45 citations · 73 across the 10 of their papers we have counts for
10 papers
Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment
Qijie Ding, Jie Yin, Daokun Zhang +1
Entity alignment (EA) aims at identifying equivalent entity pairs across different knowledge graphs (KGs) that refer to the same real-world identity. To circumvent the shortage of…
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…
Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment
Qijie Ding, Daokun Zhang, Jie Yin
Entity alignment aims to discover unique equivalent entity pairs with the same meaning across different knowledge graphs (KGs). Existing models have focused on projecting KGs into…
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…
Link Prediction with Contextualized Self-Supervision
Daokun Zhang, Jie Yin, Philip S. Yu
Link prediction aims to infer the link existence between pairs of nodes in networks/graphs. Despite their wide application, the success of traditional link prediction algorithms is…
Search Efficient Binary Network Embedding
Daokun Zhang, Jie Yin, Xingquan Zhu +1
Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving network structure and/or node content information, such tha…
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…
Network Representation Learning: A Survey
Daokun Zhang, Jie Yin, Xingquan Zhu +1
With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as soc…