SLGC: Identifying influential nodes in complex networks from the perspectives of self-centrality, local centrality, and global centrality

Identifying influential nodes in complex networks and ranking their importance plays an important role in many fields such as public opinion analysis, marketing, epidemic prevention and control. To solve the issue of the existing node centrality measure only considering the specific statistical feature of a single dimension, a SLGC model is proposed that combines a node’s self-influence, its local neighborhood influence, and global influence to identify influential nodes in the network. The exponential function of e is introduced to measure the node’s self-influence;in the local neighborhood,the node’s one-hop neighboring nodes and two-hop neighboring nodes are considered, while the information entropy is introduced to measure the node’s local influence;the topological position of the node in the network and the shortest path between nodes are considered to measure the node’s global influence. To demonstrate the effectiveness of the proposed model, extensive comparison experiments are conducted with eight existing node centrality measures on six real network data sets using node differentiation ability experiments, susceptible–infected–recovered(SIR) model and network efficiency as evaluation criteria. The experimental results show that the method can identify influential nodes in complex networks more accurately.

AIGCrank:A new adaptive algorithm for identifying a set of influential spreaders in complex networks based on gravity centrality

The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds many practical applications in numerous areas such as information dissemination,epidemic immunity,and viral marketing.However,most existing influence maximization algorithms are limited by the“rich-club”phenomenon and are thus unable to avoid the influence overlap of seed spreaders.This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy,named AIGCrank,to identify a set of influential seeds.Specifically,the gravity centrality jointly employs the neighborhood,network location and topological structure information of nodes to evaluate each node's potential of being selected as a seed.We also present a recursive ranking strategy for identifying seed nodes one-byone.Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.

Identifying multiple influential spreaders in complex networks based on spectral graph theory

One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex network.The importance and dispersion of critical nodes among them are both vital factors that can influence maximization.We therefore propose a multiple influential spreaders identification algorithm based on spectral graph theory.This algorithm first quantifies the role played by the local structure of nodes in the propagation process,then classifies the nodes based on the eigenvectors of the Laplace matrix,and finally selects a set of critical nodes by the constraint that nodes in the same class are not adjacent to each other while different classes of nodes can be adjacent to each other.Experimental results on real and synthetic networks show that our algorithm outperforms the state-of-the-art and classical algorithms in the SIR model.

Identification of Influential Users in Online Social Network: A Brief Overview

Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.

Influential Factors on Economic Benefit of Rice Farmers

[Objective] This study was to provide references for the improvement of agricultural economic benefit by analyzing the main factors on influencing the economic benefit of rice farmer. [Method] Field survey was carried out on 300 rice farmers in Suizhou, Wuxue and Xiaogan of Hubei Province. Through the descriptive statistics to the production input and output data of the sample farmers, the econometric model was established to conduct empirical test on the economic benefit of rice farmer and the influential factors. The optimal production scale for rice farmer was also estimated, and through the theoretical and empirical analysis of the optimal production scale, countermeasures were put forward with the aim to promote economic benefit of rice farmer. [Result] The application of fertilizer affected the economic benefit most, followed by the planting area; food policy, farming population and average economic income per capita had significant effects on the economic benefit of rice production; economic benefit was beneficial for cost control. [Conclusion] This study had great practical and instructive significance for stabilizing national food production, ensuring regional and national food safety.

Influential Factors of House-purchasing Intention in Work construction Sites among Migrant Workers with a Case of Investigations on 203 Migrant Workers from Jiangxi Province

The research selected 13 factors from individual and family characters, construction site characters and work characters and analyzed the influences on the willingness of purchasing houses on construction sites with Logistic regression mod- el. The results showed that gender, age differences, personality, willingness on fami- ly land conversion, management of enterprises on construction sites, relatives and friends on construction lands, and Job satisfaction were approved by significant testing, but marital status, annual household income, the rate of migrant worker in a household, transportation in work city, and corporate reputation were not approved by significant testing.

Comprehensive Analysis of the Influential Factors of Karst Cave Tourism Development

There are many karst caves in China and each karst cave is a special part of natural landscape resources.How to exploit karst cave resources to develop tourism relates to the the effective exploitation of karst caves resources.The author comprehensively analyzed the main influential factors of karst cave tourism development and concluded that the influential factors were the feature of karst caves resources,transportation condition,construction condition,regional economic development level,source market,regional combination of scenic spots,spatial competition,investors' decision-making behavior,local residents' attitudes and so on.The integrated action of these factors codetermines whether karst cave tourism development successes or not.Only by thoroughly realizing the status and role of every influential factor karst cave tourism development could be reasonably conducted,exploiting strong points and avoiding shortcomings.

Influential Factors of Water Productivity of Maize in Oasis of Arid Areas——A Case Study of Linze County

[Objective] The aim was to research influential factors of water productivity of maize in oasis in arid areas. [Method] In middle reach of Heihe River in Linze County of Zhangye City, maize, a major crop in irrigated area in the oasis, was studied and nine influential factors were chosen through grey relation analysis. [Result] According to grey relation analysis, top five factors influencing water produc- tivity of maize during 1995-2009 were as follows： applied quantity of chemical fertil- izers〉labour input〉seeds〉mulch amount〉pesticide amount, which indicated that controllable factors, instead of uncontrollable ones, dominate in influence of water productivity of maize in oasis in arid areas. [Conclusion] With water resource limited, investment of controllable factors in high-efficient agricultural production should be the major strategy to improve water productivity of maize in oasis of arid areas.

Analysis on Influential Factors of Peasants' Willingness to Exit Homestead in Regions with Different Economy Development Levels——Taihe Village,Chaohu County-Level City,and Ancang Village,Taihu County as Examples

The research concluded peasants＇ willingness on homestead exiting and influential factors by questionnaire survey and interviewing in two villages with varied levels of economic development in Anhui Province.Comparisons were performed on subjective factors and objective factors were explored by Logistic regression.The results showed that both of education background and homestead use have positive correlation with peasants＇ willingness on homestead exit;the number of family members has negative effects on peasants in advanced village but the number of migrant workers or members living in cities has positive effects;the area of homestead has positive effects on peasants in underdeveloped village.Therefore,the emphasis should be varied on homestead exit upon village economy and it is necessary to reinforce homestead exit promotion at improving immigrants＇ reception work,social security,employment,and education.

Directly searching method for slip plane and its influential factors based on critical state of slope

In order to determine the slip plane of slope directly by the calculation results of strength reduction method, and analyze the influential factors of slope stability, a numerical model was established in plane strain mode by FLAC3D for homogeneous soil slope, whose parameters were reduced until the slope reached the critical state. Then FISH program was used to get the location data of slip plane from displacement contour lines. Furthermore, the method to determine multiple slip planes was also proposed by setting different heights of elastic areas. The influential factors for the stability were analyzed, including cohesion, internal friction angle, and tensile strength. The calculation results show that with the increase of cohesion, failure mode of slope changes from shallow slipping to the deep slipping, while inclination of slip plane becomes slower and slipping volume becomes larger; with the increase of friction angle, failure mode of slope changes from deep slipping to shallow slipping, while slip plane becomes steeper and upper border of slip plane comes closer to the vertex of slope; the safety factor increases little and slip plane goes far away from vertex of slope with the increase of tensile strength.

Identifying influential spreaders in complex networks based on entropy weight method and gravity law

In complex networks,identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of networks.Nowadays,it is widely used in power networks,aviation networks,computer networks,and social networks,and so on.Traditional centrality methods mainly include degree centrality,closeness centrality,betweenness centrality,eigenvector centrality,k-shell,etc.However,single centrality method is onesided and inaccurate,and sometimes many nodes have the same centrality value,namely the same ranking result,which makes it difficult to distinguish between nodes.According to several classical methods of identifying influential nodes,in this paper we propose a novel method that is more full-scaled and universally applicable.Taken into account in this method are several aspects of node’s properties,including local topological characteristics,central location of nodes,propagation characteristics,and properties of neighbor nodes.In view of the idea of the multi-attribute decision-making,we regard the basic centrality method as node’s attribute and use the entropy weight method to weigh different attributes,and obtain node’s combined centrality.Then,the combined centrality is applied to the gravity law to comprehensively identify influential nodes in networks.Finally,the classical susceptible-infected-recovered(SIR)model is used to simulate the epidemic spreading in six real-society networks.Our proposed method not only considers the four topological properties of nodes,but also emphasizes the influence of neighbor nodes from the aspect of gravity.It is proved that the new method can effectively overcome the disadvantages of single centrality method and increase the accuracy of identifying influential nodes,which is of great significance for monitoring and controlling the complex networks.

Influential factors and control of water inrush in a coal seam as the main aquifer

In this paper, a combination of field measurement, theoretical analysis and numerical simulation were used to study the main control factors of coal mine water inrush in a main aquifer coal seam and its control scheme. On the basis of revealing and analyzing the coal seam as the main aquifer in western coal mine of Xiao Jihan coal mine, the simulation software of PHASE-2D was applied to analyze the water inflow under different influencing factors. The results showed that water inflow increases logarithmically with the coal seam thickness, increases as a power function with the permeability coefficient of the coal seam, and increases linearly with the coal seam burial depth and the head pressure; The evaluation model for the factors of coal seam water inrush was gained by using nonlinear regression analysis with SPSS. The mine water inrush risk evaluation partition within the scope of the mining field was obtained,through the engineering application in Xiao Jihan coal mine. To ensure the safe and efficient production of the mine, we studied the coal mine water disaster prevention and control measures of a main aquifer coal seam in aspects of roadway driving and coal seam mining.

Evidential method to identify influential nodes in complex networks

Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degree centrality, betweenness centra- lity and closeness centrality are taken into consideration in the proposed method. Numerical examples are used to illustrate the effectiveness of the proposed method.

Influential Node Ranking and Invulnerability of Air Traffic Cyber Physical System

To ensure flight safety,the complex network method is used to study the influence and invulnerability of air traffic cyber physical system(CPS)nodes.According to the rules of air traffic management,the logical coupling relationship between routes and sectors is analyzed,an air traffic CPS network model is constructed,and the indicators of node influence and invulnerability are established.The K-shell algorithm is improved to identify node influence,and the invulnerability is analyzed under random and selective attacks.Taking Airspace in Eastern China as an example,its influential nodes are sorted by degree,namely,K-shell,the improved K-shell(IKS)and betweenness centrality.The invulnerability of air traffic CPS under different attacks is analyzed.Results show that IKS can effectively identify the influential nodes in the air traffic CPS network,and IKS and betweenness centrality are the two key indicators that affect the invulnerability of air traffic CPS.

Tempo-spatial characteristics and influential factors of rockburst:a case study of transportation and drainage tunnels in Jinping Ⅱ hydropower station

Jinping Ⅱ hydropower station is located in a high in-situ stress region in Southwest China. During the excavations of the transportation and drainage tunnels, more than 460 rockburst events were recorded in the transportation tunnel and 110 in the drainage tunnel, which has a serious and negative influence on the tunnels’ construction and the safety of staff and equipment. In the paper, the characters of rockburst patterns are analyzed for the transportation and drainage tunnels. The results are illustrated as follow: (1) Most of intensive rockbursts occur in the layer T2b, and continuous occurrences of rockbursts are more frequently observed than those in other layers. (2) The critical overburden depth of rockburst in the transportation tunnel is 600 m, and the length of the continuous occurrence section of rockburst is smaller than 25 m. The damaged depth of the rockburst has the tendency to increase with the increasing overburden depth, and the maximum damaged depth is over 3.5 m. (3) From east to west (west to east) in Jinping Ⅱ hydropower station, the rockburst usually takes place in the right (left) side of tunnel working face, and then the left (right) or roof of the tunnel. The total length of the continuous occurrence section of rockburst is 57.4%–62.2% of the overall rockburst length, followed by the rockbursts of flake-splitting type and other types. (4) Compared with the transportation tunnel, the intensity of rockburst in the drainage tunnel is higher while the length of the continuous occurrence section of rockburst is smaller. The rockburst section with length less than 10 m and depth of 1 m mainly occurs in the layer at a depth of 1 800–2 000 m. The influences of opening geometry and excavation method on the characteristics of the adjacent zone are great, but the influence of the stress among the tunnel group induced by excavation is relatively low.

Influential nodes identification in complex networks based on global and local information

Identifying influential nodes in complex networks is essential for network robust and stability,such as viral marketing and information control.Various methods have been proposed to define the influence of nodes.In this paper,we comprehensively consider the global position and local structure to identify influential nodes.The number of iterations in the process of k-shell decomposition is taken into consideration,and the improved k-shell decomposition is then put forward.The improved k-shell decomposition and degree of target node are taken as the benchmark centrality,in addition,as is well known,the effect between node pairs is inversely proportional to the shortest path length between two nodes,and then we also consider the effect of neighbors on target node.To evaluate the performance of the proposed method,susceptible-infected(SI)model is adopted to simulate the spreading process in four real networks,and the experimental results show that the proposed method has obvious advantages over classical centrality measures in identifying influential nodes.

Influential factors on the exchange rate of dissolved inorganic nutrients at the sediment-water interface in Jiaozhou Bay, China

Incubation experiments are carried out to study the exchange rates of dissolved inorganic nutrients including silicate, phosphate, ammonium, nitrite, and nitrate (vSiO3-Si, vPO4-P, vNH4-N, vNO2-N and vNO3-N) at the sediment-water interface in the Jiaozhou Bay. Major factors influencing the exchange rates are discussed in detail, which include the dissolved inorganic nutrient concentrations in porewater (Cpw), water and clay contents, and grain size of the sediments (CH2O, Cclay and GSsed). The results may provide insight into the dynamics of nutrient transport and the environmental capacity of nutrients in Jiaozhou Bay, and should be beneficial to solving the problems caused by excessive nutrient input this area.

LCH:A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks

Identifying influential nodes in complex networks is one of the most significant and challenging issues,which may contribute to optimizing the network structure,controlling the process of epidemic spreading and accelerating information diffusion.The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity.Moreover,they do not take into account the impact of network topology evolution over time,resulting in limitations in some applications.Based on local information of networks,a local clustering H-index(LCH)centrality measure is proposed,which considers neighborhood topology,the quantity and quality of neighbor nodes simultaneously.The proposed measure only needs the information of first-order and second-order neighbor nodes of networks,thus it has nearly linear time complexity and can be applicable to large-scale networks.In order to test the proposed measure,we adopt the susceptible-infected-recovered(SIR)and susceptible-infected(SI)models to simulate the spreading process.A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.

Photosynthetic response of Pinus sylvestriformis to elevated carbon dioxide and its influential factor analysis

The photosynthetic response of 12-year old Pinus sylvestriformis to elevated CO2 and its influential factors were tested and analyzed in the forest region of Changbai Mountain in 1999. Thees grown at the natural condition were controlled at three levels of CO2 concentration (350μL·L-1, 500 μL·L -1 and 700μL·L-1) by CO2 rich settlement designed by us. Net photosynthetic rates (NPR), temperature, relative humidity, stomatal conduc- tance, intercellular CO2 concentration and photosynthetic active radiation (PAR) were measured at 6:00, 8:00, 10:00, 14:00, 16:00 and 18:00 hours a day. Experimental results showed that the NPR of Pinus sylvestriformis increased by 32.6% and 123.0% at 500 μL·L-1 and 700 μL·L-1 CO2 concentration respectively, compared to am- bient atmospheric CO2 concentration (350 μL·L-1). The relations betWeen NPR and influential factors, including temperature, relative humidity, intercellular CO2 concentration and photosynthetic active radiation, were analyzed respectively by regression analysis at different CO2 concentrations.

Influential Factors of Bucket Foundation for Offshore Wind Turbine

In this paper, the influential design thctors of wide-shallow composite bucket foundation for 3 MW off- shore wind turbine are systematically studied by numerical simulation. The results show that the bucket diameter is larger than 27 m in generak and the range of 7--12 m is appropriate for cylinder height. In particular the bucket foun- dation with diameter of 30 m and cylinder height of 10 m is suitable for most soils. Under ultimate loads, the bucket diameter and elasticity modulus of soil have major effects on the deibrmability of bucket foundation, while the influ- ence of friction coefficient between the bucket and soil is relatively slight.