Information Diffusion Method Research Articles

Risk Estimation of Typhoon Disaster Based on Three-Dimensional Information Diffusion Method

In the context of the increasing frequency and intensity of natural disasters, assessing the risk of typhoon disasters can provide significant assistance for risk control and emergency management of typhoon disasters. In this paper, based on the three-dimensional information diffusion method, the formal expected loss model is transformed into a computable typhoon risk assessment model. The fuzzy information in the small sample data is deeply mined, and the typhoon disaster risk assessment with the expected loss as the connotation is carried out, and the probability density distribution estimation of disaster-causing factors at different levels and the functional relationship identification between disaster-causing factors at different levels and direct economic loss rate are realized by using the information matrix. At the same time, combined with the frequency of typhoon occurrence, the annual risk of disasters is predicted to make up for the problem of insufficient marine environmental data and improve the calculation accuracy of risk assessment models. Taking Guangdong Province as an example, a typhoon risk assessment was conducted, estimating the probability distribution, direct economic loss rate distribution, and annual loss expectation of typhoon disasters under different wind speed scales and extreme wave heights. The results indicate that the risk estimation value of the three-dimensional information diffusion model is higher than that of the traditional model, which weakens the limitations of the low-dimensional information diffusion model and makes the evaluation results more reasonable and reliable.

Dynamic risk assessment of compound hazards based on VFS–IEM–IDM: a case study of typhoon–rainstorm hazards in Shenzhen, China

Abstract. Global warming has led to increased compound hazards, and an accurate risk assessment of such hazards is of great importance to urban emergency management. Due to the interrelations between multiple hazards, the risk assessment of a compound hazard faces several challenges: (1) the evaluation of hazard level needs to consider the correlations between compound hazard drivers, (2) usually only a small number of data samples are available for estimating the joint probability distribution of the compound hazard drivers and the loss caused by the hazards, and (3) the risk assessment process often ignores the temporal dynamics of compound hazard occurrences. This paper aims to address the mentioned challenges and develop an integrated risk assessment model VFS–IEM–IDM to quantify the dynamic risk of compound hazards based on variable fuzzy set theory (VFS), information entropy method (IEM), and information diffusion method (IDM). For the first challenge, VFS–IEM–IDM measures the effect of the compound hazard drivers via the use of relative membership degree and analyses the correlation between drivers with the entropy weight method, which is combined to evaluate compound hazard level. To address the second challenge, VFS–IEM–IDM applies the normal diffusion function to estimate the probability distribution of the compound hazard and the corresponding loss vulnerability curve. To deal with the third challenge, VFS–IEM–IDM assesses the risk of a compound hazard in different months based on the definition of probabilistic risk. In the end, this paper takes the typhoon–rainstorm disaster in Shenzhen, China, as an example to evaluate the effectiveness of the proposed VFS–IEM–IDM model. The results show that VFS–IEM–IDM effectively estimates the typhoon–rainstorm compound hazard level and assesses the dynamic risk of the compound hazards.

Spatially resolved risk assessment of Natech in the Yangtze River Economic Belt, China

China is not only a large industrial country but also one of the countries with severe natural disasters in the world. The impact of a technical accident triggered by natural disasters (Natech) cannot be ignored. In Natech risk assessment, most research focuses on the quantitative analysis of Natech risk in small-scale level areas, such as parks or enterprises; Natech risk assessment in large-scale areas is typically at low resolution. There is a lack of systematic and high-resolution assessments on a large regional scale. In this paper, a grid Natech risk assessment method that couples the probability model and the information diffusion method is proposed. We select the Yangtze River Economic Belt (YREB) as the research area to assess its Natech risks triggered by floods, earthquakes and typhoons with a spatial resolution of 1 km × 1 km. The results show that the atmospheric risk level is higher than the water environment risk level, but their distribution trends are roughly similar. For the Natech risk triggered by floods, the high-risk areas of the atmosphere and water accounted for 3.83% and 1.73% of the YREB respectively, and were distributed along with the river network. For the Natech risk triggered by earthquakes, the high-risk areas of the atmosphere and water accounted for 0.47% and 0.08% of the YREB, respectively, and high-risk areas were mostly concentrated in north-central Sichuan and northern Yunnan. For the Natech risk triggered by typhoons, the high-risk areas of the atmosphere and water accounted for 3.52% and 2.68% of the YREB respectively, which were mostly concentrated in parts of Jiangxi and south-eastern Hunan. The spatially resolved map can help to identify the spatial characteristics and hot spots, which can provide a scientific reference for macro decision-making in environmental risk management.

Proposal and Performance Evaluation of Information Diffusion Technique with Novel Virtual-Cell-Based Wi-Fi Direct

Wi-Fi Direct (WFD) has attracted much attention in recent years as a device-to-device communication technology that enables the sharing and diffusion of information in disaster areas that lack existing communication infrastructures. In WFD, mobile devices such as smartphones and tablets establish communication groups (a wireless local area network (WLAN)), and transmit and/or receive data by Wi-Fi among devices autonomously. However, WFD is not effective for spreading data throughout a wide range because it randomly selects communication partners and needs to wait uselessly for random lengths of time to maintain access control. Thus, we propose a novel and efficient information diffusion method exploiting WFD and delay-tolerant networking (DTN). This method involves a unique virtual cell design based on device locations and enables efficient information diffusion without interference by assigning communication times to the virtual cells. As there is no deterministic means of evaluating multihop communication exploiting WFD and a mathematical model is needed to quantitatively indicate the performance of our method, we further construct a performance evaluation model using the reaction–diffusion model, which is a partial differential equation used to mathematically describe the spread of epidemic diseases. The effectiveness of this proposed diffusion method is evaluated exploiting the constructed model.

Using Multiple Index Comprehensive Method to Assess Urban Rainstorm Disaster Risk in Jiangsu Province, China

An integrated index system for urban rainstorm risk evaluation has been developed. Meanwhile, an information diffusion method (IDM) and variable fuzzy sets (VFSs) were employed to evaluate the dangerousness, sensitivity, and vulnerability risk of urban rainstorm disasters, respectively. Then, the comprehensive risk zoning map was drawn. Finally, Jiangsu Province has been taken as a case study area. Due to heavy rainfall in short-term and low rainstorm resistance ability, Wuxi, Changzhou, Nanjing, and Suzhou have higher dangerousness while Wuxi, Changzhou, and Nanjing have higher sensitivity. And because of potential losses in urban rainstorm disaster, Wuxi and Suzhou have higher vulnerability than other cities. The comprehensive risk zoning map showed that most cities of Jiangsu Province are at the moderate risk level, and the northwestern cities have lower risk level than the southern cities. The results are consistent with the actual situation of Jiangsu Province, and the study can provide some decision-making references for the urban rainstorm management.

Establishing a dynamic elastic modulus prediction model of larch based on nondestructive testing data

To accurately evaluate the dynamic elastic modulus (Ed) of wood in ancient timberwork buildings, the new materials of larch were used as the research object, and the stress wave nondestructive testing method was used to determine it. Based on nondestructive testing data, this paper proposed a method for predicting the Ed of larch using the principle of information diffusion. It selected the distance (D) from the bark to the pith in the cross-section of the wood and the height (H) from the base to the top in the radial section of the wood. The fuzzy diffusion relationships between the two evaluation indexes and the Ed were established using the information diffusion principle and the first- and second-order fuzzy approximate inferences in the fuzzy information optimization process. The calculation results showed that the dynamic elastic modulus model constructed by the information diffusion method can better predict the Ed of larch. The coefficient of determination between the measured value and the predicted value of the Ed was 0.861, they were in good agreement. The weights of the two influencing factors were 0.7 and 0.3, respectively, the average relative error of the fitted sample data was the minimum, which was 8.55%. This prediction model provided a strong basis for field inspection.

Risk Assessment of Agricultural Drought Disaster on the Huaibei Plain of China Based on the Improved Connection Number and Entropy Information Diffusion Method

In recent years, drought disaster has occurred frequently in China, causing significant agricultural losses. It is increasingly important to assess the risk of agricultural drought disaster (ADD) and to develop a targeted risk management approach. In this study, an ADD risk assessment model was established. First, an improved fuzzy analytic hierarchy process based on an accelerated genetic algorithm (AGA-FAHP) was used to build an evaluation indicator system. Then, based on the indicators, the ADD assessment connection numbers were established using the improved connection number method. Finally, the entropy information diffusion method was used to form an ADD risk assessment model. The model was applied to the Huaibei Plain in Anhui Province (China), with the assessment showing that, in the period from 2008 to 2017, the plain was threatened continuously by ADD, especially during 2011–2013. The risk assessment showed that southern cities of the study area were nearly twice as likely to be struck by ADD as northern cities. Meanwhile, the eastern region had a higher frequency of severe and above-grade ADD events (once every 21 years) than the western region (once every 25.3 years). Therefore, Huainan was identified as a high-risk city and Huaibei as a low-risk city, with Suzhou and Bengbu more vulnerable to ADD than Fuyang and Bozhou. Understanding the spatial dynamics of risk in the study area can improve agricultural system resilience by optimizing planting structures and by enhancing irrigation water efficiency. This model could be used to provide support for increasing agricultural drought disaster resilience and risk management efficiency.

Risk Evaluation Based on Variable Fuzzy Sets and Information Diffusion Method

Risk evaluation is an effective way to reduce the impacts of natural hazards and it plays an increasingly important role in emergency management. Traditional methods of assessing risks mainly utilize Geographic Information System (GIS) to get risk map, and information diffusion method (IDM) to deal with incomplete data sets. However, there are few papers discuss the uncertainty of integrated hazards and consider dynamic risk under time dimension. The model proposed in this study combines the variable fuzzy set theory with information diffusion method (VFS-IDM) to solve the uncertainness of multiple hazards dynamic risk assessment when data sets are incomplete. This study employs fuzzy set theory (VFS) to calculate the relative membership degree and applies information entropy method (IEM) to obtain the weights of criteria indicators for multiple hazards evaluation. Then applies information diffusion method (IDM) to estimate condition probability distribution and vulnerability curve with the VFS-IEM model results, time data and multiple hazards losses. Then the expected value of multiple hazards dynamic risk can be calculated by using the normal information diffusion estimator so as to improve the accuracy of risk evaluation results.

Desastres Naturales y Mortalidad: Análisis Probabilístico de Argentina (1970-2015)

The frequency and intensity of natural disasters are expected to increase due to climate change. In this context, this work advances in the estimation of an absolute measure of mortality risk generated by natural disasters, in regions of Argentina. In particular, exceedance probabilities of mortality are estimated using the information diffusion method. The historical data of natural disasters provided by the records of DesInventar are used. These records cover the 1970-2015 period and are published by the Social Studies Network in Disaster Prevention in Latin America (LA RED). The results suggest that the Centro region is the one with the highest risk of absolute mortality, while the Noreste region has the lowest risk levels. In a given year there is a 0.93 probability that, at least, one death will occur due to natural disasters in the Centro region. In the Noreste region, this probability is 0.77. The results are robust and significant

Normal Information Diffusion Distribution and Its Application in Inferring the Optimal Probability Density Functions of the Event Coordinates From the Microseismic or Acoustic Emission Sources

To obtain optimal probability density functions (PDFs) or cumulative density functions (CDFs) of the event coordinates from the microseismic or acoustic emission sources, the normal information diffusion (NID) method based on the “ $3\sigma $ ” truncated interval is introduced. Six sets of different data of the event coordinates from the locating sources are used to illustrate the goodness-of-fit of the NID method, log-logistic (3P) method, lognormal method, and normal method. The results show that the Kolmogorov-Smirnov (K-S) and chi-square test values of the NID distributions (NIDDs) are always less than those of the log-logistic (3P) distributions (LLD3s), lognormal distributions (LNDs), and normal distributions (NDs); the cumulative probability values of the NIDDs are equal to 1, while those of the LLD3s, LNDs, and NDs are less than 1; the curves of the NIDDs have multimodal feature and can reflect the fluctuation of the event coordinates’ data. The conclusion can be drawn that the NIDDs are the optimal PDFs or CDFs of the event coordinates from the microseismic or acoustic emission sources. In the locating methods of the microseismic or acoustic emission sources, it is suggested that the NID method can be further used to improve the locating accuracy.

Analysis of Agricultural Drought Risk Based on Information Distribution and Diffusion Methods in the Main Grain Production Areas of China

Accurate assessment of agricultural drought risk is of strategic significance to ensure future grain production security in the main grain production areas of China. Agricultural drought risk assessment is based on drought vulnerability characteristics. In this study, firstly the drought thresholds were redefined by correlation analysis of drought strength based on the Standardized Precipitation Evapotranspiration Index (SPEI) and drought damage rates, then the information distribution and the two-dimensional normal information diffusion method were employed to establish the vulnerability curve between drought strength and drought damage rates. Finally, provincial drought risks and the conditional probabilities at different drought damage stages were obtained. The results show that the drought vulnerability curve was nonlinear. With the increase of drought strength, drought damage rates increased rapidly at the beginning, and after a small fluctuation locally, they no longer increased significantly and tended to be relative stable. The occurrence probabilities of agricultural drought risk presented great spatial differences, with the characteristics of high in the northern, moderate in the central and southwestern part, and lower in the southeastern provinces in the main grain production areas of China. The analysis of conditional probability showed that Hubei, Henan, and Jiangxi were the provinces most prone to drought-affected risk under the drought-induced condition; while Liaoning, Hunan, and Inner Mongolia were the ones most prone to lost harvest risk under the drought-induced or the drought-affected condition. The results could be used to provide guidance for drought risk management and to formulate appropriate plans by the relevant departments.

Assessing multi-risk characteristics of heat and cold stress for rice across the southern parts of China.

Rice (Oryza sativa) growth is always threatened by heat as well as cold stress, when it is exposed to natural environment. Heat growing degree hours (HGDH) and cold growing degree hours (CGDH1 and CGDH2) were firstly proposed to quantify heat and cold stress occurred during different growing stages. Information diffusion method was effectively used to fit the distribution and estimate probability of single stress at each station, with an advantage of no limitation in data series. In terms of single stress, highest probability was seen in HGDH, followed by CGDH1 and CGDH2. Seven copula functions, i.e., normal and t, Gumbel-Hougaard, Clayton, Frank, Joe, and Ali-Mikhail-Haq, were applied to fit the distribution of multi-stress with significant dependence, and simple calculation based on single stress was used to quantify the probability for multi-stress with independence. In these copulas, t was the most chosen one in the fitting of HGDH-CGDH1, HGDH-CGDH2, CGDH1-CGDH2, and HGDH-CGDH1-CGDH2, selected by the statistic of Akaike information criterion. Regarding bi-stress, higher joint probability was in HGDH-CGDH1, relative to HGDH-CGDH2 and CGDH1-CGDH2. As expected, the co-occurrence probability of tri-stress was lower than that of bi-stress in the magnitude and spatial extent, while joint probability of tri-stress was larger. Given the condition of occurrence of HGDH or CGDH1, the joint probability of HGDH-CGDH1 was higher than other pairs of bi-stress and tri-stress. It was special that higher joint probability of CGDH1-CGDH2 was detected under the condition of CGDH2 relative to CGDH1. Joint probability of tri-stress was lower under the condition of HGDH-CGDH1, in comparison with HGDH-CGDH2 and CGDH1-CGDH2. Hazards of single stress and multi-stress were expressed by the integration of intensity of stress index and corresponding probability. Most consistent conclusions agreed that central Fujian, Zhejiang, and northeastern Jiangxi were exposed to higher hazard, derived from not only single stress but also multi-stress. These results can be helpful in provision of information regarding prevention and adaptation strategies for rice cultivation as a response to extreme temperature stress.

Experimental and statistical studies on the thermal properties of frozen clay in Qinghai-Tibet Plateau

Frozen soil consists of an irregular distribution of solid particles, gas, water and ice lenses; hence, the thermal property of the frozen soil is uncertain and variable. In addition, ice lenses and ice-water phase changes make the soil sensitive to temperature and intensify this variability phenomenon. However, little is known about the extent to which the thermal properties of frozen soil vary and their statistical characteristics. To address this lack of knowledge, we conducted large-scale tests on a clay from the Qinghai-Tibet Plateau (≥50 samples) and measured the thermal conductivities and heat capacities at five temperatures (−10 ° C, −5 ° C, −2 ° C, −1 ° C and 20 ° C). The measured data show that the two thermal parameters of the fifty clay samples exhibit obvious variability at each soil temperature. In addition, we selected four potential probability distributions to compare with the measured curves and found that the normal distribution, lognormal distribution and gamma distribution agreed well with the measured data and all satisfied the hypothesis testing. Based on goodness-of-fit analyses and a comparison with the normal information diffusion method (NIDM), we proposed that the normal distribution can best describe the thermal parameters of the frozen clay. This study can contribute to a better understanding of the variability of frozen soil properties. Furthermore, the results and conclusions of this study provide a theoretical basis and reference for heat calculations of frozen soil.

Characteristics of drought vulnerability for maize in the eastern part of Northwest China

Based on information distribution and diffusion method theory and combined with the standardized precipitation index and relative meteorological yield data, meteorological factors and social factors were comprehensively considered to assess the vulnerability of maize (Zea mays) to drought. The probability distribution curve of meteorological drought degree (MDD) and relative meteorological yield in the eastern part of Northwest China (Gansu, Ningxia and Shaanxi) from 1978 to 2016 were obtained, using a two-dimensional normal information diffusion method to construct the vulnerability relationship between MDD and relative meteorological yield. The drought vulnerability curve of maize in the study area was obtained. The probability distribution of MDD was multiplied by the fragility curve and summed to obtain the multi-year average risk. The MDD probability distribution curve showed that the probability of moderate drought in Shaanxi was relatively high, followed by Gansu and Ningxia. The probability distribution of Gansu was more discrete. The probability of strong meteorological drought in Ningxia was high, followed by Shaanxi and Gansu. Probability distribution of relative meteorological yield for maize in Gansu Province was highly discrete, with thick tailings, large uncertainties, and more extreme values, which were strongly affected by meteorological conditions, followed by Shaanxi and Ningxia. Taking meteorological drought as the cause and maize damage as the result, the vulnerability relationship between MDD and drought damage was obtained. With an increased MDD, the relative meteorological yield of maize gradually declined. From the average value, when MDD was less than −2.60, the relative meteorological yield of maize was reduced within 15%; when MDD was greater than −2.60, the relative meteorological yield of maize increased within 10%. When the degree of meteorological drought exceeded −2.2, maize was most vulnerable to drought in Shaanxi followed by Ningxia and Gansu. When meteorological drought was less than −2.2, maize was most vulnerable to drought in Shaanxi followed by Gansu and Ningxia. The expected values of relative meteorological production in Gansu, Ningxia, and Shaanxi were 1.36%, 2.48%, and −1.76%, respectively; therefore, Shaanxi had the highest maize drought risk, followed by Gansu and Ningxia. This research had a clear physical background and clear risk connotations. The results provide a data foundation and a theoretical basis for drought disaster reduction for maize in the study area.

A novel algorithm based on information diffusion and fuzzy MADM methods for analysis of damages caused by diabetes crisis

Diabetes mellitus is one of the most common chronic diseases in the world. A remarkable point about chronic diseases is that patients may be involved in related complications throughout their lifetime. Management and control of chronic diseases are one of the most costly and important issues in the healthcare field due to their long term treatment period. In this paper, the number of patients involved in complications of type II diabetes has been studied in 19 years and the goal is to calculate the severity of damage caused by diabetes by a new hybrid algorithm which is consisted of multi criteria decision making, variable fuzzy set theory and information diffusion method for the first time. However, the traditional probability statistical methods ignore the fuzziness of risk assessment with incomplete data sets and require a large sample size of data, the new proposed algorithm deals effectively with these challenges and produces more accurate results with fewer errors. For example in terms of working with small data samples, it will be shown that the mean error of results produced by hybrid algorithm is 0.0198 while it is 0.0264 for multiple regression and 0.0299 for cox proportional hazard method which means 33.3% and 51% error increase in proportion to hybrid algorithm respectively.

Research of influence of sample size on normal information diffusion based on the Monte Carlo method: risk assessment for natural disasters

Information diffusion techniques and Monte Carlo methods have been widely used in solving all kinds of problems of small samples with incomplete data in the field of natural disaster risk assessment such as environmental resource rating, flood monitoring and temperature changing. Data are not the only thing that matters for natural disaster risk assessment, but with enough data, we can accurately predict the time, place, scale and loss of future disasters. It is important to simulate the complete data scene when there is a minimum sample size. In this paper, we collect temperature data from 3050 meteorological stations in China and use the Monte Carlo simulation method to investigate the effect of sample size on estimating the normal information diffusion. The results show that (1) for the same sample, the information diffusion method is significantly better than the traditional histogram method. (2) Using the hard histogram estimation method, the recommended sample size is 85 or more, which is slightly larger than the traditional threshold value (i.e., 30), while using the information diffusion estimation method, the recommended sample size decreases to 45 or more. (3) Simulation experiments show that, with insufficient samples, both estimation methods, i.e., the information diffusion and the traditional histogram methods become invalid because of its poor correlation, low robustness, high RMSE and variance values. These results indicate that the Monte Carlo simulation method and information diffusion technique have certain practical reference value in the research of natural disaster risk assessment in the case of a small sample.

Analysis of Drought Vulnerability Characteristics and Risk Assessment Based on Information Distribution and Diffusion in Southwest China

Drought vulnerability characteristics and risk assessment form the basis of drought risk management. In this study, the standardized precipitation index (SPI) and drought damage rates (DDR) were combined to analyze drought vulnerability characteristics and drought risk in Southwest China (SC). The information distribution method was applied to estimate the probability density of the drought strength (DS) and the two-dimensional normal information diffusion method was used to construct the vulnerability relationships between DS and drought damage (DD). The risk was then evaluated by combining the probability function of the DS and the DD vulnerability curve. The results showed that the relationship between the DS and the DD was nonlinear in SC and its provinces. With the increase in DS, the degree of DD increased gradually, stabilized, or decreased toward the end. However, the vulnerability characteristics of the different provinces varied widely due to multiple risk-bearing bodies and abilities to resist disasters. The risk values obtained across the range of time scales of the SPI were not significantly different. The yielding probabilities will be reduced for the crop area by 10%, 30%, and 70% due to drought. Compared to a normal year in SC, the probability values were 16.04%, 10.29%, and 2.70%, respectively. These results have the potential to provide a reference for agricultural production and drought risk management.

Edge information diffusion based reconstruction (EIDR) for cone beam computed laminography.

Computed laminography (CL) is a prospective nondestructive testing technique for flat object inspection in industrial applications. However, CL image reconstruction is a challenging task because incomplete projection data are acquired from the CL scan. When a conventional computed tomography (CT) reconstruction method is applied to cone beam CL data, the vertical edges (singularities in the z-direction) in the reconstructed image would be blurred. On the contrary, the horizontal edges (singularities within slices) can be quite accurately reconstructed. Based on this key observation, an edge information diffusion method is developed, which fixes the horizontal edges and propagates their values within the slices. An effective CL reconstruction method is then proposed for flat object inspection by combining the edge information diffusion procedure, which plays the role of regularization, with conventional CT image reconstruction algorithms. Experiments on both simulated data and real data are performed to verify the effectiveness of the proposed method. The results show that the proposed method can effectively suppress the inter-slice aliasing and blurring caused by incompleteness of the CL scan data, and that it outperforms other state-of-the-art methods.

Chinese Marine Economy Development: Dynamic Evolution and Spatial Difference

This study focuses on China’s coastal area and its marine economic development. Applying the information diffusion method, the study establishes a kernel density function and its decomposition using a marine economic per capita as the index of the model to depict the dynamic evolution law and the internal influential factors of the Chinese marine economy during 1996–2013. The relative development rate was introduced to analyze the spatial differences in the marine economy’s development. In this way, space and time dimensions fully characterized the evolution of the Chinese marine economy. Additionally, the influence of growth and inequality in the process of its development can be analyzed. The study shows that the Chinese marine economy as a whole has been growing, and regional marine economic development is relatively coordinated. In addition, the marine economy began to develop even more rapidly after 2004. There are three factors affecting the dynamic evolution of China’s marine economy: first, the most influential mean effect, followed by, second, the variance effect, and third, the least influential residual effect. The biggest influence on the dynamic evolution of the marine economy is the improvement of the development level of the marine economy in the coastal area. Meanwhile, due to the existence of inequality, provinces at higher development levels are more dispersed. Furthermore, the existence of the residual effect weakens the influence of the mean effect, and the influence on the dynamic evolution of the marine economy continuously increases. In the analysis of the influencing factors of the evolution and spatial difference of marine economic development, the level of opening to the outside world, the level of investment in fixed assets and the industrial structure have a positive role in promoting economic development. However, capital investment in scientific human research has a negative correlation with economic development, and does not pass the significant test. The difference in regional development levels and development speed is also very apparent; namely, the provinces with higher development levels generally displayed faster development speeds while those with lower development levels showed slower development speeds across the four periods analyzed.

A novel information diffusion method based on network consistency for identifying disease related microRNAs.

The abnormal expression of miRNAs is directly related to the development of human diseases. Predicting the potential candidate miRNAs associated with diseases can contribute to the detection, diagnosis, treatment and prevention of human complex diseases. The effective inference of the calculation method of the relationship between miRNAs and diseases is an effective supplement to biological experiments. It is of great help in the prevention, treatment and prognosis of complex diseases. This paper proposes a novel information diffusion method based on network consistency (IDNC) for identifying disease related microRNAs. The model first synthesizes the miRNA family information and the miRNA function similarity to reconstruct the miRNA network, and reconstruct the disease network by using the known disease and miRNA-related information and the semantic score between diseases. Then the global similarity of the two networks is obtained by using the Laplacian score of graphs. The global similarity score is a measure of the similarity between diseases and miRNAs. The disease–miRNA relation network was reconstructed by integrating the global similarity relation. The network consistency diffusion seed is then obtained by combining the global similarity network with the reconstructed disease–miRNA association network. Thereafter, the stable diffusion spectrum is generated as the prediction score by using the restarted random walk algorithm. The AUC value obtained by performing the LOOCV in the gold benchmark dataset is 0.8814. The AUC value obtained by performing the LOOCV in the predictive dataset is 0.9512. Compared with other frontier methods, our method has higher accuracy, which is further illustrated by case studies of breast neoplasms and colon neoplasms to prove that IDNC is valuable.