CubeNet: Multi-Facet Hierarchical Heterogeneous Network Construction, Analysis, and Mining

TL;DR

CubeNet is a comprehensive framework for constructing and analyzing large-scale, multi-facet hierarchical heterogeneous networks, enabling more flexible, insightful, and efficient network analysis and mining.

Contribution

It introduces a systematic approach to build and organize real-world networks into semantic cells, integrating multiple analysis and mining functions into an efficient, unified system.

Findings

Constructed four large-scale multi-facet hierarchical networks

Enabled OLAP-style network analysis for deeper insights

Facilitated localized and contextual network mining

Abstract

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.