Bi-graph based social network analysis and learning.

Many real-life networks are bi-partite in nature, meaning the nodes of the network can be separated in two disjoint types and edges exist only between nodes of different type. Think for example of academic authors being linked to the papers they have authored, or mobile devices linked to the locations they visited. Quite often only the projected network is used: a network of authors, linked if they share a paper, or a network of mobile devices, linked if they visited the same location. This however leads to substantial information loss and an increase in network size. Although network analysis and learning has emerged as an important field in both social sciences, humanities and computer science, very little work exists on this specific type of network. In this project we will define new metrics to analyze the global properties of such networks, study their evolution over time, develop tailored network learning techniques, and validate our designs with large-scale network data. We shall specifically focus on three real-life cases: the author-paper network using public data as well using as data from University of Antwerp, the customer-payment receiver network using data from a large European bank, and finally a mobile device-location network using data from a US-based ad exchange. Our findings should lead to novel insights into human behavior, theory building and improved predictive modeling.

Keywords:AD HOC NETWORKS, MARKETING, DATA ANALYSIS, DATA MINING

Disciplines:Applied mathematics in specific fields, Statistics and numerical methods, Applied economics, Economic history, Macroeconomics and monetary economics, Microeconomics, Tourism