activity
20242026
most citedCataLM: Empowering Catalyst Design Through Large Language Models

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

FOCAL-Attention for Heterogeneous Multi-Label Prediction

Chenghao Zhang, Qingqing Long, Ludi Wang +3

Heterogeneous graphs have attracted increasing attention for modeling multi-typed entities and relations in complex real-world systems. Multi-label node classification on heterogen…

cs.IR2026

Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching

Yicheng Pan, Zhiyuan Ning, Ludi Wang +1

As conference submission volumes continue to grow, accurately recommending suitable reviewers has become a challenge. Most existing methods follow a ``Paper-to-Paper'' matching par…

cs.LG2025

CITE: A Comprehensive Benchmark for Heterogeneous Text-Attributed Graphs on Catalytic Materials

Chenghao Zhang, Qingqing Long, Ludi Wang +3

Text-attributed graphs(TAGs) are pervasive in real-world systems,where each node carries its own textual features. In many cases these graphs are inherently heterogeneous, containi…

cs.LG2024★ 2 cited

CataLM: Empowering Catalyst Design Through Large Language Models

Ludi Wang, Xueqing Chen, Yi Du +3

The field of catalysis holds paramount importance in shaping the trajectory of sustainable development, prompting intensive research efforts to leverage artificial intelligence (AI…

cs.CL2024

DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation

Mengyi Huang, Meng Xiao, Ludi Wang +1

Continuous Relation Extraction (CRE) aims to incrementally learn relation knowledge from a non-stationary stream of data. Since the introduction of new relational tasks can oversha…