Node Deletion Research Articles

Putative Drug Target Identification in Tinea Causing Pathogen Trichophyton rubrum Using Subtractive Proteomics Approach.

Trichophyton, important among the three keratinophylic fungi grouped as dermatophytes, is known to cause superficial infections in skin, nail and hair of all the living organisms. The side effects produced by the drugs currently administered to counter these infections have necessitated the search for novel targets. The present study focused on finding putative drug targets in Trichophyton rubrum using the subtractive proteomics approach where its whole proteome was analyzed to find proteins non-homologous to humans inclusive of their gut flora and human protein domain but essential to T. rubrum, to identify sub-cellular localization, functional classification of uncharacterized proteins and to analyze the protein network, druggability and pathway of the targets. The study's strength relies on its addition of important steps namely, non-homology of the pathogen domain to human domain, non-homology to gut microbiota and substantiation of the importance of the targets in networking by node deletion to the existing methods in drug discovery for dermatophytoses. The study has resulted in the identification of two novel drug targets from the whole proteome of T. rubrum that are not present in human and human gut microbiota.

Approximation algorithm for (connected) bounded-degree deletion problem on unit disk graphs

In this paper, we study the minimum (connected) k-bounded-degree node deletion problem (Min(C)kBDND). For a connected graph G, a constant k and a weight function w:V→R+, a vertex set C⊆V(G) is a kBDND-set if the maximum degree of graph G−C is at most k. If furthermore, the subgraph of G induced by C is connected, then C is a CkBDND-set. The goal of MinWkBDND (resp. MinWCkBDND) is to find a kBDND-set (resp. CkBDND-set) with the minimum weight. In this paper, we focus on their cardinality versions with w(v)≡1,v∈V, which are denoted as MinkBDND and MinCkBDND. This paper presents a (1+ε) and a 3.76-approximation algorithm for MinkBDND and MinCkBDND on unit disk graphs, respectively, where 0<ε<1 is an arbitrary constant.

Analysis of the convergence of the degree distribution of contracting random networks towards a Poisson distribution using the relative entropy.

We present analytical results for the structural evolution of random networks undergoing contraction processes via generic node deletion scenarios, namely, random deletion, preferential deletion, and propagating deletion. Focusing on configuration model networks, which exhibit a given degree distribution P_{0}(k) and no correlations, we show using a rigorous argument that upon contraction the degree distributions of these networks converge towards a Poisson distribution. To this end, we use the relative entropy S_{t}=S[P_{t}(k)||π(k|〈K〉_{t})] of the degree distribution P_{t}(k) of the contracting network at time t with respect to the corresponding Poisson distribution π(k|〈K〉_{t}) with the same mean degree 〈K〉_{t} as a distance measure between P_{t}(k) and Poisson. The relative entropy is suitable as a distance measure since it satisfies S_{t}≥0 for any degree distribution P_{t}(k), while equality is obtained only for P_{t}(k)=π(k|〈K〉_{t}). We derive an equation for the time derivative dS_{t}/dt during network contraction and show that the relative entropy decreases monotonically to zero during the contraction process. We thus conclude that the degree distributions of contracting configuration model networks converge towards a Poisson distribution. Since the contracting networks remain uncorrelated, this means that their structures converge towards an Erdős-Rényi (ER) graph structure, substantiating earlier results obtained using direct integration of the master equation and computer simulations [Tishby etal., Phys. Rev. E 100, 032314 (2019)2470-004510.1103/PhysRevE.100.032314]. We demonstrate the convergence for configuration model networks with degenerate degree distributions (random regular graphs), exponential degree distributions, and power-law degree distributions (scale-free networks).

Complex Network Analysis of VANET Topology With Realistic Vehicular Traces

Recent years have witnessed growing interests in vehicular ad hoc network (VANET) topology. Existing works assume that the networks generated at each time are independent. In these works, the VANET topologies are formed based on simple traffic and communication models such as unit disk graph model. Interestingly, some works find that VANETs are scale-free ones characterized by strong connectivity and survivability, while some argue that they are not. This study analyzes VANETs in more realistic settings, trying to find the answer to the paradox. Specifically, in this paper we propose a Dynamically Evolving Networking (DEN) model and take as input realistic vehicular traces to make the research and results more practical. We consider the effect of node addition, node deletion and link loss due to node mobility and keep the network evolving by including preferential attachment and link compensation mechanisms. We find that the evolved VANET exhibits a scale-invariant feature under certain conditions. We also find that the emergence of such phenomenon has no relation to communication range if the range is large. Furthermore, we apply complex network theory to capture the dynamics of the VANET. Theoretically for the first time we show that VANET would evolve into the scale-free topology when the probability of node addition is relatively large or the probability of link compensation is properly set, the presence of which would help establish a strongly connected VANET topology.

Dynamic Node Embeddings From Edge Streams

Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Such temporal networks (or edge streams) consist of a sequence of timestamped edges and are seemingly ubiquitous. Despite the importance of accurately modeling the temporal information, most embedding methods ignore it entirely or approximate the temporal network using a sequence of static snapshot graphs. In this work, we propose using the notion of temporal walks for learning dynamic embeddings from temporal networks. Temporal walks capture the temporally valid interactions (e.g., flow of information, spread of disease) in the dynamic network in a lossless fashion. Based on the notion of temporal walks, we describe a general class of embeddings called continuous-time dynamic network embeddings (CTDNEs) that completely avoid the issues and problems that arise when approximating the temporal network as a sequence of static snapshot graphs. Unlike previous work, CTDNEs learn dynamic node embeddings directly from the temporal network at the finest temporal granularity and thus use only temporally valid information. As such CTDNEs naturally support online learning of the node embeddings in a streaming real-time fashion. Finally, the experiments demonstrate the effectiveness of this class of embedding methods that leverage temporal walks as it achieves an average gain in AUC of $11.9\%$ across all methods and graphs.

Limitations of Blockchain Technology with its Applications.

Block chain is also called as chain of blocks i.e. it is collection of technologies or collection of Technologies or a bit like a block chain sack of Logo From the sack y can collect different bricks and arrange them some particular way/different way to create a chain blocks .It is decentralized i.e. no any central control or authority assigned to it .It is simple. Easy to understand and work the system. New blocks are added at the end of the block chain .It uses data structure-link list-also called as linear data structure, stored at contiguous location in work on concept of pointers. They form a chain in continuous way using array ,doubly link list-pointers are used for fast and speedy execution ,it traverse in forward direction –move ahead, backward direction-move back, operations like addition &amp; deletion of node can be efficiently done

Graph topology resulting from addition and deletion of nodes determined by random walk

How the power-law form of the degree distribution of networks can survive in the equilibrium state of number of nodes is an open question, since the deletion of nodes seriously affects the degree distribution. In this paper, we introduce a network evolution rule based on a random walk to describe the replacement of nodes in networks. Numerical results show that model assumptions support the power-law form of degree distribution even when the replacement of vertices achieves an equilibrium state of the number of vertices, and the distribution is described by the distribution of the maximum degree that each node experiences in its life. We showed additionally that the reduction of the addition probability of nodes causes an edge condensation to hub nodes. A novel feature of the model is that the visit frequency and recurrence property of the walker to a vertex determines the degree and lifetime of the vertex in an equilibrium state of the number of vertices.

Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Identifying important nodes is very crucial to design efficient communication networks or contain the spreading of information such as diseases and rumors. The problem is formulated as follows: given a network, which nodes are the more important? Most current studies did not incorporate the structure change as well as application features of a network. Aiming at the node importance evaluation in wireless sensor networks, a new method which ranks nodes according to their structural importance and performance impact is proposed. Namely, this method considers two aspects of the network, network structural characteristics and application requirements. This method integrates four indicators which reflect the node importance, namely, node degree, number of spanning trees, delay, and network energy consumption. Firstly, the changes in the four indicators are analyzed using the node deletion method. Then, the TOPSIS multi-attribute decision-making method is applied to merge these four evaluation indicators. On this basis, a more comprehensive evaluation method (MADME) for node importance is obtained. Theory study reveals MADME method saves computational time. And the simulation results show the superiority of the MADME method over various algorithms such as the N-Burt method, betweenness method, DEL-Node method, and IE-Matrix method. The accuracy of the evaluation can be improved, and the key nodes determined by the MADME method have a more obvious effect on the network performance. Our method can provide guidance on influential node identification in the network.

Learning the Graph Edit Costs Based on a Learning Model Applied to Sub-optimal Graph Matching

Attributed graphs are used to represent patterns composed of several parts in pattern recognition. The nature of these patterns can be diverse, from images, to handwritten characters, maps or fingerprints. Graph edit distance has become an important tool in structural pattern recognition since it allows us to measure the dissimilarity of attributed graphs. It is based on transforming one graph into another through some edit operations such as substitution, deletion and insertion of nodes and edges. It has two main constraints: it requires an adequate definition of the costs of these operations and its computation cost is exponential with regard to the number of nodes. In this paper, we first present a general framework to automatically learn these edit costs considering graph edit distance is computed in a sub-optima way. Then, we specify this framework in two different models based on neural networks and probability density functions. An exhaustive practical validation on 14 public databases, which have different features such as the size of the graphs, the number of attributes or the number of graphs per class have been performed. This validation shows that with the learned edit costs, the accuracy is higher than with some manually imposed costs or other costs automatically learned by previous methods.

Convergence towards an Erdős-Rényi graph structure in network contraction processes.

In a highly influential paper twenty years ago, Barabási and Albert [Science 286, 509 (1999)SCIEAS0036-807510.1126/science.286.5439.509] showed that networks undergoing generic growth processes with preferential attachment evolve towards scale-free structures. In any finite system, the growth eventually stalls and is likely to be followed by a phase of network contraction due to node failures, attacks, or epidemics. Using the master equation formulation and computer simulations, we analyze the structural evolution of networks subjected to contraction processes via random, preferential, and propagating node deletions. We show that the contracting networks converge towards an Erdős-Rényi network structure whose mean degree continues to decrease as the contraction proceeds. This is manifested by the convergence of the degree distribution towards a Poisson distribution and the loss of degree-degree correlations.

Distributed graph queries over models@run.time for runtime monitoring of cyber-physical systems

Smart cyber-physical systems (CPSs) have complex interaction with their environment which is rarely known in advance, and they heavily depend on intelligent data processing carried out over a heterogeneous and distributed computation platform with resource-constrained devices to monitor, manage and control autonomous behavior. First, we propose a distributed runtime model to capture the operational state and the context information of a smart CPS using directed, typed and attributed graphs as high-level knowledge representation. The runtime model is distributed among the participating nodes, and it is consistently kept up to date in a continuously evolving environment by a time-triggered model management protocol. Our runtime models offer a (domain-specific) model query and manipulation interface over the reliable communication middleware of the Data Distribution Service (DDS) standard widely used in the CPS domain. Then, we propose to carry out distributed runtime monitoring by capturing critical properties of interest in the form of graph queries, and design a distributed graph query evaluation algorithm for evaluating such graph queries over the distributed runtime model. As the key innovation, our (1) distributed runtime model extends existing publish–subscribe middleware (like DDS) used in real-time CPS applications by enabling the dynamic creation and deletion of graph nodes (without compile time limits). Moreover, (2) our distributed query evaluation extends existing graph query techniques by enabling query evaluation in a real-time, resource-constrained environment while still providing scalable performance. Our approach is illustrated, and an initial scalability evaluation is carried out on the MoDeS3 CPS demonstrator and the open Train Benchmark for graph queries.

Experimental Profiling of Temperature and Luminosity inside Greenhouse using Wireless Sensor Network

Monitoring and control of climatic variables inside a greenhouse (GH) play crucial role in quality production of crops in the green-house (GH). Measurement and monitoring of major GH variables, such as temperature, humidity, soil moisture, light intensity and CO2 in atmosphere, has been generally carried out using a central data acquisition system to which all sensors are individually wired. This technique involves huge cabling and lacks flexibility of adding sensors in future. This paper presents a detailed methodology and results of experimental measurement and profiling (both temporal and spatial) of temperature and luminosity inside a laboratory model of GH using a wireless sensor network (WSN). For the experimental work, the GH was divided into two rows, row 1 and row 2, with three zones of equal dimensions in each row. At the centre of each zone one wireless sensor node was placed for measurement and acquisition of temperature and luminosity in that zone. A WSN was created with these six wireless sensor (WS) nodes (or motes) and one network coordinator (NC). Profiling of temperature and luminosity was carried out under uncontrolled as well as controlled environmental conditions. Two controlled conditions were created. In the first case only temperature was controlled by placing a heater at the centre of GH. The second case involves simultaneous control of temperature and luminosity, for which an incandescent lamp was placed in each zone of row 1 while all the zones of row 2 were covered with a tinted plastic sheet. Data was gathered from all the six WS nodes in each controlled case as well as in uncontrolled environment. Surface profiles of both temperature and luminosity were drawn in Lab VIEW on the basis of collaborative processing of the data. The experimental results so obtained are correlated with the expected theoretical profiles. The use of WSN and Lab VIEW for collecting and processing the data, respectively, provides remarkable flexibility in terms of addition and deletion of WS nodes. Moreover, no wiring is needed for power or signals.

A subset of topologically associating domains fold into mesoscale core-periphery networks

Mammalian genomes are folded into a hierarchy of compartments, topologically associating domains (TADs), subTADs, and long-range looping interactions. The higher-order folding patterns of chromatin contacts within TADs and how they localize to disease-associated single nucleotide variants (daSNVs) remains an open area of investigation. Here, we analyze high-resolution Hi-C data with graph theory to understand possible mesoscale network architecture within chromatin domains. We identify a subset of TADs exhibiting strong core-periphery mesoscale structure in embryonic stem cells, neural progenitor cells, and cortical neurons. Hyper-connected core nodes co-localize with genomic segments engaged in multiple looping interactions and enriched for occupancy of the architectural protein CCCTC binding protein (CTCF). CTCF knockdown and in silico deletion of CTCF-bound core nodes disrupts core-periphery structure, whereas in silico mutation of cell type-specific enhancer or gene nodes has a negligible effect. Importantly, neuropsychiatric daSNVs are significantly more likely to localize with TADs folded into core-periphery networks compared to domains devoid of such structure. Together, our results reveal that a subset of TADs encompasses looping interactions connected into a core-periphery mesoscale network. We hypothesize that daSNVs in the periphery of genome folding networks might preserve global nuclear architecture but cause local topological and functional disruptions contributing to human disease. By contrast, daSNVs co-localized with hyper-connected core nodes might cause severe topological and functional disruptions. Overall, these findings shed new light into the mesoscale network structure of fine scale genome folding within chromatin domains and its link to common genetic variants in human disease.

Variational h-adaption method for the phase field approach to fracture

The phase field approach to fracture is efficient in simulating crack nucleation and propagation but requires a very fine mesh to resolve the regularization length scale. To alleviate such difficulty we have developed a variational h-adaption method for this approach based on Mosler and Ortiz (Int J Numer Methods Eng 72(5):505–523, 2007, Int J Numer Methods Eng 77(3):437–450, 2009), which consists of a mesh refinement scheme based on edge bisection and a mesh coarsening scheme via node deletion. The main advantages are that a single criterion can be used for the adaption process without any undetermined constant as in a residual-based method and that there is no need to weigh the importance of the two coupled fields, as would be needed for a recovery-based method. We showed examples to demonstrate the efficiency of the proposed method and compared the performance of two bisection operations: simple edge bisection and the modified backward longest edge propagation path scheme. In particular, these two methods lead to condition numbers of the coefficient matrices on the same order of magnitude.

Lymphatic Endothelial Cells Are Essential Components of the Subcapsular Sinus Macrophage Niche

In lymph nodes, subcapsular sinus macrophages (SSMs) form an immunological barrier that monitors lymph drained from peripheral tissues. Upon infection, SSMs activate B and natural killer T(NKT) cells while secreting inflammatory mediators. Here, we investigated the mechanisms regulating development and homeostasis of SSMs. Embryonic SSMs originated from yolk sac hematopoiesis and were replaced by a postnatal wave of bone marrow (BM)-derived monocytes that proliferated to establish the adult SSM network. The SSM network self-maintained by proliferation with minimal BM contribution. Upon pathogen-induced transient deletion, BM-derived cells contributed to restoring the SSM network. Lymphatic endothelial cells (LECs) were the main source of CSF-1 within the lymph node and conditional deletion of Csf1 in adult LECs decreased the network of SSMs and medullary sinus macrophages (MSMs). Thus, SSMs have a dual hematopoietic origin, and LECs are essential to the niche supporting these macrophages.

Effective and scalable methods for graph protection strategies against epidemics on dynamic networks

Dynamic networks are networks with temporal relationship features which evolve over time by the inclusion and deletion of nodes and edges. Suppressing the epidemic spreading in such networks is quite challenging. The problem of protecting a limited number of nodes to restrain the spreading of malicious attacks or dangerous rumor in the networks is called graph protection problem. However, most of existing strategies only consider to protect at once regardless the evolving network structure and incoming attacks over time, i.e., these strategies either pre-protect important nodes before the epidemic starts or post-allocate the protection while the attacks have already spread over the network. In this paper, we introduce multiple-turns protection strategies, which divide the size of protection budget into several turns and protect nodes according to the currently observed temporal snapshot of dynamic networks. We construct a minimum vertex cover of the input network efficiently using reinforcement learning approach. To capture the state of the input network, a feature-based representation of each node is constructed using a graph embedding technique. Experimental evaluations show that our proposed methods, namely ReProtect and ReProtect-p effectively restrain epidemic propagation in synthetic and real-world network datasets. By protecting about 15% of nodes, our methods can obtain up to 84% of surviving nodes and outperform other baseline methods on two popular epidemic models: SIS and SIR.

Succinct data structure for dynamic trees with faster queries

Navarro and Sadakane [19] gave a dynamic succinct data structure for storing an ordinal tree. The structure supports tree queries in either O(log⁡n/log⁡log⁡n) or O(log⁡n) time, and insertion or deletion of a single node in O(log⁡n) time. In this paper we improve the result of Navarro and Sadakane by reducing the time complexities of some queries (e.g. degree and level_ancestor) from O(log⁡n) to O(log⁡n/log⁡log⁡n).

Graph edit distance: Restrictions to be a metric

In the presentation of the graph edit distance in 1983 and other newer bibliography, authors state that it is necessary to apply the distance restrictions (non-negativity, identity of indiscernible elements, symmetry and triangle inequality) to each of the edit functions (insertion, deletion and substitution of nodes and edges) involved in the process of computing the graph edit distance to make the graph edit distance a true distance. Moreover, graph edit distance algorithms presented in the last three decades have been based on mapping the edit path that transforms a graph into the other one into a bijection of the graphs in which some null nodes have been added. In this paper, we show that the triangle inequality does not need to be imposed in each edit function if the graph edit distance is defined through an edit path; however, it is necessary if it is defined as a graph bijection. This is an important finding since the triangle inequality is the only restriction that relates different edit functions and concerns the process of tuning the edit functions given a specific application. Hence, on one hand, it would encourage research to define new algorithms based on the edit path instead of the graph bijection and, on the other hand, use edit functions without the restriction, for instance, that the sum of the costs of insertion and deletion of a pair of nodes has to be larger or equal than the cost of substituting them, which could increase the recognition ratio of a concrete application.

Identification of Key Nodes in Aircraft State Network Based on Complex Network Theory

With the development of aviation, the air traffic density in the terminal area is high and the traffic situation is relatively complex, which brings challenges to the flight deployment. In order to fully understand the air flight situation and provide decision-making basis for controllers, this paper proposes a key conflict aircraft identification method based on complex network theory and node deletion method. First, an aircraft state network is constructed with an aircraft as nodes and airborne collision avoidance system (ACAS) communication relations as edges. Network efficiency, network robustness, connection density, and largest component were used as the indexes of network performance. The weight of each index is determined by using AHP-entropy weight method. A multi-attribute decision-making method was introduced to quantify network performance. Then we used a node deletion method to determine key conflict aircrafts. The simulation and experiment are respectively carried out on the artificial network and the aircraft state network of a certain day in the terminal area of Kunming Changshui Airport. The results show that the method proposed in this paper can identify the key conflict points in the aircraft state network. The deployment of selected nodes can not only effectively reduce the complexity of the flight state network, but also provide a reference for air traffic control services and reduce the control difficulty of the controller.

D-Chain Tomography of Networks: A New Structure Spectrum and an Application to the SIR Process

The analysis of the dynamics on complex networks is closely connected to structural features of the networks. Features like, for instance, graph-cores and node degrees have been studied ubiquitously. Here we introduce the D-spectrum of a network, a novel new framework that is based on a collection of nested chains of subgraphs within the network. Graph-cores and node degrees are merely from two particular such chains of the D-spectrum. Each chain gives rise to a ranking of nodes and, for a fixed node, the collection of these ranks provides us with the D-spectrum of the node. Besides a node deletion algorithm, we discover a connection between the D-spectrum of a network and some fixed points of certain graph dynamical systems (MC systems) on the network. Using the D-spectrum we identify nodes of similar spreading power in the susceptible-infectious-recovered (SIR) model on a collection of real world networks as a quick application. We then discuss our results and conclude that D-spectra represent a meaningful augmentation of graph-cores and node degrees.