Search

As most of the community discovery methods are researched by static thought, some community discovery algorithms cannot represent the whole dynamic network change process efficiently. This paper proposes a novel dynamic community discovery method (Phylogenetic Planted Partition Model, PPPM) for phylogenetic evolution. Firstly, the time dimension is introduced into the typical migration partition model, and all states are treated as variables, and the observation equation is constructed. Secondly, this paper takes the observation equation of the whole dynamic social network as the constraint between variables and the error function. Then, the quadratic form of the error function is minimized. Thirdly, the Levenberg–Marquardt (L–M) method is used to calculate the gradient of the error function, and the iteration is carried out. Finally, simulation experiments are carried out under the experimental environment of artificial networks and real networks. The experimental results show that: compared with FaceNet, SBM + MLE, CLBM, and PisCES, the proposed PPPM model improves accuracy by 5% and 3%, respectively. It is proven that the proposed PPPM method is robust, reasonable, and effective. This method can also be applied to the general social networking community discovery field.

Link prediction exercises may prove particularly challenging with noisy and incomplete networks, such as criminal networks. Also, the link prediction effectiveness may vary across different relations within a social group. We address these issues by assessing the performance of different link prediction algorithms on a mafia organization. The analysis relies on an original dataset manually extracted from the judicial documents of operation “Montagna”, conducted by the Italian law enforcement agencies against individuals affiliated with the Sicilian Mafia. To run our analysis, we extracted two networks: one including meetings and one recording telephone calls among suspects, respectively. We conducted two experiments on these networks. First, we applied several link prediction algorithms and observed that link prediction algorithms leveraging the full graph topology (such as the Katz score) provide very accurate results even on very sparse networks. Second, we carried out extensive simulations to investigate how the noisy and incomplete nature of criminal networks may affect the accuracy of link prediction algorithms. The experimental findings suggest the soundness of link predictions is relatively high provided that only a limited amount of knowledge about connections is hidden or missing, and the unobserved edges follow some kind of generative law. The different results on the meeting and telephone call networks indicate that the specific features of a network should be taken into careful consideration.

Diabetes mellitus is a complex clinical entity that will grow in importance in the future. The complications of diabetes have a significant impact on patient survival and quality of life, particularly with respect to coronary artery disease (CAD). Appropriate screening and aggressive intervention can significantly benefit many patients with diabetes. In addition, it is important to consider strategies useful not only in the diagnosis of CAD but also in the prognostic evaluation of diabetic patients with coronary disease. Prognostic data are essential in defining risk categories and to apply appropriate treatment for the degree of risk. Therefore, accurate cardiovascular risk stratification of patients with type 2 diabetes is required. However, this can be a problematic issue because the clinical presentation and progression of CAD differs between diabetic and nondiabetic subjects. In addition to a higher prevalence of CAD, patients with diabetes experience more diffuse and extensive coronary artery involvement, more often have left ventricular dysfunction, a more advanced coronary disease at the time of diagnosis, and more often experience silent ischemia. Furthermore, diabetic patients have frequently a less favorable response to revascularization procedures and a poorer long-term outcome. The purpose of this review is to discuss the relative role of various procedures for diagnosis of CAD and for cardiac risk stratification in patients with diabetes.

Left ventricular (LV) function is one of the most powerful predictors of prognosis in the setting of heart failure (HF) or following myocardial infarction. It is important to differentiate patients in whom LV dysfunction is caused by necrosis and scar tissue formation from those with LV dysfunction because of ischemia or hibernating, but viable myocardium. Previous studies showed that identification of the latter group of patients predicts substantial survival benefit, symptomatic improvement, and improved LV function with revascularization. On the other hand, more recent investigations, such as the Surgical Treatment for Ischemic Heart Failure (STICH) trial [3], did not find a significant interaction between myocardial-viability status and medical versus surgical treatment with respect to the rates of death from any cause or from cardiovascular causes or the rate of death or hospitalization for cardiovascular causes.