53 citations · 58 across the 8 of their papers we have counts for
8 papers
Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion
Dongxiao He, Ruqiong Zhang, Zhizhi Yu +4
Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications. Recently, Graph Neural Network…
TIGFlow-GRPO: Trajectory Forecasting via Interaction-Aware Flow Matching and Reward-Guided Optimization
Xuepeng Jing, Wenhuan Lu, Hao Meng +2
Human trajectory forecasting is important for intelligent multimedia systems operating in visually complex environments, such as autonomous driving and crowd surveillance. Although…
Speech-to-See: End-to-End Speech-Driven Open-Set Object Detection
Wenhuan Lu, Xinyue Song, Wenjun Ke +3
Audio grounding, or speech-driven open-set object detection, aims to localize and identify objects directly from speech, enabling generalization beyond predefined categories. This…
KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks
Zhizhi Yu, Di Jin, Cuiying Huo +5
Social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next gene…
TeKo: Text-Rich Graph Neural Networks with External Knowledge
Zhizhi Yu, Di Jin, Jianguo Wei +5
Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i.e., networks). Typical GNNs and their variants follow a m…
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
Di Jin, Zhizhi Yu, Pengfei Jiao +5
Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection ha…
BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks
Di Jin, Xiangchen Song, Zhizhi Yu +4
Graph convolutional networks (GCNs), aiming to integrate high-order neighborhood information through stacked graph convolution layers, have demonstrated remarkable power in many ne…
GCN for HIN via Implicit Utilization of Attention and Meta-paths
Di Jin, Zhizhi Yu, Dongxiao He +3
Heterogeneous information network (HIN) embedding, aiming to map the structure and semantic information in a HIN to distributed representations, has drawn considerable research att…