The Dominant Nodes Index in Shortest Paths (DNISP) as A Local Measure of Source Looking for the Target in Complex Networks

<p>When there are multiple alternate shortest paths between any two nodes in a complex network, there is a need to know details about the content of the paths and the dominance of the nodes within it, this need comes to maximize, control and speed of the information diffusion. This paper discusses the creation of a new special measure as a local scale for any X node in the network. This measure will give each neighbour of the node X a domination value to access the rest of the network, in other words any nodes included in the shortest path (X,Y) will be given a control value, taking into account the existence of more than one shortest path between (X,Y). Such a measure is called a DNISP, which stands for Dominant Nodes Index in Shortest Paths. The X-node needs to examine all shortest paths that connect it with any other nodes across the ones that are directly associated with it. This measure provides an insight into how information flows between nodes according to dominant values with each node.</p>

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THE OPTIMUM OF SYNCHRONIZABILITY IN COMPLEX NETWORKS

International Journal of Modern Physics C ◽

10.1142/s0129183110015816 ◽

2010 ◽

Vol 21 (10) ◽

pp. 1255-1261 ◽

Cited By ~ 4

Author(s):

PEI-JIE MA ◽

BING-HONG WANG

Keyword(s):

Complex Networks ◽

Complex Network ◽

Coupling Strength ◽

Laplacian Matrix ◽

Weight Matrix ◽

Exact Form ◽

Eigenvalues And Eigenvectors ◽

Optimal Network ◽

The One ◽

Insight Into

In this brief report, we investigate the synchronizability of the complex network. In order to optimize the synchronizability, we propose a method by introducing a weight matrix, which makes the synchronized states stable for the widest range of the overall coupling strength. We give a proof in mathematics and gain the exact form of the weight matrix, which is equal to Lβ. Matrix L is the one that describes the optimal network and matrix β is constructed by the eigenvalues and eigenvectors of the usual Laplacian matrix. This result may provide us insight into the synchronization of complex network more deeply.

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Geometrical congruence and efficient greedy navigability of complex networks

10.21203/rs.3.rs-40458/v1 ◽

2020 ◽

Author(s):

Carlo Cannistraci ◽

Alessandro Muscoloni

Keyword(s):

Complex Networks ◽

Complex Network ◽

Hyperbolic Space ◽

Power Law ◽

Shortest Paths ◽

Network Measure ◽

Power Law Exponent ◽

Current Belief ◽

Networks Analysis ◽

Structural Brain

Abstract Hyperbolic networks are supposed to be congruent with their underlying latent geometry and following geodesics in the hyperbolic space is believed equivalent to navigate through topological shortest paths (TSP). This assumption of geometrical congruence is considered the reason for nearly maximally efficient greedy navigation of hyperbolic networks. Here, we propose a complex network measure termed geometrical congruence (GC) and we show that there might exist different TSP, whose projections (pTSP) in the hyperbolic space largely diverge, and significantly differ from the respective geodesics. We discover that, contrary to current belief, hyperbolic networks do not demonstrate in general geometrical congruence and efficient navigability which, in networks generated with nPSO model, seem to emerge only for power-law exponent close to 2. We conclude by showing that GC measure can impact also real networks analysis, indeed it significantly changes in structural brain connectomes grouped by gender or age.

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Information on evolutionary age in redundancy of complex network

Modern Physics Letters B ◽

10.1142/s0217984919503317 ◽

2019 ◽

Vol 33 (27) ◽

pp. 1950331

Author(s):

Shiguo Deng ◽

Henggang Ren ◽

Tongfeng Weng ◽

Changgui Gu ◽

Huijie Yang

Keyword(s):

Principal Component Analysis ◽

Complex Networks ◽

Metabolic Network ◽

Complex Network ◽

Cellular Networks ◽

Topological Structure ◽

Quantitative Measure ◽

Principal Component ◽

Evolutionary Information ◽

Local Patterns

Evolutionary processes of many complex networks in reality are dominated by duplication and divergence. This mechanism leads to redundant structures, i.e. some nodes share most of their neighbors and some local patterns are similar, called redundancy of network. An interesting reverse problem is to discover evolutionary information from the present topological structure. We propose a quantitative measure of redundancy of network from the perspective of principal component analysis. The redundancy of a community in the empirical human metabolic network is negatively and closely related with its evolutionary age, which is consistent with that for the communities in the modeling protein–protein network. This behavior can be used to find the evolutionary difference stored in cellular networks.

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CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01362-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sima Ranjbari ◽

Toktam Khatibi ◽

Ahmad Vosough Dizaji ◽

Hesamoddin Sajadi ◽

Mehdi Totonchi ◽

...

Keyword(s):

Complex Networks ◽

Complex Network ◽

Semen Quality ◽

Intrauterine Insemination ◽

Machine Learning Algorithms ◽

Semen Parameters ◽

Feature Engineering ◽

Operating Characteristics ◽

Scoring Method ◽

Novel Approach

Abstract Background Intrauterine Insemination (IUI) outcome prediction is a challenging issue which the assisted reproductive technology (ART) practitioners are dealing with. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. Many previous studies have been focused on predicting the in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) outcome using machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to propose an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking the most significant features. Methods For this purpose, a novel approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute describing 11,255 IUI treatment cycles for 8,360 couples. Our dataset includes the couples' demographic characteristics, historical data about the patients' diseases, the clinical diagnosis, the treatment plans and the prescribed drugs during the cycles, semen quality, laboratory tests and the clinical pregnancy outcome. Results Experimental results show that the proposed method outperforms the compared methods with Area under receiver operating characteristics curve (AUC) of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female body mass index (BMI).

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Computing nearest neighbour interchange distances between ranked phylogenetic trees

Journal of Mathematical Biology ◽

10.1007/s00285-021-01567-5 ◽

2021 ◽

Vol 82 (1-2) ◽

Author(s):

Lena Collienne ◽

Alex Gavryushkin

Keyword(s):

Cancer Research ◽

Computational Complexity ◽

Phylogenetic Tree ◽

Shortest Path ◽

Phylogenetic Trees ◽

Shortest Paths ◽

Nearest Neighbour ◽

Tree Inference ◽

Subtree Prune And Regraft ◽

Comparison Algorithms

AbstractMany popular algorithms for searching the space of leaf-labelled (phylogenetic) trees are based on tree rearrangement operations. Under any such operation, the problem is reduced to searching a graph where vertices are trees and (undirected) edges are given by pairs of trees connected by one rearrangement operation (sometimes called a move). Most popular are the classical nearest neighbour interchange, subtree prune and regraft, and tree bisection and reconnection moves. The problem of computing distances, however, is $${\mathbf {N}}{\mathbf {P}}$$ N P -hard in each of these graphs, making tree inference and comparison algorithms challenging to design in practice. Although anked phylogenetic trees are one of the central objects of interest in applications such as cancer research, immunology, and epidemiology, the computational complexity of the shortest path problem for these trees remained unsolved for decades. In this paper, we settle this problem for the ranked nearest neighbour interchange operation by establishing that the complexity depends on the weight difference between the two types of tree rearrangements (rank moves and edge moves), and varies from quadratic, which is the lowest possible complexity for this problem, to $${\mathbf {N}}{\mathbf {P}}$$ N P -hard, which is the highest. In particular, our result provides the first example of a phylogenetic tree rearrangement operation for which shortest paths, and hence the distance, can be computed efficiently. Specifically, our algorithm scales to trees with tens of thousands of leaves (and likely hundreds of thousands if implemented efficiently).

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Dynamic Shortest Paths Methods for the Time-Dependent TSP

Algorithms ◽

10.3390/a14010021 ◽

2021 ◽

Vol 14 (1) ◽

pp. 21

Author(s):

Christoph Hansknecht ◽

Imke Joormann ◽

Sebastian Stiller

Keyword(s):

Column Generation ◽

Traveling Salesman Problem ◽

Shortest Path ◽

Valid Inequalities ◽

Shortest Paths ◽

Traveling Salesman ◽

Time Dependent ◽

Full Generality ◽

Branching Rule ◽

The Traveling Salesman Problem

The time-dependent traveling salesman problem (TDTSP) asks for a shortest Hamiltonian tour in a directed graph where (asymmetric) arc-costs depend on the time the arc is entered. With traffic data abundantly available, methods to optimize routes with respect to time-dependent travel times are widely desired. This holds in particular for the traveling salesman problem, which is a corner stone of logistic planning. In this paper, we devise column-generation-based IP methods to solve the TDTSP in full generality, both for arc- and path-based formulations. The algorithmic key is a time-dependent shortest path problem, which arises from the pricing problem of the column generation and is of independent interest—namely, to find paths in a time-expanded graph that are acyclic in the underlying (non-expanded) graph. As this problem is computationally too costly, we price over the set of paths that contain no cycles of length k. In addition, we devise—tailored for the TDTSP—several families of valid inequalities, primal heuristics, a propagation method, and a branching rule. Combining these with the time-dependent shortest path pricing we provide—to our knowledge—the first elaborate method to solve the TDTSP in general and with fully general time-dependence. We also provide for results on complexity and approximability of the TDTSP. In computational experiments on randomly generated instances, we are able to solve the large majority of small instances (20 nodes) to optimality, while closing about two thirds of the remaining gap of the large instances (40 nodes) after one hour of computation.

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Identifying Influential Spreaders in Complex Networks by Considering the Impact of the Number of Shortest Paths

Journal of Systems Science and Complexity ◽

10.1007/s11424-021-0111-7 ◽

2021 ◽

Author(s):

Yangyang Luan ◽

Zhongkui Bao ◽

Haifeng Zhang

Keyword(s):

Complex Networks ◽

Shortest Paths ◽

The Impact

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Topology Identification of Complex Network via Chaotic Ant Swarm Algorithm

Mathematical Problems in Engineering ◽

10.1155/2013/401983 ◽

2013 ◽

Vol 2013 ◽

pp. 1-5 ◽

Cited By ~ 2

Author(s):

Haipeng Peng ◽

Lixiang Li ◽

Jürgen Kurths ◽

Shudong Li ◽

Yixian Yang

Keyword(s):

Complex Networks ◽

Numerical Simulations ◽

Complex Network ◽

Parameter Optimization ◽

Ant Algorithm ◽

Structure Parameters ◽

Topology Identification ◽

Unknown Structure ◽

Swarm Algorithm ◽

Scientific Fields

Nowadays, the topology of complex networks is essential in various fields as engineering, biology, physics, and other scientific fields. We know in some general cases that there may be some unknown structure parameters in a complex network. In order to identify those unknown structure parameters, a topology identification method is proposed based on a chaotic ant swarm algorithm in this paper. The problem of topology identification is converted into that of parameter optimization which can be solved by a chaotic ant algorithm. The proposed method enables us to identify the topology of the synchronization network effectively. Numerical simulations are also provided to show the effectiveness and feasibility of the proposed method.

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