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20212026
most citedGCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily

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

collaborators

5 papers

cs.CV2026

EarthLD: Towards Unified Open-World Landslide Understanding via Vision-Language Guided Diffusion Models

Yuanchao Su, Lianru Gao, Mengying Jiang +3

Landslides are widespread geological hazards, yet their automated detection and mapping in remote sensing imagery remain challenging because of their irregular morphology, ambiguou…

cs.LG2024

Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding

Mengying Jiang, Guizhong Liu, Yuanchao Su +1

In representation learning on graph-structured data, many popular graph neural networks (GNNs) fail to capture long-range dependencies, leading to performance degradation. Furtherm…

cs.LG2024

Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions

Mengying Jiang, Guizhong Liu, Yuanchao Su +2

Most existing methods for predicting drug-drug interactions (DDI) predominantly concentrate on capturing the explicit relationships among drugs, overlooking the valuable implicit c…

cs.LG2023

Relation-aware graph structure embedding with co-contrastive learning for drug-drug interaction prediction

Mengying Jiang, Guizhong Liu, Biao Zhao +2

Relation-aware graph structure embedding is promising for predicting multi-relational drug-drug interactions (DDIs). Typically, most existing methods begin by constructing a multi-…

cs.LG2021★ 5 cited

GCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily

Mengying Jiang, Guizhong Liu, Yuanchao Su +1

In representation learning on the graph-structured data, under heterophily (or low homophily), many popular GNNs may fail to capture long-range dependencies, which leads to their p…