8 papers
EA-Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment
Yixuan Nan, Xixun Lin, Yanmin Shang +4
Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object and plays a critical role in knowledge fusion and i…
PathMind: A Retrieve-Prioritize-Reason Framework for Knowledge Graph Reasoning with Large Language Models
Yu Liu, Xixun Lin, Yanmin Shang +3
Knowledge graph reasoning (KGR) is the task of inferring new knowledge by performing logical deductions on knowledge graphs. Recently, large language models (LLMs) have demonstrate…
Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning
Yu Liu, Yanan Cao, Xixun Lin +3
Knowledge graph completion (KGC) aims to infer new knowledge and make predictions from knowledge graphs. Recently, large language models (LLMs) have exhibited remarkable reasoning…
Deep Graph Neural Point Process For Learning Temporal Interactive Networks
Su Chen, Xiaohua Qi, Xixun Lin +3
Learning temporal interaction networks(TIN) is previously regarded as a coarse-grained multi-sequence prediction problem, ignoring the network topology structure influence. This pa…
RANA: Robust Active Learning for Noisy Network Alignment
Yixuan Nan, Xixun Lin, Yanmin Shang +3
Network alignment has attracted widespread attention in various fields. However, most existing works mainly focus on the problem of label sparsity, while overlooking the issue of n…
Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic Graphs
Nan Sun, Xixun Lin, Zhiheng Zhou +3
Recently, Out-of-distribution (OOD) detection in dynamic graphs, which aims to identify whether incoming data deviates from the distribution of the in-distribution (ID) training se…
VR-GNN: Variational Relation Vector Graph Neural Network for Modeling both Homophily and Heterophily
Fengzhao Shi, Ren Li, Yanan Cao +5
Graph Neural Networks (GNNs) have achieved remarkable success in diverse real-world applications. Traditional GNNs are designed based on homophily, which leads to poor performance…
RLINK: Deep Reinforcement Learning for User Identity Linkage
Xiaoxue Li, Yanan Cao, Yanmin Shang +3
User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise…