90 citations · 200 across the 28 of their papers we have counts for
28 papers
HeterSEED: Semantics-Structure Decoupling for Heterogeneous Graph Learning under Heterophily
Xinyi Li, Ming Li, Lu Bai +5
Many real-world heterogeneous graphs exhibit pronounced heterophily, where connected nodes often have dissimilar labels or play different semantic roles. In such settings, standard…
Please refuse to answer me! Mitigating Over-Refusal in Large Language Models via Adaptive Contrastive Decoding
Yupeng Qi, Ziyu Lyu, Lixin Cui +2
Safety-aligned large language models (LLMs) often generate refusal responses to harmless queries due to the over-refusal problem. However, existing methods for mitigating over-refu…
RAIE: Region-Aware Incremental Preference Editing with LoRA for LLM-based Recommendation
Jin Zeng, Yupeng Qi, Hui Li +4
Large language models (LLMs) are increasingly adopted as the backbone of recommender systems. However, user-item interactions in real-world scenarios are non-stationary, making pre…
LGAN: An Efficient High-Order Graph Neural Network via the Line Graph Aggregation
Lin Du, Lu Bai, Jincheng Li +5
Graph Neural Networks (GNNs) have emerged as a dominant paradigm for graph classification. Specifically, most existing GNNs mainly rely on the message passing strategy between neig…
MidPO: Dual Preference Optimization for Safety and Helpfulness in Large Language Models via a Mixture of Experts Framework
Yupeng Qi, Ziyu Lyu, Min Yang +3
As large language models (LLMs) are increasingly applied across various domains, enhancing safety while maintaining the helpfulness of LLMs has become a critical challenge. Recent…
Knowledge Probing for Graph Representation Learning
Mingyu Zhao, Xingyu Huang, Ziyu Lyu +3
Graph learning methods have been extensively applied in diverse application areas. However, what kind of inherent graph properties e.g. graph proximity, graph structural informatio…
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
Zhuo Xu, Lu Bai, Lixin Cui +3
Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective…
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks
Zhehan Zhao, Lu Bai, Lixin Cui +4
Graph Neural Networks (GNNs) are powerful tools for graph classification. One important operation for GNNs is the downsampling or pooling that can learn effective embeddings from t…
Dual-modal Prior Semantic Guided Infrared and Visible Image Fusion for Intelligent Transportation System
Jing Li, Lu Bai, Bin Yang +4
Infrared and visible image fusion (IVF) plays an important role in intelligent transportation system (ITS). The early works predominantly focus on boosting the visual appeal of the…
SSHPool: The Separated Subgraph-based Hierarchical Pooling
Zhuo Xu, Lixin Cui, Ming Li +6
In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. We commence by assigning…
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification
Feifei Qian, Lixin Cui, Ming Li +6
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are d…
AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks
Lixin Cui, Ming Li, Yue Wang +2
In this work, we develop an Aligned Entropic Reproducing Kernel (AERK) for graph classification. We commence by performing the Continuous-time Quantum Walk (CTQW) on each graph str…
QESK: Quantum-based Entropic Subtree Kernels for Graph Classification
Lu Bai, Lixin Cui, Edwin R. Hancock
In this paper, we propose a novel graph kernel, namely the Quantum-based Entropic Subtree Kernel (QESK), for Graph Classification. To this end, we commence by computing the Average…
HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification
Lu Bai, Lixin Cui, Yue Wang +2
In this work, we propose a family of novel quantum kernels, namely the Hierarchical Aligned Quantum Jensen-Shannon Kernels (HAQJSK), for un-attributed graphs. Different from most e…
Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus
Yue Wang, Yao Wan, Lu Bai +5
To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples cont…
Writing Style Aware Document-level Event Extraction
Zhuo Xu, Yue Wang, Lu Bai +1
Event extraction, the technology that aims to automatically get the structural information from documents, has attracted more and more attention in many fields. Most existing works…
Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks
Yue Wang, Zhuo Xu, Lu Bai +5
Joint-event-extraction, which extracts structural information (i.e., entities or triggers of events) from unstructured real-world corpora, has attracted more and more research atte…
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
Lu Bai, Lixin Cui, Edwin R. Hancock
In this paper, we develop a new graph kernel, namely the Hierarchical Transitive-Aligned kernel, by transitively aligning the vertices between graphs through a family of hierarchic…
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling
Yue Wang, Chenwei Zhang, Shen Wang +4
We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future link…
Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis
Lu Bai, Lixin Cui, Lixiang Xu +3
In this work, we develop a novel framework to measure the similarity between dynamic financial networks, i.e., time-varying financial networks. Particularly, we explore whether the…
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking
Yue Wang, Yao Wan, Chenwei Zhang +3
Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps pe…
A Quantum-inspired Similarity Measure for the Analysis of Complete Weighted Graphs
Lu Bai, Luca Rossi, Lixin Cui +2
We develop a novel method for measuring the similarity between complete weighted graphs, which are probed by means of discrete-time quantum walks. Directly probing complete graphs…
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification
Lu Bail, Lixin Cui, Yuhang Jiao +2
In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification. Our idea is to tran…
Learning Vertex Convolutional Networks for Graph Classification
Lu Bai, Lixin Cui, Shu Wu +2
In this paper, we develop a new aligned vertex convolutional network model to learn multi-scale local-level vertex features for graph classification. Our idea is to transform the g…