Ranking the spreading ability of nodes in network core

2015 ◽  
Vol 26 (05) ◽  
pp. 1550059 ◽  
Author(s):  
Xiao-Lei Tong ◽  
Jian-Guo Liu ◽  
Jiang-Pan Wang ◽  
Qiang Guo ◽  
Jing Ni
Keyword(s):  
Network Structure ◽  
Decomposition Method ◽  
Scientific Collaboration ◽  
Closeness Centrality ◽  
Collaboration Network ◽  
Improved Method ◽  
Node Spreading ◽  
Influential Nodes ◽  
The One ◽  
Aviation Network

Ranking nodes by their spreading ability in complex networks is of vital significance to better understand the network structure and more efficiently spread information. The k-shell decomposition method could identify the most influential nodes, namely network core, with the same ks values regardless to their different spreading influence. In this paper, we present an improved method based on the k-shell decomposition method and closeness centrality (CC) to rank the node spreading influence of the network core. Experiment results on the data from the scientific collaboration network and U.S. aviation network show that the accuracy of the presented method could be increased by 31% and 45% than the one obtained by the degree k, 32% and 31% than the one by the betweenness.

Identifying Important Packages of Object-Oriented Software Using Weighted k-Core Decomposition

2014 ◽  
Vol 23 (4) ◽  
pp. 461-476 ◽  
Author(s):  
Weifeng Pan ◽  
Bo Hu ◽  
Bo Jiang ◽  
Bo Xie
Keyword(s):  
Resource Allocation ◽  
Decomposition Method ◽  
Object Oriented ◽  
Closeness Centrality ◽  
The Other ◽  
Software Systems ◽  
Degree Centrality ◽  
Software Network ◽  
Influential Nodes ◽  
Better Than

AbstractIdentifying important entities in software systems has many implications for effective resource allocation. Complex network research opens new opportunities for identifying important entities from software networks. However, the existing methods only focus on identifying important classes. Little work has been done on the identification of important packages. Moreover, the metrics they used to quantify the class importance are only designed for unweighted software networks and cannot fit in with the weighted software networks. To overcome these limitations, in this article, we introduce the weighted k-core decomposition method (Wk-core) to identify the important packages. First, we use a weighted software network to describe packages and their internal dependencies. Second, we use Wk-core to partition a software network into a layered structure. Then, the packages that are denoted by the nodes within the main core are the identified important packages. To evaluate our method, we use a variant of the susceptible–infectious–recovered model to examine the spreading influence of the nodes in six real weighted software networks. The results show that our method can well identify influential nodes, better than other four methods (i.e., original k-core decomposition, degree centrality, closeness centrality, and betweenness centrality methods). Furthermore, we demonstrate our method on two software networks and show that the important packages identified by our method are more meaningful from a software engineering perspective when compared with the other methods.

Node influence identification via resource allocation dynamics

2014 ◽  
Vol 25 (11) ◽  
pp. 1450065 ◽  
Author(s):  
Shu-Jiao Ma ◽  
Zhuo-Ming Ren ◽  
Chun-Ming Ye ◽  
Qiang Guo ◽  
Jian-Guo Liu
Keyword(s):  
Resource Allocation ◽  
Complex Networks ◽  
Network Structure ◽  
Decomposition Method ◽  
Scale Free Network ◽  
Improved Method ◽  
Exponential Form ◽  
Ranking List ◽  
Scale Free ◽  
Free Network

Identifying the node influence in complex networks is an important task to optimally use the network structure and ensure the more efficient spreading in information. In this paper, by taking into account the resource allocation dynamics (RAD) and the k-shell decomposition method, we present an improved method namely RAD to generate the ranking list to evaluate the node influence. First, comparing with the epidemic process results for four real networks, the RAD method could identify the node influence more accurate than the ones generated by the topology-based measures including the degree, k-shell, closeness and the betweenness. Then, a growing scale-free network model with tunable assortative coefficient is introduced to analyze the effect of the assortative coefficient on the accuracy of the RAD method. Finally, the positive correlation is found between the RAD method and the k-shell values which display an exponential form. This work would be helpful for deeply understanding the node influence of a network.

Identifying influential spreaders based on diffusion K-truss decomposition

2018 ◽  
Vol 32 (22) ◽  
pp. 1850238 ◽  
Author(s):  
Li Yang ◽  
Yu-Rong Song ◽  
Guo-Ping Jiang ◽  
Ling-Ling Xia
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Network Structure ◽  
Decomposition Method ◽  
Spreading Behavior ◽  
The Core ◽  
Influential Nodes ◽  
Simulation Results ◽  
The Impact ◽  
Real Complex

Identifying the most influential spreaders is important in optimizing the network structure or disseminating information through networks. Recent study showed that the K-truss decomposition could filter out the nodes that performed a worse spreading behavior in the maximal K-shell subgraph. The spreaders belonging to the maximal K-truss subgraph show better performance compared to previously used importance criteria. However, the accuracy of the K-truss or the K-shell in determining node coreness is largely susceptible to core-like group. In this paper, we propose an improved diffusion K-truss decomposition method by considering both the diffusion and clustering of edges to eliminate the impact of core-like group on identifying influential nodes. To validate the effectiveness of the proposed method, we compare it with five typical methods by carrying out Monte–Carlo simulations over six real complex networks. Simulation results demonstrate that the proposed method can effectively disintegrate the core-like group and accurately identify the influential nodes.

scholarly journals Multi-hop assortativities for network classification

2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
Author(s):  
Leonardo Gutiérrez-Gómez ◽  
Jean-Charles Delvenne
Keyword(s):  
Machine Learning ◽  
Scientific Collaboration ◽  
State Of The Art ◽  
Medical Engineering ◽  
Research Field ◽  
Classification Task ◽  
Collaboration Network ◽  
Structural Patterns ◽  
Art Methods

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.

Scholar2vec: Vector Representation of Scholars for Lifetime Collaborator Prediction

2021 ◽  
Vol 15 (3) ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Feng Xia ◽  
Jian Wu ◽  
Zhiguo Gong ◽  
Hanghang Tong ◽  
...  
Keyword(s):  
Scientific Collaboration ◽  
Early Stage ◽  
Collaboration Network ◽  
Vector Representation ◽  
Network Embedding ◽  
Machine Learning Methods ◽  
Academic Networks ◽  
Special Relationships ◽  
Real World Datasets ◽  
Vector Representations

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining.

Improved influential nodes identification in complex networks

2021 ◽  
pp. 1-9
Author(s):  
Shi Dong ◽  
Wengang Zhou
Keyword(s):  
Theoretical Analysis ◽  
Complex Networks ◽  
Network Structure ◽  
Information Entropy ◽  
Network System ◽  
Weighted Degree ◽  
Identification Methods ◽  
Hybrid Mechanism ◽  
Influential Nodes ◽  
Influential Node

Influential node identification plays an important role in optimizing network structure. Many measures and identification methods are proposed for this purpose. However, the current network system is more complex, the existing methods are difficult to deal with these networks. In this paper, several basic measures are introduced and discussed and we propose an improved influential nodes identification method that adopts the hybrid mechanism of information entropy and weighted degree of edge to improve the accuracy of identification (Hm-shell). Our proposed method is evaluated by comparing with nine algorithms in nine datasets. Theoretical analysis and experimental results on real datasets show that our method outperforms other methods on performance.

scholarly journals Power Quality: Scientific Collaboration Networks and Research Trends

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2067 ◽  
Author(s):  
Francisco Montoya ◽  
Raul Baños ◽  
Alfredo Alcayde ◽  
Maria Montoya ◽  
Francisco Manzano-Agugliaro
Keyword(s):  
Power Quality ◽  
Scientific Collaboration ◽  
Research Field ◽  
Research Trends ◽  
Collaboration Network ◽  
Collaboration Networks ◽  
Different Types ◽  
Voltage Frequency ◽  
Advanced Model ◽  
Proper Operation

Power quality is a research field related to the proper operation of devices and technological equipment in industry, service, and domestic activities. The level of power quality is determined by variations in voltage, frequency, and waveforms with respect to reference values. These variations correspond to different types of disturbances, including power fluctuations, interruptions, and transients. Several studies have been focused on analysing power quality issues. However, there is a lack of studies on the analysis of both the trending topics and the scientific collaboration network underlying the field of power quality. To address these aspects, an advanced model is used to retrieve data from publications related to power quality and analyse this information using a graph visualisation software and statistical tools. The results suggest that research interests are mainly focused on the analysis of power quality problems and mitigation techniques. Furthermore, they are observed important collaboration networks between researchers within and across countries.

scholarly journals An Improved Method for Discerning Broken Rotor Bar Fault and Load Oscillation in Induction Motors

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3130 ◽  
Author(s):  
Liling Sun ◽  
Boqiang Xu
Keyword(s):  
Spectral Analysis ◽  
Theoretical Basis ◽  
Induction Motors ◽  
The Other ◽  
Improved Method ◽  
Broken Rotor Bar ◽  
Signal Parameters ◽  
Analysis Technique ◽  
Novel Strategy ◽  
The One

A few methods for discerning broken rotor bar (BRB) fault and load oscillation in induction motors have been reported in the literature. However, they all perhaps inevitably fail in adverse cases in which these two phenomena are simultaneously present. To tackle this problem, an improved method for discerning BRB fault and load oscillation is proposed in this paper based on the following work. On the one hand, the theoretical basis is analytically extended to include such an adverse case, yielding some important findings on the spectra of the instantaneous reactive and active powers. A novel strategy is thus outlined to correctly discern BRB fault and load oscillation even when simultaneously present. On the other hand, Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) is adopted as the spectral analysis technique to deal with the instantaneous reactive and active powers, yielding a certain improvement compared to the existing methods, adopting Fast Fourier Transform (FFT). Simulation and experimental results demonstrate that the proposed method can correctly discern BRB fault and load oscillation even when simultaneously present.

scholarly journals Scientific collaboration network ammong Brazilian universities

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Maria Isabel Escalona-Fernandez ◽  
Antonio Pulgarin-Guerrero ◽  
Ely Francina Tannuri de Oliveira ◽  
Maria Cláudia Cabrini Gracio
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Scientific Collaboration ◽  
Methodological Approach ◽  
Scientific Production ◽  
Collaboration Network ◽  
Network Data ◽  
New Knowledge

This paper analyses the scientific collaboration network formed by the Brazilian universities that investigate in dentistry area. The constructed network is based on the published documents in the Scopus (Elsevier) database covering a period of 10 (ten) years. It is used social network analysis as the best methodological approach to visualize the capacity for collaboration, dissemination and transmission of new knowledge among universities. Cohesion and density of the collaboration network is analyzed, as well as the centrality of the universities as key-actors and the occurrence of subgroups within the network. Data were analyzed using the software UCINET and NetDraw. The number of documents published by each university was used as an indicator of its scientific production.

scholarly journals Arcabouço de Comunicação Publish/Subscribe para a Coleta de Dados de Aplicações Médicas

2021 ◽  
Author(s):  
Gabriele Ribeiro ◽  
Joberto S. B. Martins
Keyword(s):  
Network Structure ◽  
Medical Data ◽  
Medical Applications ◽  
Distributed Environment ◽  
Communication Framework ◽  
Production And Consumption ◽  
Basic Set ◽  
Network Communications ◽  
The One ◽  
Health Application

Medical applications are increasingly using computing resources such as IoT sensors and network communications paradigms. An e-Health application requires a basic set of elements such as sensors, a communication framework, and a network structure adapted to the application's specific requirements. This work expands and develops a framework based on the Publish / Subscribe paradigm to develop PSIoT-Health. The PSIoT-Health framework focuses on medical applications that collect data produced in a distributed manner. The PSIoT-Health adapts the Pub/Sub model to the requirements of medical applications and proposes a solution for the production and consumption of data between producers and consumers of medical data in a distributed environment such as the one existing in a smart city.

Sign in / Sign up

Export Citation Format

Share Document