Tutorial 2

Nowadays, more and more applications are based on larger and larger networks. It is well recognized that network data is sophisticated and challenging. To process graph data effectively, the first critical challenge is network data representation, that is, how to represent networks properly so that advanced analytic tasks, such as pattern discovery, analysis, and prediction, can be conducted efficiently in both time and space. In this tutorial, we will present the recent thoughts and achievements on network representation. More specifically, the fundamental problems in network representation learning, including why we need to revisit network representation, what are the research goals of network representation, and how network representations can be learned, will be discussed.