Author name cluster

Yanhui Gu

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6 papers

1 author row

AAAI Conference 2026 Short Paper

Atom-level Adaptive Receptive Fields: A Pruning-Based Encoder for 2D Molecular Graphs (Student Abstract)

Yuhao Zhang
Ningkang Peng
Yafei Liu
Lin Li
Masaru Kitsuregawa
Yanhui Gu

The two-dimensional (2D) graph structure of a molecule encodes abundant latent property information. A well-designed molecular graph encoder can capture informative low-dimensional dense representations of molecules, which can subsequently be applied to a widerange of downstream tasks. To achieve fine-grained anddiscriminative molecular representations that capture localized structural information, we propose an novel atom-level adaptive receptive field encoder, enabling each atomic node in the molecular graph to dynamically adjust its receptive field size. To the best of our knowledge, we are the first to introduce an effective rank-guided pruning strategy for 2D molecular graphs.

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AAAI Conference 2026 Conference Paper

Counterfactual Question Generation Uncovering Learner Contradictions

Bo Zhang
Hao Yu
Wenjie Dong
Yvhang Yang
Dezhuang Miao
Fengyi Song
Yanhui Gu
Xiaoming Zhang

Conventional feedback, even when accompanied by brief explanations, rarely uncovers the hidden contradictions that trigger a learner's mistake. We bridge this gap with counterfactual question generation (CFQG): given a learner's answer, generate a follow-up question that deliberately contradicts it, compelling the learner to confront the underlying conflict. CFQG thus transforms assessment from passive scoring into an interactive and contradiction-centered dialogue that supports knowledge repair. To automate CFQG, we propose GapProbe, which probes the knowledge gap between a learner’s belief and curated facts through a knowledge graph (KG), then designs counterfactual questions (CFQs) that negate the belief. Identifying contradiction-aware triples, and more importantly, selecting those most likely to confuse the learner, are highly challenging in large-scale KGs. GapProbe tackles these challenges with an iterative ProConB cycle coupled with a schema-aware KGMap. By caching one- and multi-hop schema patterns of the KG, KGMap provides ``roadmap'' to guide LLMs jump to deep and contradiction-aware triples, beyond traditional step-wise graph traversal. We present the CFQG benchmark and corresponding metrics for evaluating how generated CFQs trigger, focus, and deepen learner reflection through explicit contradictions. Experiments on multiple datasets and LLMs show that GapProbe boosts LLM reasoning over KGs and generates follow-up questions that consistently promote deeper and more focused learner reflection.

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AAAI Conference 2024 Short Paper

MapLE: Matching Molecular Analogues Promptly with Low Computational Resources by Multi-Metrics Evaluation (Student Abstract)

Xiaojian Chen
Chuyue Liao
Yanhui Gu
Yafei Li
Jinlan Wang
Yi Chen
Masaru Kitsuregawa

Matching molecular analogues is a computational chemistry and bioinformatics research issue which is used to identify molecules that are structurally or functionally similar to a target molecule. Recent studies on matching analogous molecules have predominantly concentrated on enhancing effectiveness, often sidelining computational efficiency, particularly in contexts of low computational resources. This oversight poses challenges in many real applications (e.g., drug discovery, catalyst generation and so forth). To tackle this issue, we propose a general strategy named MapLE, aiming to promptly match analogous molecules with low computational resources by multi-metrics evaluation. Experimental evaluation conducted on a public biomolecular dataset validates the excellent and efficient performance of the proposed strategy.

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AAAI Conference 2023 Short Paper

HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs for Protein-Ligand Binding Affinity Prediction (Student Abstract)

Xianfeng Zhang
Yanhui Gu
Guandong Xu
Yafei Li
Jinlan Wang
Zhenglu Yang

Gathering information from multi-perspective graphs is an essential issue for many applications especially for proteinligand binding affinity prediction. Most of traditional approaches obtained such information individually with low interpretability. In this paper, we harness the rich information from multi-perspective graphs with a general model, which abstractly represents protein-ligand complexes with better interpretability while achieving excellent predictive performance. In addition, we specially analyze the protein-ligand binding affinity problem, taking into account the heterogeneity of proteins and ligands. Experimental evaluations demonstrate the effectiveness of our data representation strategy on public datasets by fusing information from different perspectives.

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IS Journal 2016 Journal Article

Point-of-Interest Recommendations via a Supervised Random Walk Algorithm

Guandong Xu
Bin Fu
Yanhui Gu

Recently, location-based social networks (LBSNs) such as Foursquare and Whrrl have emerged as a new application for users to establish personal social networks and review various points of interest (POIs), triggering a new recommendation service aimed at helping users locate more preferred POIs. Although users' check-in activities could be explicitly considered as user ratings, in turn being utilized directly for collaborative filtering-based recommendations, such solutions don't differentiate the sentiment of reviews accompanying check-ins, resulting in unsatisfactory recommendations. This article proposes a new POI recommendation framework by simultaneously incorporating user check-ins and reviews along with side information into a tripartite graph and predicting personalized POI recommendations via a sentiment-supervised random walk algorithm. The experiments conducted on real data demonstrate the superiority of this approach in comparison with state-of-the-art techniques.

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AAAI Conference 2011 Conference Paper

SemRec: A Semantic Enhancement Framework for Tag Based Recommendation

Guandong Xu
Yanhui Gu
Peter Dolog
Yanchun Zhang
Masaru Kitsuregawa

Collaborative tagging services provided by various social web sites become popular means to mark web resources for different purposes such as categorization, expression of a preference and so on. However, the tags are of syntactic nature, in a free style and do not reﬂect semantics, resulting in the problems of redundancy, ambiguity and less semantics. Current tag-based recommender systems mainly take the explicit structural information among users, resources and tags into consideration, while neglecting the important implicit semantic relationships hidden in tagging data. In this study, we propose a Semantic Enhancement Recommendation strategy (SemRec), based on both structural information and semantic information through a uniﬁed fusion model. Extensive experiments conducted on two real datasets demonstrate the effectiveness of our approaches.

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