On relational learning and discovery in social networks: a survey

The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements

A Comparative Study Based on Provincial Performance

This thesis attempts to transfer the spotlight upon the political issue about democratization in China to something else that has not been spared enough attention yet. Here it refers to the transformation of governance mode. The rather rapid industrialization and urbanization in the process of modernization in China does not aggravate the swelling of bureaucratic system to the extent that largely increases financial burden, internal disorder, and inefficiency. The maintenance of a stable and controllable government on both national and local level benefits from the transformation of governance mode from command mode to network mode

Information geometric methods for complexity

Research on the use of information geometry (IG) in modern physics has witnessed significant advances recently. In this review article, we report on the utilization of IG methods to define measures of complexity in both classical and, whenever available, quantum physical settings. A paradigmatic example of a dramatic change in complexity is given by phase transitions (PTs). Hence we review both global and local aspects of PTs described in terms of the scalar curvature of the parameter manifold and the components of the metric tensor, respectively. We also report on the behavior of geodesic paths on the parameter manifold used to gain insight into the dynamics of PTs. Going further, we survey measures of complexity arising in the geometric framework. In particular, we quantify complexity of networks in terms of the Riemannian volume of the parameter space of a statistical manifold associated with a given network. We are also concerned with complexity measures that account for the interactions of a given number of parts of a system that cannot be described in terms of a smaller number of parts of the system. Finally, we investigate complexity measures of entropic motion on curved statistical manifolds that arise from a probabilistic description of physical systems in the presence of limited information. The Kullback-Leibler divergence, the distance to an exponential family and volumes of curved parameter manifolds, are examples of essential IG notions exploited in our discussion of complexity. We conclude by discussing strengths, limits, and possible future applications of IG methods to the physics of complexity.Comment: review article, 60 pages, no figure

Spatial-Temporal Analysis of Residential Housing, Office Property, and Retail Property Price Index Correlations: Evidence from Ten Chinese Cities

Using correlation-based hierarchical analysis and synchronization analysis, this study focuses on monthly price indices for residential homes, office buildings, and retail properties in ten major Chinese cities for the years 2005 to 2021. Through these analyses, one can identify interactions and interdependence among the price indices, heterogeneous patterns in synchronizations of the price indices, and their evolving paths with time. Empirical findings suggest that the degree of real estate price comovements across all property types and cities is relatively low and stable from January 2017 to February 2020, followed by significant increases during the COVID-19 pandemic from March 2020 to January 2021 and significant decreases since February 2021 with the recovery of the economy. Several groups of property types and cities are determined in this study, each of which having its members reveal rather strong but volatile synchronizations of price indices. Rolling importance analysis does not suggest persistent increasing or decreasing trends for the real estate price associated with a specific property type and city. Policy studies on real estate price comovements may benefit from these findings here