  Due to the ever-increasing size of data, construction, analysis and mining of
universal massive networks are becoming forbidden and meaningless. In this
work, we outline a novel framework called CubeNet, which systematically
constructs and organizes real-world networks into different but correlated
semantic cells, to support various downstream network analysis and mining tasks
with better flexibility, deeper insights and higher efficiency. Particular, we
promote our recent research on text and network mining with novel concepts and
techniques to (1) construct four real-world large-scale multi-facet
hierarchical heterogeneous networks; (2) enable insightful OLAP-style network
analysis; (3) facilitate localized and contextual network mining. Although some
functions have been covered individually in our previous work, a systematic and
efficient realization of an organic system has not been studied, while some
functions are still our on-going research tasks. By integrating them, CubeNet
may not only showcase the utility of our recent research, but also inspire and
stimulate future research on effective, insightful and scalable knowledge
discovery under this novel framework.
