Social Investor

With based in Probabilistic Network Analisis in Ph.D Course, Social networks, biological networks, sensor networks, the WWW, ... all are examples of networks that provide increasingly rich sources of data, and that become an increasingly important subject of study. The common underlying structure, and the similarity of  analysis tasks in very different domains concerned with networks has given rise to  the emergent discipline of network science. An important element of network science  consists of Machine Learning on graph data for network structure analysis and predictive  modeling. In this idea core Machine Learning techniques for network data are presented. The focus lies on techniques that build on probabilistic network models, and that apply statistical learning principles.

Random graph models; power laws; 

Graph clustering/community detection

Node classification and link prediction 

Random walk models: pagerank, hitting times, commute times

Multi-relational graphs and statistical relational learning

Mastering Statics by The Hong Kong University of Science and Technology, Sustainable and Inclusive Landscapes; Enhance your transformative thinking and action to build sustainable integrated landscapes and contribute our world’s SDGs. 

Understand how landscapes are vibrant and dynamic multi-functional spaces in which competing claims on resources exist alongside interdependencies among sectors, ambitions and inhabitants

Develop leadership skills to mobilise resources and motivate stakeholders to achieve SDG-focused sustainable change

Gain critical insights and develop the skills to capitalize on investors motivation to invest and the need for innovative financing in landscapes

The knowledge and skills to design and implement effective landscape governance