centre in South India.

Background Kikuchi-Fujimoto disease (KFD) is a benign lesion of the lymph nodes predominantly seen in younger women and is a condition associated with a good prognosis due to its indolent nature. Since KFD is chiefly a diagnosis of exclusion, it is liable to a higher degree of misdiagnosis. Aim In our study, we attempt to document and analyse the demographic, clinical, and pathological spectrum of cases diagnosed as KFD in a South Indian tertiary care centre over a period of 10 years. Methodology This study was conducted in a retrospective observational manner. Descriptive statistics, including simple frequency and percentage, were used to analyse and summarise the collected data. Results Among the 44 cases obtained, 36 patients were female and eight were males, with an age range of eight to 70 years. The mean age of the affected population was 26 years. Fever was a presenting complaint in 63.6% of cases, and 100% of the cases had lymphadenopathy, chiefly affecting the posterior cervical group of nodes (32/44 cases). Multiple lymph node groups were involved in 22 cases. Most of the nodes were larger than 1 cm in their greatest dimension. On analysis of peripheral smears, 43.2% of patients showed leukopenia. Anti-nuclear antibody (ANA) analysis showed a positive status among two patients, simultaneously establishing the diagnosis of systemic lupus erythematosus (SLE). A history of treated tuberculosis was present in four patients, and one patient had a history of non-compliance with TB treatment. Two patients had recently been diagnosed with TB and were on treatment. The involved nodes were subjected to fine-needle aspiration studies in 13 patients; 12/13 cases showed reactive lymphadenitis, while one case had atypical lymphoid cells. The involved nodes of all the patients were excised and sent for histopathological analysis. The diagnosis of KFD was confirmed on the biopsy. Conclusion In this study, we note the clinical presentations of all the patients diagnosed with KFD and elaborate on the investigations that were employed to verify the diagnosis via exclusion that enabled differentiation of KFD from its close mimickers, which have specific treatment. Keeping this entity in mind is essential in order to prevent overdiagnosis, as most often this condition resolves spontaneously, and in occasional cases, symptomatic treatment is sufficient for its management.

Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling.

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., EGA.analytical) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (EGA.sampling) yielded higher accuracy and smaller errors than PA; lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then EGA.analytical and EGA.sampling. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the EGA.analytical technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the EGA.sampling method as a valuable alternate technique. The findings also indicated encouraging results for extending the regularization-based network modeling EGA method to the Bayesian framework and discussed future directions in this line of work. The study illustrated the practical application of the techniques to two empirical examples in R.

An energy‐efficient Chebyshev fire hawks optimization algorithm for energy balancing in sensor‐enabled Internet of Things

SummarySensor‐enabled systems have been used successfully in agricultural, healthcare, commercial, and military application domains. Recently, there has been significant interest in the intelligent applications of sensor‐enabled technologies, particularly in the domains of smart grid, Internet of Vehicles (IoV), body area networks, and the Internet of Things (IoT). In recent research, various protocols and algorithm are developed for effective energy‐efficient routing and energy balancing. These existing models have some issues like high energy consumption and minimum network life time. In order to overcome these existing issues, a novel cluster head selection and routing mechanism in a wireless sensor network (WSN) environment is proposed. The clustering process has been formed by an enhanced Taylor kernel fuzzy C‐means algorithm (TKFC‐means). The cluster head in the group of sensor nodes has been identified based on energy and distance calculation. Finally, the routing has been performed by a novel energy‐efficient Chebyshev fire hawks optimization‐based routing protocol to route data to the edge server, which helps to balance the energy effectively. This protocol takes into account various factors, including distance, cost, residual energy, load, temperature, latency, and overall energy. The proposed model can obtain a throughput value of 82 Mbps for the sensor nodes at 500 and an end‐to‐end delay of 3.6 at 500 sensor nodes. The packet delivery ratio and loss ratio attain 96.4% and 2.7%, respectively, with 500 sensor nodes in the proposed approach. The proposed method consumes 0.45 mJ of energy with 500 nodes. From this analysis, the proposed model can obtain better results than the existing compared models.

BRMECQN: Design of an efficient integration of Bioinspired Routing Model with Energy efficient Clustering for high QoS Wireless Networks

In this dynamic domain of communication in wireless network technology, the need for advanced models that boost the Quality of Service (QoS) parameters concerning energy efficiency, speed, throughput, packet delivery ratio, and minimization of jitter is of prime concern. Traditional routing and clustering methods in wireless networks are often confronted with limitations such as suboptimal resource utilization, reduced energy efficiency, and lower QoS metrics. In this regard, the present work introduces an innovative integration of bioinspired routing models with energy-efficient clustering to enhance the performance of wireless networks. A novel Fan-Shaped Clustering approach is employed in the proposed model for a proper grouping of the network nodes, which can reduce the energy consumption and enhance the load balancing across the network. The model performs a further integration of Elephant Herding Optimization and Bat Optimization algorithms following the clustering. This integration has been done to develop an optimal route finding mechanism between the clustered nodes through its bioinspired approach, to utilize the global search capability of Elephant Herding Optimization, and the proficiency of Bat Optimizations in local searches. The proposed model, when tested empirically on different configurations of networks, presents significant improvements over methods in practice. The results suggest a 10.5% improvement in energy efficiency, an improvement in speed of 8.3%, a rise in throughput of 3.5%, an improvement in Packet Delivery Ratio of 4.5%, and a reduction in jitter of 1.9%. These have been substantial improvements indicating that the proposed model overcomes the limitations of the existing ones and at the same time places a new benchmark in the performance of wireless networks. The impact of this work is immense, providing a scalable and efficient solution for next-generation wireless networks. This integration of bioinspired algorithms in the routing and clustering of the network shall open new avenues for research and development into the field with the potential of more resilient, efficient, and high-performing wireless communication systems. This work, therefore, adds immense impetus to the development of wireless network technologies to uphold higher QoS and better resource management in an era of ever-growing digital communication demands.

Simple incision and suture modeling on fixed structured grids

This paper presents a simple yet versatile approach to simulate skin incisions and suturing using nonlinear finite element analysis on a fixed structured grid with a spring-based suture model. Incisions and wounds are introduced by element removal and the sutures are discretely modeled by applying linear constraint relations pairwise to groups of nodes. Two numerical examples utilize the closing of an elliptical wound to study the influence of the number of sutures and their location, and two test cases use the Z-plasty transposition flap technique to investigate the versatility of the proposed method and for comparison with state-of-the-art results. The results indicate that the presented method is able to recreate literature results to an acceptable level of accuracy. Further, the method proves versatile due to the ability to freely introduce incisions and wounds onto the fixed grid modeling domain without remeshing. Lastly, the two main advantages of the discrete suture representation are the ability to capture local stress concentrations near the sutures and the full freedom in placing the sutures.

LeaDCD: Leadership concept-based method for community detection in social networks

Community discovery plays an essential role in analyzing and understanding the behavior and relationships of users in social networks. For this reason, various algorithms have been developed in the last decade for discovering the optimal community structure. In social networks, some individuals have special characteristics that make them well-known by others. These groups of users are called leaders and often have a significant impact on others, with an exceptional ability to build communities. In this paper, we propose an efficient method to detect communities in social networks using the concept of leadership (LeaDCD). The proposed algorithm mainly involves three phases. First, based on nodes' degree centrality and maximal cliques, some small groups of nodes (leaders) considered as seeds for communities are discovered. Next, unassigned nodes are added to the seeds through an expansion process to generate the initial community structure. Finally, small communities are merged to form the final community structure. To demonstrate the effectiveness of our proposal, we carried out comprehensive experiments on real-world and artificial graphs. The findings indicate that our algorithm outperforms other commonly used methods, demonstrating its high efficiency and reliability in discovering communities within social graphs.

Network Topology Evaluation and Transitive Alignments for Molecular Networking.

Untargeted tandem mass spectrometry (MS/MS) is an essential technique in modern analytical chemistry, providing a comprehensive snapshot of chemical entities in complex samples and identifying unknowns through their fragmentation patterns. This high-throughput approach generates large data sets that can be challenging to interpret. Molecular Networks (MNs) have been developed as a computational tool to aid in the organization and visualization of complex chemical space in untargeted mass spectrometry data, thereby supporting comprehensive data analysis and interpretation. MNs group related compounds with potentially similar structures from MS/MS data by calculating all pairwise MS/MS similarities and filtering these connections to produce a MN. Such networks are instrumental in metabolomics for identifying novel metabolites, elucidating metabolic pathways, and even discovering biomarkers for disease. While MS/MS similarity metrics have been explored in the literature, the influence of network topology approaches on MN construction remains unexplored. This manuscript introduces metrics for evaluating MN construction, benchmarks state-of-the-art approaches, and proposes the Transitive Alignments approach to improve MN construction. The Transitive Alignment technique leverages the MN topology to realign MS/MS spectra of related compounds that differ by multiple structural modifications. Combining this Transitive Alignments approach with pseudoclique finding, a method for identifying highly connected groups of nodes in a network, resulted in more complete and higher-quality molecular families. Finally, we also introduce a targeted network construction technique called induced transitive alignments where we demonstrate effectiveness on a real world natural product discovery application. We release this transitive alignment technique as a high-throughput workflow that can be used by the wider research community.

Blockchain-Based Cold Chain Traceability with NR-PBFT and IoV-IMS for Marine Fishery Vessels

Due to limited communication, computing resources, and unstable environments, traditional cold chain traceability systems are difficult to apply directly to marine cold chain traceability scenarios. Motivated by these challenges, we construct an improved blockchain-based cold chain traceability system for marine fishery vessels. Firstly, an Internet of Vessels system based on the Iridium Satellites (IoV-IMS) is proposed for marine cold chain monitoring. Aiming at the problems of low throughput, long transaction latency, and high communication overhead in traditional cold chain traceability systems, based on the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, a Node-grouped and Reputation-evaluated PBFT (NR-PBFT) is proposed to improve the reliability and robustness of blockchain system. In NR-PBFT, an improved node grouping scheme is designed, which introduces a consistent hashing algorithm to divide nodes into consensus and candidate sets, reducing the number of nodes participating in the consensus process, to lower communication overhead and transaction latency. Then, a reputation evaluation model is proposed to improve the node selection mechanism of NR-PBFT. It enhances the enthusiasm of nodes to participate in consensus, which considers the distance between fishery vessels, data size, and refrigeration temperature factors of nodes to increase throughput. Finally, we carried out experiments on marine fishery vessels, and the effectiveness of the cold chain traceability system and NR-PBFT were verified. Compared with PBFT, the transaction latency of NR-PBFT shortened by 81.92%, the throughput increased by 84.21%, and the communication overhead decreased by 89.4%.

Diagnostic efficiency and safety evaluation of different groups of lymph nodes in 193 cases of conventional transbronchial needle aspiration (C-TBNA).

Diagnostic efficiency and safety evaluation of different groups of lymph nodes in 193 cases of conventional transbronchial needle aspiration (C-TBNA).

The relationship between clustering and networked Turing patterns.

Networked Turing patterns often manifest as groups of nodes distributed on either side of the homogeneous equilibrium, exhibiting high and low density. These pattern formations are significantly influenced by network topological characteristics, such as the average degree. However, the impact of clustering on them remains inadequately understood. Here, we investigate the relationship between clustering and networked Turing patterns using classical prey-predator models. Our findings reveal that when nodes of high and low density are completely distributed on both sides of the homogeneous equilibrium, there is a linear decay in Turing patterns as global clustering coefficients increase, given a fixed node size and average degree; otherwise, this linear decay may not always hold due to the presence of high-density nodes considered as low-density nodes. This discovery provides a qualitative assessment of how clustering coefficients impact the formation of Turing patterns and may contribute to understanding why using refuges in ecosystems could enhance the stability of prey-predator systems. The results link network topological structures with the stability of prey-predator systems, offering new insights into predicting and controlling pattern formations in real-world systems from a network perspective.

Performance of community detection algorithms supported by node embeddings

Abstract The grouping of nodes into subsets that are relatively densely interconnected and separable from the rest of the network is a property often displayed in many complex real-world networks; this feature is known as a community structure. There is a growing demand for algorithms that can find partitions that resemble the community structure of a given network as closely as possible. However, most popular algorithms for community detection in graphs have one serious drawback, namely, they are heuristic-based and in many cases are unable to find a near-optimal solution. Moreover, their results are volatile, impacting the replicability of their results. In this paper, we investigate if the performance of greedy algorithms might be improved by initialising such algorithms with some carefully chosen partition of nodes, namely a partition obtained by embedding the nodes into real numbers space and then running a clustering algorithm on this latent representation. We believe that embedding will filter unwanted noise while retaining the proximity of nodes belonging to the same community or will learn more complex and elusive relations between nodes. Then, clustering algorithms run on this embedding will create a stable partitioning that will reduce the uncertainty in the initial phases of the community detection algorithms. The experiments show that the proposed procedure significantly improves the results over baseline community detection algorithms, namely Louvain and Leiden. It also reduces the inherent volatility of such algorithms. The impact depends on the given graph’s properties, especially the strength of the community structure and degree distribution. The largest boost in performance is given in the cases when networks are ‘noisier’, that is, when the community structure is less pronounced and there are many connections between communities. Furthermore, the design and parametrization of the procedure depend on the network’s topology, not on the size of the network itself.