Periodic subgraph mining in dynamic networks

World today can be described as interactions of many entities such as humans, animals, smartphones interacting among themselves. Interactions that occur regularly typically correspond to significant, yet often infrequent and hard to detect interaction patterns that are interesting to know in order to understand and predict behaviors of entities. To identify these regular behaviors, the book presents the periodic subgraph mining problem in a dynamic network and an efficient algorithm to solve it. A dynamic network is a temporal sequence of graphs that represents interactions among individuals of a population over the time. Social network analysis is probably the most famous example of dynamic network analysis. The book proposes the applications of the problem on some real-world networks and shows that analyzing interesting and insightful periodic interaction patterns uncover and characterize the natural periodicities of systems.

Autorentext

Manuel Barbares received a Master Degree in Information Engineering with honors from the University of Padua.He was also a visiting scholar at the Georgia Institute of Technology doing research on data mining and data analysis of large datasets. Currently he works as management consultant in enterprise systems, IT governance and strategy.