Direct Neighbors Research Articles

A high-order generalised differential quadrature element method for simulating 2D and 3D incompressible flows on unstructured meshes

In this paper, a high-order generalised differential quadrature element method (GDQE) is proposed to simulate two-dimensional (2D) and three-dimensional (3D) incompressible flows on unstructured meshes. In this method, the computational domain is decomposed into unstructured elements. In each element, the high-order generalised differential quadrature (GDQ) discretisation is applied. Specifically, the GDQ method is utilised to approximate the partial derivatives of flow variables and fluxes with high-order accuracy inside each element. At the shared interfaces between different GDQ elements, the common flux is computed to account for the information exchange, which is achieved by the lattice Boltzmann flux solver (LBFS) in the present work. Since the solution in each GDQ element solely relies on information from itself and its direct neighbouring element, the developed method is authentically compact, and it is naturally suitable for parallel computing. Furthermore, by selecting the order of elemental GDQ discretisation, arbitrary accuracy orders can be achieved with ease. Representative incompressible flow problems, including 2D laminar flows as well as 3D turbulent simulations, are considered to evaluate the accuracy, efficiency, and robustness of the present method. Successful numerical simulations, especially for scale-resolving 3D turbulent flow problems, confirm that the present method is efficient and high-order accurate.

The impact of social status on the formation of collaborative ties and effort provision: An experimental study

We study whether competition for social status induces higher effort provision and efficiency when individuals collaborate with their network neighbors. We consider a laboratory experiment in which individuals choose a costly collaborative effort and their network neighbors. They benefit from their neighbors’ effort and effort choices of direct neighbors are strategic complements. We introduce two types of social status in a 2 × 2 factorial design: (1) individuals receive monetary benefits for incoming links representing popularity; (2) they receive feedback on their relative payoff ranking within the group. We find that link benefits induce higher effort provision and strengthen the collaborative ties relative to the Baseline treatment without social status. In contrast, the ranking information induces lower effort as individuals start competing for higher ranking. Overall, we find that social status has no significant impact on the number of links in the network and the efficiency of collaboration in the group.

Enhanced reinforcement learning-based two-way transmit-receive directional antennas neighbor discovery in wireless ad hoc networks

The utilization of directional antennas for neighbor discovery in wireless ad hoc networks brings notable benefits, such as extended transmission range, reduced transmission interference, and enhanced antenna gain. However, when nodes use directional antennas for neighbor discovery, the communication range is limited, resulting in a lack of knowledge of potential neighbors. Hence, it is necessary to design a special antenna direction switching strategy for neighbor discovery based on directional antennas. Traditional methods of switching antenna directions are often random or follow predefined sequences, overlooking the historical knowledge of sector exploration for antenna directions. In contrast, existing machine learning approaches aim to leverage observed historical knowledge to adjust antenna directions for faster neighbor discovery. Nonetheless, the latency of neighbor discovery is still high because the node cannot fully utilize the observed historical knowledge (i.e., only using the knowledge observed by the node in transmission mode, ignoring the knowledge observed by the node in reception mode). Meanwhile, the corresponding reward and penalty mechanisms are still not detailed enough (i.e., these reward and penalty mechanisms only consider the sectors of discovered and undiscovered neighboring nodes, ignoring the scenario of sectors that have been rewarded). In this paper, the neighbor discovery process is modeled as a reinforcement learning-based learning automaton. We propose an enhanced reinforcement learning-based two-way transmit-receive directional antennas neighbor discovery algorithm, called ERTTND. The algorithm consists of a two-way transmit-receive reinforcement learning mechanism (TTRL) and an enhanced reward-and-penalty mechanism (ERAP). This algorithm leverages insights from nodes in transmission and reception modes to refine their tactical decisions. Then, through an enriched reward-and-penalty framework, nodes optimize their strategies, thus expediting neighbor discovery based on directional antennas in wireless ad hoc networks. Simulation results demonstrate that compared to existing representative algorithms, the proposed ERTTND algorithm can achieve over 30% savings in terms of average discovery delay and energy consumption.

On the Complexity of Target Set Selection in Simple Geometric Networks

We study the following model of disease spread in a social network. At first, all individuals are either infected or healthy. Next, in discrete rounds, the disease spreads in the network from infected to healthy individuals such that a healthy individual gets infected if and only if a sufficient number of its direct neighbors are already infected. We represent the social network as a graph. Inspired by the real-world restrictions in the current epidemic, especially by social and physical distancing requirements, we restrict ourselves to networks that can be represented as geometric intersection graphs. We show that finding a minimal vertex set of initially infected individuals to spread the disease in the whole network is computationally hard, already on unit disk graphs. Hence, to provide some algorithmic results, we focus ourselves on simpler geometric graph classes, such as interval graphs and grid graphs.

Retinal waves in adaptive rewiring networks orchestrate convergence and divergence in the visual system.

Spontaneous retinal wave activity shaping the visual system is a complex neurodevelopmental phenomenon. Retinal ganglion cells are the hubs through which activity diverges throughout the visual system. We consider how these divergent hubs emerge, using an adaptively rewiring neural network model. Adaptive rewiring models show in a principled way how brains could achieve their complex topologies. Modular small-world structures with rich-club effects and circuits of convergent-divergent units emerge as networks evolve, driven by their own spontaneous activity. Arbitrary nodes of an initially random model network were designated as retinal ganglion cells. They were intermittently exposed to the retinal waveform, as the network evolved through adaptive rewiring. A significant proportion of these nodes developed into divergent hubs within the characteristic complex network architecture. The proportion depends parametrically on the wave incidence rate. Higher rates increase the likelihood of hub formation, while increasing the potential of ganglion cell death. In addition, direct neighbors of designated ganglion cells differentiate like amacrine cells. The divergence observed in ganglion cells resulted in enhanced convergence downstream, suggesting that retinal waves control the formation of convergence in the lateral geniculate nuclei. We conclude that retinal waves stochastically control the distribution of converging and diverging activity in evolving complex networks.

Multi-view Heterogeneous Graph Neural Networks for Node Classification

Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been proposed for processing heterogeneous graphs, which are termed Heterogeneous graph neural networks (HGNNs). However, existing HGNNs tend to aggregate information from either direct neighbors or those connected by short metapaths, thereby neglecting the higher-order information and global feature similarity information in heterogeneous graphs. In this paper, we propose a Multi-View Heterogeneous graph neural network (MV-HGNN) to aggregate these information. Firstly, two auxiliary views, specifically a global feature similarity view and a graph diffusion view, are generated from the original heterogeneous graph. Secondly, MV-HGNN performs two message-passing strategies to get the representation of different views. Subsequently, a transformer-based aggregator is used to get the semantic information. Subsequently, the representations of the three views are fused into a final composite representation. We evaluate our method on the node classification task over three commonly used heterogeneous graph datasets, and the results demonstrate that our proposed MV-HGNN significantly outperforms state-of-the-art baselines.

Rebalancing agri-food trade flows due to Russian import ban: the case of direct neighbors

In recent years, research on trade rebalancing in agri-food supply chains has gained prominence due to trade sanctions, supply chain disruptions, and vulnerabilities exposed by pandemics and conflicts. This study focuses on the recalibration of agri-food trade dynamics, using the 2014 Russian import ban as a case study. The ban significantly altered the structure of agri-food export destinations for affected countries, particularly those sharing a border with Russia (e.g., Poland, Finland, Estonia, Latvia, Lithuania). Employing a cross-section regression model and structural break tests, we assess the trade rebalancing process. Our findings reveal short-term trade rebalancing effects, primarily observed in product groups not traditionally considered main trade specializations. There is evidence that significant part of the lost trade was redirected to EU28 or EAEU countries and end up in the common trade areas of these countries. Furthermore, we argue that the Russian import ban initiated a long-term structural shift in export patterns for non-traditionally traded banned products, while rebalancing for traditionally traded products was significantly quicker.

A Novel Approach to Image Retrieval for Vision-Based Positioning Utilizing Graph Topology

Abstract. This research introduces a novel approach to improve vision-based positioning in the absence of GNSS signals. Specifically, we address the challenge posed by obstacles that alter image information or features, making retrieving the query image from the database difficult. While the Bag of Visual Words (BoVW) is a widely used image retrieval technique, it has a limitation in representing each image with a single histogram vector or vocabulary of visual words, i.e., the emergence of obstacles can introduce new features to the query image, resulting in different visual words. Our study overcomes this limitation by clustering the features of each image using the k-means method and generating a graph for each class. Each node or key point in the graph obtains additional information from its direct neighbors using functions employed in graph neural networks, functioning as a feedforward network with constant parameters. This process generates new embedding nodes, and eventually, global pooling is applied to produce one vector for each graph, representing each image with graph vectors based on objects or feature classes. As a result, each image is represented with graph vectors based on objects or feature classes. In the presence of obstacles covering one or more graphs, there is sufficient information from the query image to retrieve the most relevant image from the database. Our approach was applied to indoor positioning applications, with the database collected in Bolz Hall at The Ohio State University. Traditional BoVW techniques struggle to properly retrieve most query images from the database due to obstacles like humans or recently deployed objects that alter image features. In contrast, our approach has shown progress in image retrieval by representing each image with multiple graph vectors, depending on the number of objects in the image. This helps prevent or mitigate changes in image features caused by obstacles covering or adding features to the image, as demonstrated in the results.

Network Fairness Ambivalence: When does social network capital mitigate or amplify unfairness?

Social networks inherit societal biases present across lines of gender, race, socioeconomic status, and other factors. Networks can structurally perpetuate unequal access to information and opportunities through homophilous dynamics. While there is substantial knowledge about inequity in the diffusion of opportunities in a network where nodes seek them from their immediate neighbors, much less is known when considering beyond that first hop. In this paper, we leverage recent mathematical analysis of network fairness to prove that enabling simple multi-hop dissemination can reduce inequity towards a minority group in the network as long as homophily is sufficiently weak. Otherwise, our necessary and sufficient condition proves that multi-hop dissemination strategies amplify the bias already found amongst considering direct neighbors. We empirically validate these results on four social network datasets as well as present an example of a key application of our findings with a scenario of individuals who leverage their personal network to seek job referrals. Our results suggest that online platforms designing algorithms to promote opportunities to multi-hop connections must carefully take into account network metrics measuring group size and homophily in order to avoid amplifying bias against marginalized groups on their platforms.

Implikacje wojny rosyjsko-ukraińskiej dla środowiska bezpieczeństwa Polski

The prevailing features of the environment of modern countries are uncertainty, chaos and a completely new quality of emerging changes. The contemporary international environment is complex and multidimensional, and the conflict in Ukraine that has been ongoing since 2014 is a factor that significantly destabilizes it. When analyzing the situation in the region in the context of regional security complexes, issues related to Poland’s security should also be viewed through the prism of the situation in Ukraine, which is its direct neighbor. Russia’s attack on Ukraine destabilized the security environment in Europe and strengthened the concerns of those in power to ensure Poland’s security and mobilized the international community to take active action. The aim of this article is therefore to try to find an answer to the question: what is the state of Poland’s security environment? What implications does the war in Ukraine have for Poland’s security?In order to effectively solve the research problem, an attempt will be made to answer the following research questions: What is Poland’s security environment in terms of the changes that have occurred in the international arena as a result of Russia’s aggression against Ukraine? What consequences does the war in Ukraine have for the security of Poland? Is it possible to predict, albeit to a limited extent, scenarios for Poland’s security environment in the Ukrainian context? This article is an attempt to look at Poland’s security environment in the aspect of changes that have occurred in the international arena as a result of the Russian invasion of Ukraine.

Applying self-supervised learning to network intrusion detection for network flows with graph neural network

Graph Neural Networks (GNNs) have garnered intensive attention for Network Intrusion Detection System (NIDS) due to their suitability for representing the network traffic flows. However, most present GNN-based methods for NIDS are supervised or semi-supervised. Network flows need to be manually annotated as supervisory labels, a process that is time-consuming or even impossible, making NIDS difficult to adapt to potentially complex attacks, especially in large-scale real-world scenarios. The existing GNN-based self-supervised methods focus on the binary classification of network flow as benign or not, and thus fail to reveal the types of attack in practice. This paper studies the application of GNNs to identify the specific types of network flows in an unsupervised manner. We first design an encoder to obtain graph embedding, that introduces the graph attention mechanism and considers the edge information as the only essential factor. Then, a self-supervised method based on graph contrastive learning is proposed. The method samples center nodes, and for each center node, generates subgraph by it and its direct neighbor nodes, and corresponding contrastive subgraph from the interpolated graph, and finally constructs positive and negative samples from subgraphs. Furthermore, a structured contrastive loss function based on edge features and graph local topology is introduced. To the best of our knowledge, it is the first GNN-based self-supervised method for the multiclass classification of network flows in NIDS. Detailed experiments conducted on four real-world datasets (NF-Bot-IoT, NF-Bot-IoT-v2, NF-CSE-CIC-IDS2018, and NF-CSE-CIC-IDS2018-v2) systematically compare our model with the state-of-the-art supervised and self-supervised models, illustrating the considerable potential of our method. Our code is accessible through https://github.com/renj-xu/NEGSC.

Multi-view contrastive learning with virtual social group influence for social recommendation

Social recommendation systems leverage user–item interaction and user–user social network data to model user preferences and provide recommendations. Previous research has shown that capturing the influence of high-order neighbors and social groups in social networks can help model user preferences. However, challenges still exist in identifying important high-order neighbors from many candidates and modeling social group influence without direct information on social groups. This paper proposes a Multi-view Contrastive Learning with Social Group Influence (MCLSGI) method to address these challenges. Our approach uses a graph walk method to identify users’ virtual social groups and a GNN-based framework to model user preferences in different views, capturing the influence of direct and high-order neighbors as well as social groups. We also adopt multi-view contrastive learning to fuse users’ preferences in different views. We conducted experiments on two real-world datasets (Ciao and Epinions) to validate our method’s effectiveness. Compared to the best baseline, we improved by 1.39% and 1.21% with MAE, and 1.79% and 0.96% with RMSE.

A Graph Dynamics Prior for Relational Inference

Relational inference aims to identify interactions between parts of a dynamical system from the observed dynamics. Current state-of-the-art methods fit the dynamics with a graph neural network (GNN) on a learnable graph. They use one-step message-passing GNNs---intuitively the right choice since non-locality of multi-step or spectral GNNs may confuse direct and indirect interactions. But the effective interaction graph depends on the sampling rate and it is rarely localized to direct neighbors, leading to poor local optima for the one-step model. In this work, we propose a graph dynamics prior (GDP) for relational inference. GDP constructively uses error amplification in non-local polynomial filters to steer the solution to the ground-truth graph. To deal with non-uniqueness, GDP simultaneously fits a ``shallow'' one-step model and a polynomial multi-step model with shared graph topology. Experiments show that GDP reconstructs graphs far more accurately than earlier methods, with remarkable robustness to under-sampling. Since appropriate sampling rates for unknown dynamical systems are not known a priori, this robustness makes GDP suitable for real applications in scientific machine learning. Reproducible code is available at https://github.com/DaDaCheng/GDP.

Some New Generators to Produce Minimal Circular Strongly Balanced Generalized Neighbor Designs of Class-I

Minimal circular strongly balanced neighbor designs control the neighbor effects economically but these designs can only be constructed for v odd. For v even, their alternates are minimal circular strongly balanced generalized neighbor designs of class-I (MCSBGNDs-I) which are the designs in which (i) each treatment appears exactly once with itself as neighbors, (ii) v/2 unordered pairs appear twice as neighbors while the remaining ones appear once. On the basis of constructors developed by Munir et al. (2023), some new generators are developed in this article to generate cyclic shifts for efficient MCSBGNDs-I in blocks of (i) equal sizes, (ii) two different sizes, and (iii) three different sizes for m (mod 4) ≡ 2 & 3 with v even and k = 4l, k = 4l+2, k (odd) > 3, k (mod) ≡ 1 & k (mod) ≡ 3. Efficiency of neighbor effects and of Separability show that our proposed generators produce designs which control the neighbor effects efficiently as well as estimate the direct effects and neighbor effects independently.

Exploiting Cation Structure and Water Content in Modulating the Acidity of Ammonium Hydrogen Sulfate Protic Ionic Liquids.

In this paper, we investigated the effect of cation structure and water content on proton dissociation in alkylammonium [HSO4]- protic ionic liquids (ILs) doped with 20 wt % water and correlated this with experimental Hammett acidities. For pure systems, increased cation substitution resulted in a reduction in the number of direct anion-anion neighbors leading to larger numbers of small aggregates, which is further enhanced with addition of water. We also observed spontaneous proton dissociation from [HSO4]- to water only for primary amine-based protic ILs, preceded by the formation of an anion trimer motif. Investigation using DFT calculations revealed spontaneous proton dissociation from [HSO4]- to water can occur for each of the protic ILs investigated; however, this is dependent on the size of the anion aggregates. These findings are important in the fields of catalysis and lignocellulosic biomass, where solvent acidity is a crucial parameter in biomass fractionation and lignin chemistry.

Learning higher-order features for relation prediction in knowledge hypergraph

Knowledge Hypergraph (KHG) is a higher-order extension of the Knowledge Graph (KG), and its relation prediction is based on known data to predict unknown higher-order relations, thereby providing useful knowledge services. However, the existing KHG relation algorithms still have some limitations: (i) most studies only consider the influence of the direct neighbors, and (ii) they ignore the complex interactions existing inside higher-order facts. Based on this, we propose a KHG relation prediction model HoGCNF2 based on higher-order hypergraph convolutional network and feature fusion. Dual-channel hypergraph convolutional network considers the significant and higher-order information propagation of entities. Feature fusion strategy considers different types of higher-order structures. Besides, attention mechanism adaptively assigns weights to the learned embeddings. Extensive experiments demonstrate the superiority of HoGCNF2 on different datasets. Specifically, the MRR result improves by 2.6% on the unfixed dataset FB-AUTO, and improves by 9.7% on the fixed dataset WikiPeople-4. Our implementations are publicly available at: https://doi.org/10.24433/CO.5584354.v1.

Self-Organized Polygon Formation Control Based on Distributed Estimation

This paper studies the problem of controlling a multi-robot system to achieve a polygon formation in a self-organized manner. Different from the typical formation control strategies where robots are steered to satisfy the predefined control variables, such as pair-wise distances, relative positions and bearings, the foremost idea of this paper is to achieve polygon formations by injecting control inputs randomly to a few robots (say, vertex robots) of the group, and the rest follow the simple principles of moving towards the midpoint of their two nearest neighbors in the ring graph without any external inputs. In our problem, a fleet of robots is initially distributed in the plane. The so-called vertex robots take the responsibility of determining the geometric shape of the entire formation and its overall size, while the others move so as to minimize the differences with two direct neighbors. In the first step, each vertex robot estimates the number of robots in its associated chain. Two types of control inputs that serve for the estimation are designed using the measurements from the latest and the last two time instants respectively. In the second step, the self-organized formation control law is proposed where only vertex robots receive external information. Comparisons between the two estimation strategies are carried out in terms of the convergence speed and robustness. The effectiveness of the whole control framework is further validated in both simulation and physical experiments.

Inside the bubble: exploring the environments of reionisation-era Lyman-α emitting galaxies with JADES and FRESCO

We present a study of the environments of 17 Lyman-α emitting galaxies (LAEs) in the reionisation-era (5.8 < z < 8) identified by JWST/NIRSpec as part of the JWST Advanced Deep Extragalactic Survey (JADES). Unless situated in sufficiently (re)ionised regions, Lyman-α emission from these galaxies would be strongly absorbed by neutral gas in the intergalactic medium (IGM). We conservatively estimate sizes of the ionised regions required to reconcile the relatively low Lyman-α velocity offsets (ΔvLyα < 300 km s−1) with moderately high Lyman-α escape fractions (fesc, Lyα > 5%) observed in our sample of LAEs, suggesting the presence of ionised hydrogen along the line of sight towards at least eight out of 17 LAEs. We find minimum physical ‘bubble’ sizes of the order of Rion ∼ 0.1–1 pMpc are required in a patchy reionisation scenario where ionised bubbles containing the LAEs are embedded in a fully neutral IGM. Around half of the LAEs in our sample are found to coincide with large-scale galaxy overdensities seen in FRESCO at z ∼ 5.8–5.9 and z ∼ 7.3, suggesting Lyman-α transmission is strongly enhanced in such overdense regions, and underlining the importance of LAEs as tracers of the first large-scale ionised bubbles. Considering only spectroscopically confirmed galaxies, we find our sample of UV-faint LAEs (MUV ≳ −20 mag) and their direct neighbours are generally not able to produce the required ionised regions based on the Lyman-α transmission properties, suggesting lower-luminosity sources likely play an important role in carving out these bubbles. These observations demonstrate the combined power of JWST multi-object and slitless spectroscopy in acquiring a unique view of the early Universe during cosmic reionisation via the most distant LAEs.

Meet in the air: Distributed neighbor discovery in 3D networks with directional transceivers

We formulate and analyze a generic neighbor discovery problem in three-dimensional wireless networks with directional transceivers, where any pair of neighbor nodes in 3D Euclidean space need to steer their transceivers towards each other simultaneously to discover each other. This problem, termed as 3D directional neighbor discovery, arises in a variety of emerging networked systems such as flying ad hoc networks composed of drones and distributed space systems composed of spacecrafts. Compared to the omni-directional 2D neighbor discovery problem extensively investigated in the literature, the 3D directional neighbor discovery problem is intuitively more challenging. Motivated by this observation, we establish an algorithmic framework on the 3D directional neighbor discovery problem. We first mathematically formulate the problem and derive the worst-case discovery delay of any neighbor discovery algorithm. Guided by the performance limit, we then design distributed neighbor discovery algorithms achieving bounded and minimal worst-case discovery delay. We further demonstrate how our algorithmic framework can be generalized to solve generic multi-dimensional rendezvous problems. Extensive numerical analysis is then presented to empirically evaluate of our neighbor discovery algorithms.

On Maximizing the Probability of Achieving Deadlines in Communication Networks

We consider the problem of meeting deadline constraints in wireless communication networks. Fulfilling deadlines depends heavily on the routing algorithm used. We study this dependence generically for a broad class of routing algorithms. For analyzing the impact of routing decisions on deadline fulfillment, we adopt a stochastic model from operations research to capture the source-to-destination delay distribution and the corresponding probability of successfully delivering data before a given deadline. Based on this model, we propose a decentralized algorithm that operates locally at each node and exchanges information solely with direct neighbors in order to determine the probabilities of achieving deadlines. A modified version of the algorithm also improves routing tables iteratively to progressively increase the deadline achievement probabilities. This modified algorithm is shown to deliver routing tables that maximize the deadline achievement probabilities for all nodes in a given network. We tested the approach by simulation and compared it with routing strategies based on established metrics, specifically the average delay, minimum hop count, and expected transmission count. Our evaluations encompass different channel quality and small-scale fading conditions, as well as various traffic load scenarios. Notably, our solution consistently outperforms the other approaches in all tested scenarios.