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cs.LG2025
A Survey of Large Language Models for Data Challenges in Graphs
Mengran Li, Pengyu Zhang, Wenbin Xing +11
Graphs are a widely used paradigm for representing non-Euclidean data, with applications ranging from social network analysis to biomolecular prediction. While graph learning has a…
cs.LG2024
TIGER: Temporally Improved Graph Entity Linker
Pengyu Zhang, Congfeng Cao, Paul Groth
Knowledge graphs change over time, for example, when new entities are introduced or entity descriptions change. This impacts the performance of entity linking, a key task in many u…
cs.LG2024
CYCLE: Cross-Year Contrastive Learning in Entity-Linking
Pengyu Zhang, Congfeng Cao, Klim Zaporojets +1
Knowledge graphs constantly evolve with new entities emerging, existing definitions being revised, and entity relationships changing. These changes lead to temporal degradation in…