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Nestedness and modularity have been recurrently observed in species interaction networks. Some studies argue that those topologies result from selection against unstable networks, and others propose that they likely emerge from processes driving the interactions between pairs of species. Here we present a model that simulates the evolution of consumer species using resource species following simple rules derived from the integrative hypothesis of specialization (IHS). Without any selection on stability, our model reproduced all commonly observed network topologies. Our simulations demonstrate that resource heterogeneity drives network topology. On the one hand, systems containing only homogeneous resources form generalized nested networks, in which generalist consumers have higher performance on each resource than specialists. On the other hand, heterogeneous systems tend to have a compound topology: modular with internally nested modules, in which generalists that divide their interactions between modules have low performance. Our results demonstrate that all real-world topologies likely emerge through processes driving interactions between pairs of species. Additionally, our simulations suggest that networks containing similar species differ from heterogeneous networks and that modules may not present the topology of entire networks.

In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional routing protocols, such as OSPF or the Dijkstra algorithm, often fall short in handling the complexity, scalability, and dynamic nature of modern network environments, including unmanned aerial vehicle (UAV), satellite, and 5G networks. By leveraging their ability to model network topologies and learn from complex interdependencies between nodes and links, GNNs offer a promising solution for distributed and scalable routing optimization. This paper provides a comprehensive review of the latest research on GNN-based routing methods, categorizing them into supervised learning for network modeling, supervised learning for routing optimization, and reinforcement learning for dynamic routing tasks. We also present a detailed analysis of existing datasets, tools, and benchmarking practices. Key challenges related to scalability, real-world deployment, explainability, and security are discussed, alongside future research directions that involve federated learning, self-supervised learning, and online learning techniques to further enhance GNN applicability. This study serves as the first comprehensive survey of GNNs for routing optimization, aiming to inspire further research and practical applications in future communication networks.

Background Analyses of a bipartite Genotype and Phenotype Network (GPN), linking the genetic variants and phenotypes based on statistical associations, provide an integrative approach to elucidate the complexities of genetic relationships across diseases and identify pleiotropic loci. In this study, we assess contributions to constructing a well-defined GPN with a clear representation of genetic associations by comparing the network properties with a random network, including connectivity, centrality, and community structure. Then, we extend our discussion to include two applications of bipartite GPN in disease heritability enrichment analysis and phenome-wide association studies (PheWAS). Results We construct network topology annotations of genetic variants that quantify the possibility of pleiotropy and apply stratified linkage disequilibrium (LD) score regression to 12 highly genetically correlated phenotypes to identify enriched annotations. The constructed network topology annotations are informative for disease heritability after conditioning on a broad set of functional annotations from the baseline-LD model. In application of PheWAS, the community detection method can be used to obtain a priori grouping of phenotypes detected from GPN based on the shared genetic architecture, then jointly test the association between multiple phenotypes in each network module and one genetic variant to discover the cross-phenotype associations and pleiotropy. Significance thresholds for PheWAS are adjusted for multiple testing by applying the false discovery rate (FDR) control approach. Extensive simulation studies and analyses of 633 electronic health record (EHR)-derived phenotypes in the UK Biobank GWAS summary dataset reveal that most multiple phenotype association tests based on GPN can well-control FDR and identify more significant genetic variants compared with the tests based on UK Biobank categories. Conclusions The construction and integration of the bipartite GPNs enhance our understanding of disease heritability, genetic architecture between phenotypes, and pleiotropy.

Identifying the topology of low-voltage (LV) networks is becoming increasingly important. Having precise and accurate topology information is crucial for future network operations and network modelling. Topology identification approaches based on smart-meter data typically rely on Root Mean Square (RMS) voltage, current, and power measurements, which are limited in accuracy due to factors such as time resolution, measurement intervals, and instrument errors. This paper presents a novel methodology for identifying distribution network topologies through the utilisation of smart-meter harmonic data. The methodology introduces, for the first time, the application of voltage Total Harmonic Distortion (THD) and individual harmonic components (V2–V20) as topology identifiers. The proposed approach leverages the unique properties of harmonic distortion to improve the accuracy of topology identification. This paper first analyses the influential factors affecting topology identification, establishing that harmonic distortion propagation patterns offer superior discrimination compared to RMS voltage. Through systematic investigation, the findings demonstrate the potential of harmonic-based analysis as a more effective alternative for topology identification in modern power distribution systems.

Unmanned Aerial Vehicles (UAVs) have become essential tools across various sectors due to their versatility and advanced capabilities in autonomy, perception, and networking. Despite over a decade of experimental efforts in multi-UAV systems, substantial theoretical challenges concerning coordination mechanisms still need to be solved, particularly in maintaining network connectivity and optimizing routing. Current research has revealed the absence of an efficient algorithm tailored for the routing problem of multiple UAVs connected to a central station, especially under the constraints of maintaining constant network connectivity and minimizing the average goal revisit time. This paper proposes a heuristic routing algorithm for multiple UAV systems to address the return visit challenge in flying ad hoc networks (FANETs) linked to a central station. Our approach introduces a composite valuation function for target prioritization and a mathematical model for task assignment with relay allocation, allowing any UAV to visit various objectives and gain an advantage or incur a cost for each. We exclusively utilized a simulation environment to mimic UAV operations, assessing communication range, connectivity, and routing performance. Extensive simulations demonstrate that our routing algorithm remains efficient in the face of frequent topological alterations in the network, showing robustness against dynamic environments and superior performance compared to existing methods. This paper presents different approaches to efficiently directing UAVs and explains how heuristic algorithms can enhance our understanding and improve current methods for task assignments.

We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and affect memory and computational savings by processing the data on-the-fly as they are acquired. The setup entails observations modeled as stationary graph signals generated by local diffusion dynamics on the unknown network. Moreover, we may have a priori information on the presence or absence of a few edges as in the link prediction problem. The stationarity assumption implies that the observations’ covariance matrix and the so-called graph shift operator (GSO—a matrix encoding the graph topology) commute under mild requirements. This motivates formulating the topology inference task as an inverse problem, whereby one searches for a sparse GSO that is structurally admissible and approximately commutes with the observations’ empirical covariance matrix. For streaming data, said covariance can be updated recursively, and we show online proximal gradient iterations can be brought to bear to efficiently track the time-varying solution of the inverse problem with quantifiable guarantees. Specifically, we derive conditions under which the GSO recovery cost is strongly convex and use this property to prove that the online algorithm converges to within a neighborhood of the optimal time-varying batch solution. Numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network.

Gene regulatory networks (GRNs) provide a transformation function between the static genomic sequence and the primary spatial specification processes operating development. The regulatory information encompassed in developmental GRNs thus goes far beyond the control of individual genes. We here address regulatory information at different levels of network organization, from single node to subcircuit to large-scale GRNs and discuss how regulatory design features such as network architecture, hierarchical organization, and cis-regulatory logic contribute to the developmental function of network circuits. Using specific subcircuits from the sea urchin endomesoderm GRN, for which both circuit design and biological function have been described, we evaluate by Boolean modeling and in silico perturbations the import of given circuit features on developmental function. The examples include subcircuits encoding positive feedback, mutual repression, and coherent feedforward, as well as signaling interaction circuitry. Within the hierarchy of the endomesoderm GRN, these subcircuits are organized in an intertwined and overlapping manner. Thus, we begin to see how regulatory information encoded at individual nodes is integrated at all levels of network organization to control developmental process.

This paper considers the secure distributed control consensus problem of linear multi‐agent systems (MASs) under switching topologies, subject to intermittent sequential scaling attacks, which was compelled to scaling factor, attack frequency, duration and cooperative‐competitive networks. First, the scenario of a fixed topology is considered, and a novel control protocol combined with a logarithmic quantizer and relative state measurements of neighbouring agents is discussed. The sighed graph is utilized to characterize the communication topology determined by the information flow directions and captured by the graph Laplacian matrix. After that, sufficient conditions for effectiveness of the developed control methods in guiding the MASs to secure bipartite leader‐following consensus are constructed. Second, the scenario of switching topologies is considered, and it is derived that the secure bipartite consensus will be achieved if the designed state feedback control protocol with the scaling factor, the attack duration, attack frequency and switching signal are selected properly. At last, to prove the effectiveness of the designed controllers, a simulation example depended on the real‐word actual military is introduced. This paper considers the secure distributed control consensus problem of linear multi‐agent systems under switching topologies, subject to intermittent sequential scaling attacks, which was compelled to scaling factor, attack frequency, duration and cooperative‐competitive networks.

The successful integration of engineered gene circuits into host cells remains a significant challenge in synthetic biology due to circuit–host interactions, such as growth feedback, where the circuit influences cell growth and vice versa. Understanding the dynamics of circuit failures and identifying topologies resilient to growth feedback are crucial for both fundamental and applied research. Utilizing transcriptional regulation circuits with adaptation as a paradigm, we systematically study more than 400 topological structures and uncover various categories of failures. Three dynamical mechanisms of circuit failures are identified: continuous deformation of the response curve, strengthened or induced oscillations, and sudden switching to coexisting attractors. Our extensive computations also uncover a scaling law between a circuit robustness measure and the strength of growth feedback. Despite the negative effects of growth feedback on the majority of circuit topologies, we identify several circuits that maintain optimal performance as designed, a feature important for applications.

The recent development of unmanned aerial vehicle (UAV) technology has shown the possibility of using UAVs in many research and industrial fields. One of them is for UAVs moving in swarms to provide wireless networks in environments where there is no network infrastructure. Although this method has the advantage of being able to provide a network quickly and at a low cost, it may cause scalability problems in multi-hop connectivity and UAV control when trying to cover a large area. Therefore, as more UAVs are used to form drone networks, the problem of efficiently controlling the network topology must be solved. To solve this problem, we propose a topology control system for drone networks, which analyzes relative positions among UAVs within a swarm, then optimizes connectivity among them in perspective of both interference and energy consumption, and finally reshapes a logical structure of drone networks by choosing neighbors per UAV and mapping data flows over them. The most important function in the scheme is the connectivity optimization because it should be adaptively conducted according to the dynamically changing complex network conditions, which includes network characteristics such as user density and UAV characteristics such as power consumption. Since neither a simple mathematical framework nor a network simulation tool for optimization can be a solution, we need to resort to reinforcement learning, specifically DDPG, with which each UAV can adjust its connectivity to other drones. In addition, the proposed system minimizes the learning time by flexibly changing the number of steps used for parameter learning according to the deployment of new UAVs. The performance of the proposed system was verified through simulation experiments and theoretical analysis on various topologies consisting of multiple UAVs.