8 citations · 13 across the 3 of their papers we have counts for
5 papers
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
Yixin Liu, Yizhen Zheng, Daokun Zhang +2
Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily a…
Towards Unsupervised Deep Graph Structure Learning
Yixin Liu, Yu Zheng, Daokun Zhang +3
In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated whe…
Attributed Network Embedding via Subspace Discovery
Daokun Zhang, Jie Yin, Xingquan Zhu +1
Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on netwo…
SINE: Scalable Incomplete Network Embedding
Daokun Zhang, Jie Yin, Xingquan Zhu +1
Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content.…
MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding
Daokun Zhang, Jie Yin, Xingquan Zhu +1
Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes. As a result…