Learning from Heterogeneity: A Dynamic Learning Framework for
  Hypergraphs
Tiehua Zhang Yuze Liu Zhishu Shen Xingjun Ma Xin Chen Xiaowei Huang Jun Yin Jiong Jin
Graph neural network (GNN) has gained increasing popularity in recent years
owing to its capability and flexibility in modeling complex graph structure
data. Among all graph learning methods, hypergraph learning is a technique for
exploring the implicit higher-order correlations when training the embedding
space of the graph. In this paper, we propose a hypergraph learning framework
named LFH that is capable of dynamic hyperedge construction and attentive
embedding update utilizing the heterogeneity attributes of the graph.
Specifically, in our framework, the high-quality features are first generated
by the pairwise fusion strategy that utilizes explicit graph structure
information when generating initial node embedding. Afterwards, a hypergraph is
constructed through the dynamic grouping of implicit hyperedges, followed by
the type-specific hypergraph learning process. To evaluate the effectiveness of
our proposed framework, we conduct comprehensive experiments on several popular
datasets with eleven state-of-the-art models on both node classification and
link prediction tasks, which fall into categories of homogeneous pairwise graph
learning, heterogeneous pairwise graph learning, and hypergraph learning. The
experiment results demonstrate a significant performance gain (average 12.5% in
node classification and 13.3% in link prediction) compared with recent
state-of-the-art methods.