129 citations · 204 across the 24 of their papers we have counts for
24 papers
ContextFlow: Hierarchical Task-State Alignment for Long-Horizon Embodied Agents
Shuhan Guo, Kun Zhang, Haifei Liu +4
Long-horizon embodied agents increasingly delegate navigation, search, approach, and manipulation to specialist executors. As these executors become stronger, the main bottleneck s…
Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interaction
Guangyi Liu, Yongqi Zhang, Xunyuan Liu +1
Drug-drug interaction (DDI) prediction is critical for treatment safety. While large language models (LLMs) show promise in pharmaceutical tasks, their effectiveness in DDI predict…
GraphOracle: Efficient Fully-Inductive Knowledge Graph Reasoning via Relation-Dependency Graphs
Enjun Du, Siyi Liu, Yongqi Zhang
Knowledge graph reasoning in the fully-inductive setting, where both entities and relations at test time are unseen during training, remains an open challenge. In this work, we int…
Benchmarking drug-drug interaction prediction methods: a perspective of distribution changes
Zhenqian Shen, Mingyang Zhou, Yongqi Zhang +1
Motivation: Emerging drug-drug interaction (DDI) prediction is crucial for new drugs but is hindered by distribution changes between known and new drugs in real-world scenarios. Cu…
Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks
Quanming Yao, Yongqi Zhang, Yaqing Wang +3
The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. However, due t…
Dual Reasoning: A GNN-LLM Collaborative Framework for Knowledge Graph Question Answering
Guangyi Liu, Yongqi Zhang, Yong Li +1
Large Language Models (LLMs) excel at intuitive, implicit reasoning. Guiding LLMs to construct thought chains can enhance their deliberate reasoning abilities, but also faces chall…
Knowledge-Enhanced Recommendation with User-Centric Subgraph Network
Guangyi Liu, Quanming Yao, Yongqi Zhang +1
Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences. The classical methods which rely on user-…
Learning to Describe for Predicting Zero-shot Drug-Drug Interactions
Fangqi Zhu, Yongqi Zhang, Lei Chen +2
Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of concurrent drug administration, posing a significant challenge in healthcare. As the development of new dr…
Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network
Yongqi Zhang, Quanming Yao, Ling Yue +4
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patie…
Positive-Unlabeled Node Classification with Structure-aware Graph Learning
Hansi Yang, Yongqi Zhang, Quanming Yao +1
Node classification on graphs is an important research problem with many applications. Real-world graph data sets may not be balanced and accurate as assumed by most existing works…
Relation-aware Ensemble Learning for Knowledge Graph Embedding
Ling Yue, Yongqi Zhang, Quanming Yao +5
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In thi…
Understanding Expressivity of GNN in Rule Learning
Haiquan Qiu, Yongqi Zhang, Yong Li +1
Rule learning is critical to improving knowledge graph (KG) reasoning due to their ability to provide logical and interpretable explanations. Recently, Graph Neural Networks (GNNs)…
AutoWeird: Weird Translational Scoring Function Identified by Random Search
Hansi Yang, Yongqi Zhang, Quanming Yao
Scoring function (SF) measures the plausibility of triplets in knowledge graphs. Different scoring functions can lead to huge differences in link prediction performances on differe…
KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning
Yongqi Zhang, Zhanke Zhou, Quanming Yao +1
While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently. To solve this problem, we first analyze the properti…
AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning
Yongqi Zhang, Zhanke Zhou, Quanming Yao +2
Due to the popularity of Graph Neural Networks (GNNs), various GNN-based methods have been designed to reason on knowledge graphs (KGs). An important design component of GNN-based…
Knowledge Graph Reasoning with Relational Digraph
Yongqi Zhang, Quanming Yao
Reasoning on the knowledge graph (KG) aims to infer new facts from existing ones. Methods based on the relational path have shown strong, interpretable, and transferable reasoning…
Bilinear Scoring Function Search for Knowledge Graph Learning
Yongqi Zhang, Quanming Yao, James Tin-Yau Kwok
Learning embeddings for entities and relations in knowledge graph (KG) have benefited many downstream tasks. In recent years, scoring functions, the crux of KG learning, have been…
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
Shimin Di, Quanming Yao, Yongqi Zhang +1
The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also…
Combining Self-Supervised and Supervised Learning with Noisy Labels
Yongqi Zhang, Hui Zhang, Quanming Yao +1
Since convolutional neural networks (CNNs) can easily overfit noisy labels, which are ubiquitous in visual classification tasks, it has been a great challenge to train CNNs against…
Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding
Yongqi Zhang, Quanming Yao, Lei Chen
Negative sampling, which samples negative triplets from non-observed ones in knowledge graph (KG), is an essential step in KG embedding. Recently, generative adversarial network (G…
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
Yongqi Zhang, Quanming Yao, Lei Chen
Knowledge graph (KG) embedding is well-known in learning representations of KGs. Many models have been proposed to learn the interactions between entities and relations of the trip…
AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
Yongqi Zhang, Quanming Yao, Wenyuan Dai +1
Scoring functions (SFs), which measure the plausibility of triplets in knowledge graph (KG), have become the crux of KG embedding. Lots of SFs, which target at capturing different…
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
Yongqi Zhang, Quanming Yao, Yingxia Shao +1
Knowledge Graph (KG) embedding is a fundamental problem in data mining research with many real-world applications. It aims to encode the entities and relations in the graph into lo…
XOGAN: One-to-Many Unsupervised Image-to-Image Translation
Yongqi Zhang
Unsupervised image-to-image translation aims at learning the relationship between samples from two image domains without supervised pair information. The relationship between two d…