Set Of Nodes Research Articles

Unmanned aerial vehicle path planning in a 3D environment using a hybrid algorithm

The optimal unmanned aerial vehicle (UAV) path planning using bio-inspired algorithms requires high computation and low convergence in a complex 3D environment. To solve this problem, a hybrid A*-FPA algorithm was proposed that combines the A* algorithm with a flower pollination algorithm (FPA). The main idea of this algorithm is to balance the high speed of the A* exploration ability with the FPA exploitation ability to find an optimal 3D UAV path. At first, the algorithm starts by finding the locally optimal path based on a grid map, and the result is a set of path nodes. The algorithm will select three discovered nodes and set the FPA's initial population. Finally, the FPA is applied to obtain the optimal path. The proposed algorithm's performance was compared with the A*, FPA, genetic algorithm (GA), and partical swarm optimization (PSO) algorithms, where the comparison is done based on four factors: the best path, mean path, standard deviation, and worst path length. The simulation results showed that the proposed algorithm outperformed all previously mentioned algorithms in finding the optimal path in all scenarios, significantly improving the best path length and mean path length of 79.3% and 147.8%, respectively.

Topic-Aware Information Coverage Maximization in Social Networks

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Influence maximization</i> (IM) aims to identify a set of nodes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$</tex-math> </inline-formula> to maximize the expected number of nodes influenced during the information propagation starting from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$</tex-math> </inline-formula> . Some works had extended this problem to be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">topic-aware</i> , where each node is associated with a topic distribution and tends to be activated with different probabilities by different topics. However, whether it is topic-aware or not, IM problem only focuses on the active nodes and overlooks all the inactive ones. Actually, an inactive node may receive the information from their active in-neighbors and become informed. Therefore, this type of nodes should also be considered when measuring the coverage of information propagation. Inspired by this, we formulate a new problem called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">topic-aware information coverage maximization</i> (TAICM), which aims to maximize the sum of the expected number of both active and informed nodes in topic-aware social networks. Then we devise a heuristic method to solve it. Experiments on three real-world datasets demonstrate that our method can achieve similar or higher information coverage in much less or at least acceptable time than some commonly used IM algorithms.

Frequency Propagation: Multimechanism Learning in Nonlinear Physical Networks.

We introduce frequency propagation, a learning algorithm for nonlinear physical networks. In a resistive electrical circuit with variable resistors, an activation current is applied at a set of input nodes at one frequency and an error current is applied at a set of output nodes at another frequency. The voltage response of the circuit to these boundary currents is the superposition of an activation signal and an error signal whose coefficients can be read in different frequencies of the frequency domain. Each conductance is updated proportionally to the product of the two coefficients. The learning rule is local and proved to perform gradient descent on a loss function. We argue that frequency propagation is an instance of a multimechanism learning strategy for physical networks, be it resistive, elastic, or flow networks. Multimechanism learning strategies incorporate at least two physical quantities, potentially governed by independent physical mechanisms, to act as activation and error signals in the training process. Locally available information about these two signals is then used to update the trainable parameters to perform gradient descent. We demonstrate how earlier work implementing learning via chemical signaling in flow networks (Anisetti, Scellier, etal., 2023) also falls under the rubric of multimechanism learning.

Completion Thyroidectomy May Not Always Be Necessary in the Setting of Incidental Metastatic Lymph Nodes

Completion Thyroidectomy May Not Always Be Necessary in the Setting of Incidental Metastatic Lymph Nodes

IDR: Intent‐driven routing strategy for reliable and cost‐effective multicasting in airborne tactical networks

AbstractTraditional multicast routing strategies can hardly meet the demanding requirements of multicasting mission assignment messages in airborne tactical networks. This letter proposes an intent‐driven routing strategy, termed as IDR, to realize reliable and efficient multicast. IDR exploits bit index explicit replication technique to encode not only the destination information but also the retransmission information into packet headers. If part of the multicast members failed to receive the packet, IDR can perceive the error and retransmit the packet to the precise set of nodes. Compared to the state‐of‐the‐art reliable multicast routing methods, IDR can enhance multicast retransmission success rate by about 10% or more and reduce retransmission overhead by around 50%.

Simulation of the cancer cell growth and their invasion into healthy tissues using local radial basis function method

Applying mathematical models to simulate dynamic biological processes has been a common practice for a long time. In recent decades, cancer research has also adopted this approach to understand how cancer cell populations grow and spread. This study focuses on a mathematical model that uses a system of PDEs to explain the time-dependent reaction–diffusion interaction among cancer cells, extracellular matrix, and matrix degradation enzymes. We use a computational method that involves the discrete Galerkin technique by employing local radial basis functions (LRBFs) as its basis to approximate the behavior of cancer cells as they grow and invade neighboring healthy tissues. This novel approach employs a smaller set of nodes to approximate the solution, instead of considering all data in the given domain of the cancer growth model. Utilizing locally supported radial basis functions, this method significantly reduces the computational volume required, in contrast to globally supported radial basis functions. Finally, we provide experimental examples to validate and illustrate the effectiveness of this new scheme in modeling the growth and behavior of cancer cells at different stages.

Discrete Artificial Fish Swarm Algorithm-Based One-Off Optimization Method for Multiple Co-Existing Application Layer Multicast Routing Trees

As an effective multicast application mechanism, the application layer multicast (ALM) determines the path of data transmission through a routing tree. In practical applications, multiple multicast sessions often occur simultaneously; however, few studies have considered this situation. A feasible solution is to sequentially optimize each co-existing ALM routing tree. However, this approach can lead to node congestion, and, even if the node out-degree reservation strategy is adopted, an optimal solution may not be obtained. In this study, to solve the problem of routing tree construction for multiple co-existing application layer multicast sessions, an optimization model that minimizes the overall delay and instability is constructed, and a one-off optimization method based on the discrete artificial fish swarm algorithm (DAFSA) is proposed. First, Steiner node sets corresponding to the multicast sessions are selected. Then, the routing trees for each multicast session are obtained through the improved spanning tree algorithm based on the complete graph composed of Steiner node sets. The experimental results show that the proposed method can simultaneously obtain multiple co-existing ALM routing trees with a low total delay and low instability. Even if the input is a single multicast session, it can lead to ALM routing trees with a lower delay and less instability than other algorithms, and the introduction of a penalty function can effectively avoid the problem of excessive replication and forwarding loads on some end-hosts. In addition, the proposed algorithm is insensitive to parameter changes and exhibits good stability and convergence properties for networks of different sizes.

Improving the efficiency of network controllability processes on temporal networks

The controllability of temporal networks has become an important issue for researchers in recent years. In temporal networks, the controllability is achieved through the control of dynamics. Existing studies take only some dynamics into account when controlling such networks, e.g., only adding links, and ignore the rest. Besides, conventional controllability methods on temporal networks suffer from time complexity and data overhead. In this paper, a dynamic controllability method is proposed which considers all dynamics of temporal networks to fully control the network, including adding and removing nodes and links. The proposed controllability method finds all the minimum driver nodes sets (MDSs) of a temporal network in a polynomial time. In this regard, to maintain and track the network changes, a new tree-based model, named Controllable Dynamics Temporal Network (CDTN), is proposed by which the control process is executed with high speed and low overhead. The experimental results show that the proposed controllability method outperforms state-of-the-art methods in terms of speed (47% improvement), overhead (33% improvement), and the size of MDS (38% improvement).

Bridging Software-Hardware for CXL Memory Disaggregation in Billion-Scale Nearest Neighbor Search

We propose CXL-ANNS , a software-hardware collaborative approach to enable scalable approximate nearest neighbor search (ANNS) services. To this end, we first disaggregate DRAM from the host via compute express link (CXL) and place all essential datasets into its memory pool. While this CXL memory pool allows ANNS to handle billion-point graphs without an accuracy loss, we observe that the search performance significantly degrades because of CXL’s far-memory-like characteristics. To address this, CXL-ANNS considers the node-level relationship and caches the neighbors in local memory, which are expected to visit most frequently. For the uncached nodes, CXL-ANNS prefetches a set of nodes most likely to visit soon by understanding the graph traversing behaviors of ANNS. CXL-ANNS is also aware of the architectural structures of the CXL interconnect network and lets different hardware components collaborate with each other for the search. Furthermore, it relaxes the execution dependency of neighbor search tasks and allows ANNS to utilize all hardware in the CXL network in parallel. Our evaluation shows that CXL-ANNS exhibits 93.3% lower query latency than state-of-the-art ANNS platforms that we tested. CXL-ANNS also outperforms an oracle ANNS system that has unlimited local DRAM capacity by 68.0%, in terms of latency.

On the Network Index of MAS with Layered Lattice-like Structures of Multiple Vertex-Related Parameters

In this article, a robust index named first-order network coherence (FONC) for the multi-agent systems (MASs) with layered lattice-like structure is studied via the angle of the graph spectra theory. The union operation of graphs is utilized to construct two pairs of non-isomorphic layered lattice-like structures, and the expression of the index is acquired by the approach of Laplacian spectra, then the corresponding asymptotic results are obtained. It is found that when the cardinality of the node sets of coronary substructures with better connectedness tends to infinity, the FONC of the whole network will have the same asymptotic behavior with the central lattice-like structure in the considered classic graph frameworks. The indices of the networks were simulated to illustrate the the asymptotic results, as described in the last section.

Selecting Optimal Path in Multiple-Path Routing for MANETs Using Fuzzy Cost

Background/ Objectives: Mobile Ad Hoc Networks (MANETs) consists of a set of nodes, where the nodes tend to change their positions time to time because of their infrastructure less characteristic. In MANETs, the protocol that is considered to be the appropriate for constructing an efficient route is AODV. Broadcast storm is another approach that can be used for establishing a path between the sender and receiver. Due to the concerns related to contention and collision, broadcast is not considered as an efficient mechanism for path selection. Though the aim of broadcasting is to minimize the flooding other issues like bandwidth consumption, collision and contention cannot be controlled. Since MANETs are associated with inadequate bandwidth, the approach of broadcasting is not efficient. Multipath routing approach permits in constructing multiple paths between the communicating nodes (sender and receiver). This approach is complicated due to the problem of node mobility in MANETs. The other paths available are not reliable because of the ad hoc nature of MANETs. The intention is to design a fuzzy controller that assists in reducing the reconstruction of paths using fuzzy cost assigned to each node in the network. Methods: The concept of this paper is to establish an optimal path from sender node to the receiver node from the available multiple paths in MANETs. The path established in this approach uses fuzzy cost. Fuzzy measure is the criteria used for establishing a path. The concept ImRMR used in this paper has been included with few improvements. The basic functionality of ImRMR is that paths are classified based on rank fitness. The proposal is to classify the paths based on its fuzzy cost. This fuzzy cost classifies the paths fitness. Findings: The proposed fuzzy cost approach discussed in this study is compared with On-Demand Multicast Routing Protocol (ODMRP). The result exhibits that the proposed method works better under differing conditions like traffic load, transmission range between nodes and also considers node mobility. The proposed mechanism with inclusion of fuzzy cost shows 5% energy efficiency, reduces the collisions by about 35% and throughput is enhanced by 30%. There has been a considerable decrease in the number of queries by 23%. The simulation results indicate that ODMRP with fuzzy cost is more useful and significant. Hence, consideration of fuzzy cost as a means for selecting an effective route is assumed to be the best. Novelty: This method contributes in reducing the drawbacks of routing approaches whether it is unipath and multi-path approach. The proposed approach changes the value of the attributes for a given route with fuzzy cost. A threshold value will be considered initially and the route having a cost more than the initialized threshold would be advised as the best route. This route would be considered as the efficient route for transmission between the sender and the receiver. Keywords: Uni­path, multiple paths, Fuzzy cost, Fuzzy fitness, Rank fitness

A New Algorithm Framework for the Influence Maximization Problem Using Graph Clustering

Given a social network modelled by a graph, the goal of the influence maximization problem is to find k vertices that maximize the number of active vertices through a process of diffusion. For this diffusion, the linear threshold model is considered. A new algorithm, called ClusterGreedy, is proposed to solve the influence maximization problem. The ClusterGreedy algorithm creates a partition of the original set of nodes into small subsets (the clusters), applies the SimpleGreedy algorithm to the subgraphs induced by each subset of nodes, and obtains the seed set from a combination of the seed set of each cluster by solving an integer linear program. This algorithm is further improved by exploring the submodularity property of the diffusion function. Experimental results show that the ClusterGreedy algorithm provides, on average, higher influence spread and lower running times than the SimpleGreedy algorithm on Watts–Strogatz random graphs.

Computational analysis of a class of singular nonlinear fractional multi-order heat conduction model of the human head

The subject of the article is devoted to the development of a matrix collocation technique based upon the combination of the fractional-order shifted Vieta–Lucas functions (FSVLFs) and the quasilinearization method (QLM) for the numerical evaluation of the fractional multi-order heat conduction model related to the human head with singularity and nonlinearity. The fractional operators are adopted in accordance with the Liouville–Caputo derivative. The quasilinearization method (QLM) is first utilized in order to defeat the inherent nonlinearity of the problem, which is converted to a family of linearized subequations. Afterward, we use the FSVLFs along with a set of collocation nodes as the zeros of these functions to reach a linear algebraic system of equations at each iteration. In the weighted L2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_2$$\\end{document} norm, the convergence analysis of the FSVLFs series solution is established. We especially assert that the expansion series form of FSVLFs is convergent in the infinity norm with order O(1K3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}(\\frac{1}{K^3})}$$\\end{document}, where K represents the number of FSVLFs used in approximating the unknown solution. Diverse computational experiments by running the presented combined QLM-FSVLFs are conducted using various fractional orders and nonlinearity parameters. The outcomes indicate that the QLM-FSVLFs produces efficient approximate solutions to the underlying model with high-order accuracy, especially near the singular point. Furthermore, the methodology of residual error functions is employed to measure the accuracy of the proposed hybrid algorithm. Comparisons with existing numerical models show the superiority of QLM-FSVLFs, which also is straightforward in implementation.

Finding Subgraphs with Maximum Total Density and Limited Overlap in Weighted Hypergraphs

Finding dense subgraphs in large (hyper)graphs is a key primitive in a variety of real-world application domains, encompassing social network analytics, event detection, biology, and finance. In most such applications, one typically aims at finding several (possibly overlapping) dense subgraphs, which might correspond to communities in social networks or interesting events. While a large amount of work is devoted to finding a single densest subgraph, perhaps surprisingly, the problem of finding several dense subgraphs in weighted hypergraphs with limited overlap has not been studied in a principled way, to the best of our knowledge. In this work, we define and study a natural generalization of the densest subgraph problem in weighted hypergraphs, where the main goal is to find at most k subgraphs with maximum total aggregate density, while satisfying an upper bound on the pairwise weighted Jaccard coefficient, i.e., the ratio of weights of intersection divided by weights of union on two nodes sets of the subgraphs. After showing that such a problem is NP-Hard, we devise an efficient algorithm that comes with provable guarantees in some cases of interest, as well as, an efficient practical heuristic. Our extensive evaluation on large real-world hypergraphs confirms the efficiency and effectiveness of our algorithms.

Anticipating regime shifts by mixing early warning signals from different nodes

Real systems showing regime shifts, such as ecosystems, are often composed of many dynamical elements interacting on a network. Various early warning signals have been proposed for anticipating regime shifts from observed data. However, it is unclear how one should combine early warning signals from different nodes for better performance. Based on theory of stochastic differential equations, we propose a method to optimize the node set from which to construct an early warning signal. The proposed method takes into account that uncertainty as well as the magnitude of the signal affects its predictive performance, that a large magnitude or small uncertainty of the signal in one situation does not imply the signal’s high performance, and that combining early warning signals from different nodes is often but not always beneficial. The method performs well particularly when different nodes are subjected to different amounts of dynamical noise and stress.

LUSTC: A Novel Approach for Predicting Link States on Dynamic Attributed Networks

Predicting link states on dynamic attributed networks is one of the most fundamental problems for network analysis. To accurately predict the formations and disappearances of links, we need to utilize three types of information, that is, structural information, node content information, and temporal information, simultaneously. To this end, we propose a novel approach called LUSTC for predicting links and unlinks with structural information, temporal information, and node content information on dynamic attributed networks. For each snapshot, LUSTC first randomly chooses some sets of active nodes whose degree changes between adjacent snapshots and collects higher-order structural information using a random walk method based on the active nodes. Then, LUSTC generates two global matrices containing the temporal structural information and temporal node content information, respectively, as well as a sequence of auxiliary matrices for reconstructing the structural information and node content information of snapshots. These generated matrices are optimized using a nonnegative matrix factorization (NMF) method based on the structural information and node content information of snapshots. Finally, LUSTC estimates the similarity matrix for future snapshots to predict the links that are more likely to be formed or broken. The experimental results on various dynamic attributed networks demonstrate the effectiveness of LUSTC on the link prediction and unlink prediction tasks.

Designing energy-aware collaborative intrusion detection in IoT networks

The Internet of Things (IoT) with its evolution brings many benefits to people’s routine life, while at the same time posing various security challenges, due to the lack of software updates and access control policies. For protection, collaborative/distributed detection networks are one essential security solution, which can enhance the detection capability of a separate detection node by governing a set of eligible detection nodes to share information. However, internal intruders/attacks are the main threat source to the distributed structure including both IoT and CIDNs. In the literature, the CIDNs with challenge-based trust mechanism is found to be secure under many common external threats, but would be ineffective to advanced insider threats. Motivated by this issue, this work considers the factor of energy consumption and develops an enhanced challenge-based CIDN, called EnergyCIDN. The main idea relies on the observation that advanced attacks should consume more energy than a benign node. Under the evaluation on our EnergyCIDN, we conduct a set of experiments and tests in many scenarios, including simulated network, practical IoT network, and medical network. In comparison with the existing similar approaches, our EnergyCIDN showcases better detection capability regarding both common and advanced insider attacks.

Potential Locations of Strong Earthquakes in Bulgaria and the Neighbouring Regions

Information about potential earthquake sources is a key issue for seismic hazard assessment. This study presents the application of a phenomenological approach based on pattern recognition to determine the possible locations of strong earthquakes in the Bulgarian region. The technique assumes the origin of strong earthquakes in morphostructural nodes formed around the intersections of morphostructural lineaments identified by morphostructural zoning. For the territory of the Bulgaria and neighbouring regions, 178 nodes were defined in this work. The CORA-3 pattern recognition algorithm identified 59 seismogenic nodes analysing a set of geophysical and geological node’s characteristics. The identified seismogenic nodes are capable to generate earthquakes with magnitude equal to or greater than 6 and are located at the boundaries between the largest tectonic domains: Rila, Pirin, and Rhodope orogens; the Serbian-Macedonian massif; and in the Stara Planina belt. The set of characteristic features of seismogenic nodes indicates that the vicinity of potential nodes is characterized by a high contrast of neotectonic movements of the Earth’s crust and the presence of deep heterogeneities in the Earth’s crust. About 40% of the recognized nodes are not associated with any earthquakes, while the rest of the recognized seismogenic nodes are characterized by an area with a radius of 25 km where earthquakes are known to occur. Part of these “non active” seismogenic nodes are close to the historical events with magnitudes higher than 5.5 since the magnitude and location of historical events have large uncertainties. Another part of the seismogenic nodes may slightly change the location due to the uncertainties in morphostructural zonation. Other nodes may indicate unknown historical seismicity or paleoearthquakes. Defined M6+ seismogenic nodes can fill the potential gaps in the recorded seismicity on the territory of Bulgaria, thus to improve the seismic hazard assessment of the studied region.

Numerical and Scientific Investigation of Some Molecular Structures Based on the Criterion of Super Classical Average Assignments

Numbering a graph is a very practical and effective technique in science and engineering. Numerous graph assignment techniques, including distance-based labeling, topological indices, and spectral graph theory, can be used to investigate molecule structures. Consider the graph G, with the injection Ω from the node set to 1,2,…,∆, where ∆ is the sum of the number of nodes and links. Assume that the induced link assignment Ω∗ is the ceiling function of the average of root square, harmonic, geometric, and arithmetic means of the vertex labels of the end vertices of each edge. If the union of range of Ω of the node set and the range of Ω of the link set is the set 1,2,…,∆, then Ω is called a super classical average assignment (SCAA). This is known as the SCAA criterion. In this study, the graphical structures corresponding to chemical structures based on the SCAA criterion are demonstrated. The graphical depiction of chemical substances was first defined and second, the union of any number of cycles Cn, the tadpole graph, the graph extracted by identifying a line of any two cycles Cm and Cn, and the graph extracted by joining any two cycles by a path are all examined in this work.

Enhancing anomaly detection with adaptive node inspection in large-scale networks with binary sensors

Maintaining the stability and reliability of large-scale networks and graph structures is a practical challenge, particularly in sensor-intensive systems. One critical task in such networks is to identify anomalies and the origin of disturbances in a timely manner. However, successful anomaly detection requires sufficient and accurate data from sensors across the network. This work aims to develop an innovative framework to improve the accuracy of anomaly detection tasks with binary sensor data through intelligent node selection and inspection. Instead of relying only on stochastic insights obtained from network sensors, we explore how a specific small set of nodes can be inspected using a Bayesian framework so that the anomaly detection performance is improved. We demonstrate the effectiveness of the proposed model with a set of numerical experiments.