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Lingyuan Meng

7 accepted papers

2026

Make Model Transparent: Brain Network Analysis via Causal and Knowledge Graph Learning

AAAI 2026technical

Brain network analysis technology reveals the organizational mechanism and information processing mode by constructing the structural connection network between brain regions. It has achieved satisfactory results in brain disease prediction tasks, promoting the progress of neuroscience. In recent ye

Cited by 0SourcePDFScholar
2026

PhenoBrain: Phenotype-Conditioned Long-Range Communication for Multi-Modal Brain Network Analysis

ICML 2026oral

Multi-modal brain network analysis aims to predict neuropsychiatric status from functional connectomes with heterogeneous phenotypes. However, most existing methods treat phenotypes as auxiliary features and perform late fusion, implicitly assuming that the connectome representation should be learne…

Cited by 0SourceScholar
2025

FS-KEN: Few-shot Knowledge Graph Reasoning by Adversarial Negative Enhancing

IJCAI 2025

Few-shot knowledge graph reasoning (FS-KGR) try to infer missing facts in a knowledge graphs using limited data (such as only 3/5 samples).Existing strategies have shown good performance by mining more supervised information for few-shot learning through meta-learning and self-supervised learning. H

Cited by 0SourcePDFScholar
2025

SAINT: Sequence-Aware Integration for Spatial Transcriptomics Multi-View Clustering

NeurIPS 2025poster

Spatial transcriptomics (ST) technologies provide gene expression measurements with spatial resolution, enabling the dissection of tissue structure and function. A fundamental challenge in ST analysis is clustering spatial spots into coherent functional regions. While existing models effectively int…

Cited by 0SourceScholar
2025

Soft Reasoning Paths for Knowledge Graph Completion

IJCAI 2025

Reasoning paths are reliable information in knowledge graph completion (KGC) in which algorithms can find strong clues of the actual relation between entities. However, in real-world applications, it is difficult to guarantee that computationally affordable paths exist toward all candidate entities.

2024

Clustering then Propagation: Select Better Anchors for Knowledge Graph Embedding

NeurIPS 2024poster

Traditional knowledge graph embedding (KGE) models map entities and relations to unique embedding vectors in a shallow lookup manner. As the scale of data becomes larger, this manner will raise unaffordable computational costs. Anchor-based strategies have been treated as effective ways to alleviate…

Cited by 0SourcePDFScholar
2024

MINES: Message Intercommunication for Inductive Relation Reasoning over Neighbor-Enhanced Subgraphs

AAAI 2024technical

GraIL and its variants have shown their promising capacities for inductive relation reasoning on knowledge graphs. However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs. Besides, the enclosing subgraph e…

Cited by 38SourcePDFScholar