65 citations · 76 across the 4 of their papers we have counts for
5 papers
Graph Force Learning
Ke Sun, Jiaying Liu, Shuo Yu +2
Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, incre…
Network Representation Learning: From Traditional Feature Learning to Deep Learning
Ke Sun, Lei Wang, Bo Xu +3
Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been succes…
Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining
Jiaying Liu, Feng Xia, Lei Wang +4
The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims…
DINE: A Framework for Deep Incomplete Network Embedding
Ke Hou, Jiaying Liu, Yin Peng +3
Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representati…
MODEL: Motif-based Deep Feature Learning for Link Prediction
Lei Wang, Jing Ren, Bo Xu +3
Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms int…