most citedSelf-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment

100 citations · 119 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2023★ 2 cited

Fast Continual Multi-View Clustering with Incomplete Views

Xinhang Wan, Bin Xiao, Xinwang Liu +3

Multi-view clustering (MVC) has gained broad attention owing to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging i…

cs.CV2023★ 12 cited

Table Detection for Visually Rich Document Images

Bin Xiao, Murat Simsek, Burak Kantarci +1

Table Detection (TD) is a fundamental task to enable visually rich document understanding, which requires the model to extract information without information loss. However, popula…

cs.IR2023★ 3 cited

Revisiting Table Detection Datasets for Visually Rich Documents

Bin Xiao, Murat Simsek, Burak Kantarci +1

Table Detection has become a fundamental task for visually rich document understanding with the surging number of electronic documents. However, popular public datasets widely used…

cs.LG2023★ 2 cited

SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning

Lingyuan Meng, Ke Liang, Bin Xiao +5

Few-shot relation reasoning on knowledge graphs (FS-KGR) aims to infer long-tail data-poor relations, which has drawn increasing attention these years due to its practicalities. Th…

cs.AI2023

Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network

Wenxuan Tu, Bin Xiao, Xinwang Liu +3

With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world. Unfortunately, graphs usually suffer f…

cs.LG2023★ 100 cited

Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment

Meng Liu, Ke Liang, Yawei Zhao +5

Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast…