  Features representation leverages the great power in network analysis tasks.
However, most features are discrete which poses tremendous challenges to
effective use. Recently, increasing attention has been paid on network feature
learning, which could map discrete features to continued space. Unfortunately,
current studies fail to fully preserve the structural information in the
feature space due to random negative sampling strategy during training. To
tackle this problem, we study the problem of feature learning and novelty
propose a force-based graph learning model named GForce inspired by the
spring-electrical model. GForce assumes that nodes are in attractive forces and
repulsive forces, thus leading to the same representation with the original
structural information in feature learning. Comprehensive experiments on
benchmark datasets demonstrate the effectiveness of the proposed framework.
Furthermore, GForce opens up opportunities to use physics models to model node
interaction for graph learning.
