Pair Of Nodes Research Articles

Networks with correlated edge processes

Abstract This article proposes methods to model non-stationary temporal graph processes motivated by a hospital interaction data set. This corresponds to modelling the observation of edge variables indicating interactions between pairs of nodes exhibiting dependence and evolution in time over interactions. This article thus blends (integer) time series models with flexible static network models to produce models of temporal graph data, and statistical fitting procedures for time-varying interaction data. We illustrate the power of our proposed fitting method by analysing a hospital contact network, and this shows the challenge in modelling and inferring correlation between a large number of variables.

Umbrella Trees for Nesting Problem

The problem of irregular-shaped pieces representation in order to minimize the nesting time is considered. A representation in the form of umbrella tree with a square crown consisting of tricolor nodes and bicolor leaves equidistant from the root is proposed. The construction of an umbrella tree from a image based on the transformation of Cartesian pixel coordinates into reference coordinates of tree leaves is shown. The translation and rotation of pieces functions on umbrella trees with quadratic estimates of computational complexity are determined. Examples of rounding coordinates heuristics and the experimental values of the threshold recoloring leaves during rotating a piece are given. The quadratic problem of intersection localization of the slab contents and the piece is reduced to the evaluating of color mutual exclusivity predicate of the pairs of nodes of the slab tree and the piece tree with linearrhythmic estimation of time in the applied cases. A pair of nodes is mutually exclusive if at least one of the nodes is white, and it isn't mutually exclusive if both are black. In other cases a pair of nodes is considered mutually exclusive if all four pairs of child nodes are mutually exclusive. The price of reducing the computational complexity of intersection localization is an increase of the required memory size by 33 % compared to the raster representation. Umbrella trees make it possible to use photo and video frames of pieces and slabs without converting them into a vector representation.

Network inference from short, noisy, low time-resolution, partial measurements: Application to C. elegans neuronal calcium dynamics

Network link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications, e.g., estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases, the measured time series data may be subject to limitations, including limited duration, low sampling rate, observational noise, and partial nodal state measurement. However, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from Caenorhabditis elegans neural activity to systematically assess the influence of data limitations on the character of scores reflecting the likelihood of a directed link between a given node pair. We do this for three network inference techniques: Granger causality, transfer entropy, and, a machine learning-based method. Furthermore, we assess the ability of appropriate surrogate data to determine statistical confidence levels associated with the results of link-inference techniques.

Line graph contrastive learning for link prediction

Link prediction tasks focus on predicting possible future connections. Most existing researches measure the likelihood of links by different similarity scores on node pairs and predict links between nodes. However, the similarity-based approaches have some challenges in information loss on nodes and generalization ability on similarity indexes. To address the above issues, we propose a Line Graph Contrastive Learning (LGCL) method to obtain rich information with multiple perspectives. LGCL obtains a subgraph view by h-hop subgraph sampling with target node pairs. After transforming the sampled subgraph into a line graph, the link prediction task is converted into a node classification task, which graph convolution progress can learn edge embeddings from graphs more effectively. Then we design a novel cross-scale contrastive learning framework on the line graph and the subgraph to maximize the mutual information of them, so that fuses the structure and feature information. The experimental results demonstrate that the proposed LGCL outperforms the state-of-the-art methods and has better performance on generalization and robustness.

An Exact and an Approximation Method to Compute the Degree Distribution of Inhomogeneous Random Graph Using Poisson Binomial Distribution

Inhomogeneous random graphs are commonly used models for complex networks where nodes have varying degrees of connectivity. Computing the degree distribution of such networks is a fundamental problem and has important applications in various fields. We define the inhomogeneous random graph as a random graph model where the edges are drawn independently and the probability of a link between any two vertices can be different for each node pair. In this paper, we present an exact and an approximation method to compute the degree distribution of inhomogeneous random graphs using the Poisson binomial distribution. The exact algorithm utilizes the DFT-CF method to compute the distribution of a Poisson binomial random variable. The approximation method uses the Poisson, binomial, and Gaussian distributions to approximate the Poisson binomial distribution.

ADA-PC: An asynchronous distributed algorithm for minimizing pairwise connectivity in wireless multi-hop networks

Reliability analysis is of great significance to designing and maintaining wireless multi-hop networks (WMhNs). In WMhNs, several reasons can cause a node to be inoperable, such as hardware failure, software errors, and battery drain. Failure of some critical nodes may partition networks into disconnected segments. The presence of such critical nodes may also reduce the network lifetime since they consume more energy for packet forwarding. Therefore, it is crucial to identify the critical nodes of the networks and strengthen them by adding more nodes surrounding those or creating alternate pathways connecting other nodes to ensure connectivity maintenance in WMhNs. One of the most common approaches in direction is detecting the cut nodes of the networks. However, although finding cut nodes provide helpful information, it may be insufficient for precise reliability analysis since finding cut nodes only does not consider the remaining network. Critical Node Problem (CNP) aims to detect the most important nodes of the network whose removal minimizes the pairwise connectivity (the total number of node pairs connected by at least one path). In other words, the CNP tries to identify a set of nodes whose absence partitions the network into several disconnected segments of similar size. Detecting critical nodes for pairwise connectivity reveals the weak points and bottlenecks of the networks and may help to increase the fault tolerance and lifetime of WMhNs. This paper proposes an Asynchronous Distributed Algorithm for minimizing Pairwise Connectivity (ADA-PC) in WMhNs. To the best of our knowledge, this is the first distributed algorithm for the targeted problem in the network literature. The proposed algorithm uses a distributed Breadth-First Search (BFS) tree which limits bit complexity to O(n.m.log2n) and space complexity to O(d), where d is the network’s diameter. The experimental study on both testbed experiment and simulation reveals that the proposed algorithm is capable of finding the most critical nodes with up to 60% lower sent bytes than the existing central algorithms.

Modeling and prediction for frequency response functions of parameter-varying mechanical systems based on generalized receptance coupling substructure analysis

Most machining systems such as machine tools and robots are parameter-varying mechanical systems, which show different dynamic characteristics under different parameters. Generally, a large number of modal tests are required for parameter-varying mechanical systems to obtain frequency response functions (FRFs) under different system parameters, which reduces efficiency. Receptance coupling substructure analysis (RCSA) provides ideas for solving such problems. In this study, the generalized RCSA (GRCSA) was proposed. The coupling method of two substructures at arbitrary pairs of nodes was derived, and a more general coupling method of multiple substructures at arbitrary pairs of nodes was further elaborated, which provides a complete set of theories and methods for the modeling and prediction of FRF of general parameter-varying mechanical systems. Based on the derivation process and results of the GRCSA, the parameter-varying mechanical systems are classified into three basic categories: variable interface systems, variable spatial attitude systems, and variable substructure systems. Typical cases of three basic categories in engineering practice were selected for studying, and the prediction models for FRFs of various parameter-varying mechanical systems were derived in detail. The calculation methods for FRF matrices of various substructures and the calibration methods for interface parameters are given. The validity of the prediction models was verified by experiments and simulations. The relative errors of the established prediction models under most system variables are less than 1% for the natural frequency, and the maximum relative error of the prediction models is 3.775% for the natural frequency. Finally, based on the prediction models, the FRFs under different system variables were predicted, and the variation laws of the natural frequencies with the system variables were analyzed. The modeling processes based on the GRCSA for the FRFs of typical parameter-varying mechanical systems are general, and can provide references and ideas for the study of frequency response characteristics of other complex parameter-varying mechanical systems.

A NOTCH1 Mutation Found in a Newly Established Ovarian Cancer Cell Line (FDOVL) Promotes Lymph Node Metastasis in Ovarian Cancer

Peritoneal implantation and lymph node metastasis have different driving mechanisms in ovarian cancer. Elucidating the underlying mechanism of lymph node metastasis is important for treatment outcomes. A new cell line, FDOVL, was established from a metastatic lymph node of a patient with primary platinum-resistant ovarian cancer and was then characterized. The effect of NOTCH1-p.C702fs mutation and NOTCH1 inhibitor on migration was evaluated in vitro and in vivo. Ten paired primary sites and metastatic lymph nodes were analyzed by RNA sequencing. The FDOVL cell line with serious karyotype abnormalities could be stably passaged and could be used to generated xenografts. NOTCH1-p.C702fs mutation was found exclusively in the FDOVL cell line and the metastatic lymph node. The mutation promoted migration and invasion in cell and animal models, and these effects were markedly repressed by the NOTCH inhibitor LY3039478. RNA sequencing confirmed CSF3 as the downstream effector of NOTCH1 mutation. Furthermore, the mutation was significantly more common in metastatic lymph nodes than in other peritoneal metastases in 10 paired samples (60% vs. 20%). The study revealed that NOTCH1 mutation is probably a driver of lymph node metastasis in ovarian cancer, which offers new ideas for the treatment of ovarian cancer lymph node metastasis with NOTCH inhibitors.

On optimally solving sub‐tree scheduling for wireless sensor networks with partial coverage: A branch‐and‐cut algorithm

AbstractGiven a wireless sensor network, we consider the problem to minimize its total energy consumption over consecutive time slots with respect to communication activities. Nonempty and disjoint subsets of nodes are required to be active and connected under a tree topology configuration in the different time slots, and each network node must be active in a unique time slot. Moreover, the power required by the same pair of network nodes to communicate on the associated direct channel may vary in the different time slots. The problem has been recently introduced in the literature under the name Sub‐Tree Scheduling for Wireless Sensor Networks with Partial Coverage. We focus on the exact solution of the problem. We present a branch‐and‐cut (BC) algorithm based on a novel integer linear programming formulation which allows avoiding the introduction of symmetries in the solution space. In particular, the algorithm relies on an efficient and nontypical separation algorithm for known valid inequalities, and on an easy‐to‐implement primal bound heuristic. The effectiveness of the BC algorithm is empirically shown through an extensive experimental analysis involving 300 newly generated benchmark instances with up to 200 network nodes and 8 time slots. Additionally, the experimental results show that the BC algorithm represents a valid computational tool to benchmark the performance of heuristics addressing the problem, and can be used in practice, as an heuristic solver, to tackle problem instances that are not too large.

Quantitative Analysis of Flow Distributions in a Multiuser Telecommunication Network

Strategies are considered for the distribution of flows over various transmission routes in a multicommodity network model. Estimates of feasible network loads are formed based on the vector of jointly feasible internodal flows. Two distribution strategies are studied. When implementing the first one, the transmitted internodal flows are equal to each other. The second strategy assumes the search for a nondiscriminatory distribution of flows, during the transmission of which equal value resources are achieved. The load created by a certain pair of nodes is understood as the total capacity required to provide the given type of connection. To estimate the minimum specific resources for the transmission of a flow of a certain type, all the shortest paths between the corresponding pair of nodes are constructed. To obtain an upper estimate of resources when connecting each pair of nodes, the maximum single-product flow is calculated along all the network edges. Computational experiments were carried out for networks with different structures that make it possible to carry out a comparative analysis of equalizing distribution strategies when splitting internodal flows for transmission along different routes.

Functional Connectivity and Small-World Networks in Prion Disease

We characterized prion disease by comparing brain functional connectivity network (BFCN), which were constructed by 16-ch scalp-recorded electroencephalograms (EEGs). The connectivity between each pair of nodes (electrodes) were computed by synchronization likelihood (SL). The BFCN was applied to graph theory to discriminate prion disease patients from healthy elderlies and dementia groups.

Representing Paths in Graph Database Pattern Matching

Modern graph database query languages such as GQL, SQL/PGQ, and their academic predecessor G-Core promote paths to first-class citizens in the sense that their pattern matching facility can return paths , as opposed to only nodes and edges. This is challenging for database engines, since graphs can have a large number of paths between a given node pair, which can cause huge intermediate results in query evaluation. We introduce the concept of path multiset representations (PMRs) , which can represent multisets of paths exponentially succinctly and therefore bring significant advantages for representing intermediate results. We give a detailed theoretical analysis that shows that they are especially well-suited for representing results of regular path queries and extensions thereof involving counting, random sampling, and unions. Our experiments show that they drastically improve scalability for regular path query evaluation, with speedups of several orders of magnitude.

LIGHT WEIGHT SECURITY ALGORITHM FOR LOW POWER IOT DEVICE

Many IOT devices(mainly networks of IOT devices or networks that are deployed to monitor calamity situations) are deployed in an arbitrary and unplanned fashion. For any sensor in such a network can end up being adjacent node to any other IOT node in the network. To Establish a secure communication between every pair of adjacent IOTs node in such a network, each sensor node x in the network needs to store n -1 number of symmetric keys that sensor node x shares with all the other sensor nodes, where n is the number of sensor nodes in the present network. This memory storage requirement of the keying protocol is various, especially when n is large and the available storage in each sensor node is modest. Previous efforts to redesign this keying protocol and reduce the number of keys to be stored in each sensor node have produced protocols that are vulnerable to impersonation, eavesdropping, and collusion attacks. In this paper, we present a fully secure protocol.

DNA-methylation and genomic copy number in primary tumors and corresponding lymph node metastases in prostate cancer from patients with low and high Gleason score

In prostate cancer, the indication to irradiate the pelvic lymphatic pathways in clinical node-negative patients is solely based on clinical nomograms. To define biological risk patterns of lymphatic spread, we studied DNA-methylation and genomic copy number in primary tumors and corresponding lymph nodes metastases. DNA-methylation and genomic copy number profiles of primary tumors (PT) and paired synchronous lymph node metastases (LN) from Gleason Score (GS)-6/7a (n=20 LN-positive, n=20 LN-negative) and GS-9/10 patients (LN-positive n=20) after prostatectomy and lymphonodectomy were analyzed. GS-6/7a pN0 PTs and GS-6/7a pN1 PTs differed in histone H3K27me3/H3K9me3 mediated methylation. PTs compared to LNs, in both, GS-6/7a pN1 and GS-9/10 pN1 patients showed large differences in DNA-methylation mediated by histones H3K4me1/2, in addition to copy number changes of chromosomal regions 11q13.1, 14q11.2 and 15q26.1. Between GS-6/7a pN1 and GS-9/10 pN1 patients, methylation levels differed more when comparing LNs than PTs. 16q21-22.1 was specifically lost in GS-9/10 pN0 PTs. Immune system-related pathways characterized the differences between PTs and LNs in both GS-6/7a pN1 and GS-9/10 pN1 patients. Comparing PTs and LKs between GS-6/7a pN1 and GS-9/10 pN1 patients revealed altered transmembrane and G-protein-coupled receptor signaling. Our data suggest that progression of prostate cancer, including lymphatic spread, is associated with histone-mediated DNA-methylation and we hypothesize a methylation signature predicting lymphatic spread in GS-6/7a patients from primary tumors. Lymphatic spread in GS-6/7a patients, flanked by DNA-methylation and CNA alterations, appears to be more complex than in GS-9/10 patients, in whom the primary tumors already appear to bear lymph node metastasis-enabling alterations.

A graph neural network model for deciphering the biological mechanisms of plant electrical signal classification

The classification methods of the electrical signal can be effectively applied for the identification of salt tolerance in plants. Biotechnology can help researchers discover genes and proteins related to plant electrical signals, thereby deciphering the biological mechanisms of electrical signal classification, but it is time-consuming and expensive only using biological technologies. Although protein interaction relationships can be predicted using deep learning, the relationships between plant electrical signals and proteins are unclear. This uncertainty can directly lead to protein information incompletion or lack of plant electrical signal data, which is not conducive to prediction performance. To this end, we propose a graph neural network model that integrates plant electrical signal features to discover electrical signal-related proteins (PMESP). In particular, the model constructs protein interaction relationships as a graph structure, where nodes represent proteins. To obtain sufficient feature information of these nodes, we develop an electrical signal feature extraction model based on a bidirectional long short-term memory and utilize a pre-trained neural network to extract the semantic information features of proteins. In the PMESP, the features of the electrical signals and proteins are fused as the input of graph convolutional neural network (GCN), the representations of pairs of nodes are calculated by multilayer GCNs, and then the new protein interaction relationships are predicted by the comparison of the scores between nodes connected by edges to the scores between any pair of nodes. We finally assemble a dataset containing the relationships between electrical signals and protein interactions, employ the method of light induced rhythmic bioelectrogenesis to collect data from Arabidopsis thaliana leaves under salt stress, and use weighted gene co-expression network analysis and crawler methods to obtain Arabidopsis thaliana protein interaction relationships. The results illustrate that the area under the receiver operating characteristic curve and area under precision-recall curve values of the model on the test set are 0.9624 and 0.9684 respectively, which are significantly higher than other link predicting models and previous existing methods.

Triadic Closure Sensitive Influence Maximization

The influence are not linked to any footnote in the text. Please check and suggest. maximization problem aims at selecting the k most influential nodes (i.e., seed nodes) from a social network, where the nodes can maximize the number of influenced nodes activated by a certain propagation model. However, the widely used Independent Cascade model shares the same propagation probability among substantial adjacent node pairs, which is too idealistic and unreasonable in practice. In addition, most heuristic algorithms for influence maximization need to update the expected influence of the remaining nodes in the seed selection process, resulting in high computation cost. To address these non-trivial problems, we propose a novel edge propagation probability calculation method. The method first utilizes the triadic closure structure of social networks to precisely measure the closeness between nodes and assigns different propagation probabilities to each edge, deriving a Triadic Closure-based Independent Cascade (TC-IC) model. Then, we further propose a heuristic influence maximization algorithm named Triadic Closure-based Influence Maximization (TC-IM). The algorithm evaluates the expected influence of a node by integrating the triadic closure weighted propagation probability and the triadic closure weighted degree. Especially, in the seed selection process, only the most influential node that has not been updated in the current round needs to be updated, which significantly improves the efficiency. Besides, we further provide theoretical proofs to guarantee the correctness of this updating strategy. Experimental results on nine real datasets and three propagation models demonstrate that: (1) The TC-IC model can set a proper propagation probability for each node pair, where the IM algorithms could easily identify influential nodes; (2) The TC-IM algorithm can significantly reduce the complexity through an efficient updating strategy with a comparable influence spread to the approximation IM algorithms; (3) Besides, the TC-IM algorithm also exhibits stable performance under other IC models including UIC and WIC, exhibiting good stability and generality.

Anomalous Hall Conductivity and Nernst Effect of the Ideal Weyl Semimetallic Ferromagnet EuCd2 As2.

Weyl semimetal is a unique topological phase with topologically protected band crossings in the bulk and robust surface states called Fermi arcs. Weyl nodes always appear in pairs with opposite chiralities, and they need to have either time-reversal or inversion symmetry broken. When the time-reversal symmetry is broken the minimum number of Weyl points (WPs) is two. If these WPs are located at the Fermi level, they form an ideal Weyl semimetal (WSM). In this study, intrinsic ferromagnetic (FM) EuCd2 As2 are grown, predicted to be an ideal WSM and studied its electronic structure by angle-resolved photoemission spectroscopy, and scanning tunneling microscopy which agrees closely with the first principles calculations. Moreover, anomalous Hall conductivity and Nernst effect are observed, resulting from the non-zero Berry curvature, and the topological Hall effect arising from changes in the band structure caused by spin canting produced by magnetic fields. These findings can help realize several exotic quantum phenomena in inorganic topological materials that are otherwise difficult to assess because of the presence of multiple pairs of Weyl nodes.

Front Propagation and Arrival Times in Networks with Application to Neurodegenerative Diseases

Many physical, epidemiological, or physiological dynamical processes on networks support front-like propagation, where an initial localized perturbation grows and systematically invades all nodes in the network. A key problem is then to extract estimates for the dynamics. In particular, if a single node is seeded at a small concentration, when will other nodes reach the same initial concentration? Here, motivated by the study of toxic protein propagation in neurodegenerative diseases, we present and compare three different estimates for the arrival time in order of increasing analytical complexity: the linear arrival time, obtained by linearizing the underlying dynamical system; the Lambert time, obtained by considering the interaction of pairs of nodes; and the nonlinear arrival time, obtained by asymptotic techniques. We use the classic Fisher–Kolmogorov–Petrovsky–Piskunov equation as a paradigm for the dynamics and show that each method provides different insights but consistent time estimates. Further, we show that the nonlinear asymptotic method also gives an approximate solution, valid in the entire domain, and the correct ordering of arrival regions over large regions of parameters and initial conditions.

SigGAN: Adversarial Model for Learning Signed Relationships in Networks

Signed link prediction in graphs is an important problem that has applications in diverse domains. It is a binary classification problem that predicts whether an edge between a pair of nodes is positive or negative. Existing approaches for link prediction in unsigned networks cannot be directly applied for signed link prediction due to their inherent differences. Furthermore, signed link prediction must consider the inherent characteristics of signed networks, such as structural balance theory. Recent signed link prediction approaches generate node representations using either generative models or discriminative models. Inspired by the recent success of Generative Adversarial Network (GAN) based models in several applications, we propose a GAN based model for signed networks, SigGAN. It considers the inherent characteristics of signed networks, such as integration of information from negative edges, high imbalance in number of positive and negative edges, and structural balance theory. Comparing the performance with state-of-the-art techniques on five real-world datasets validates the effectiveness of SigGAN.

A new link prediction method to alleviate the cold-start problem based on extending common neighbor and degree centrality

The cold-start problem occurs when a new user with limited information joins the network, and it becomes challenging to predict new links in future networks. Several studies have proposed link prediction methods based on common neighbors by exploring topology information using the Triadic Closure concept. However, the common neighbor failed to predict future relations because the new user with cold-start problems was isolated and had no common neighbors. This study proposes a common neighbor enhanced by the proposed gravity of node pairs inspired by Newton’s law of gravity called Degree of Gravity for Link Prediction (DGLP). The DGLP considers degree centrality, common neighbors, and distance between candidate node pairs generated by topological information in a single-layer network. The proposed DGLP was evaluated using sixteen datasets and nine benchmark methods. The evaluation results showed that DGLP could increase Area Under the Curve (AUC) values by 7.15%, and the average AUC value reached 0.819 for experiments with 10-fold cross-validation. In addition, the calculated ratio of successfully predicted and node pairs with the cold-start problem achieved 99.94%. The prediction ratio is calculated to ensure that DGLP alleviates the cold-start problem and outperforms benchmark methods.