337 citations · 512 across the 8 of their papers we have counts for
8 papers
TGNN: A Joint Semi-supervised Framework for Graph-level Classification
Wei Ju, Xiao Luo, Meng Qu +5
This paper studies semi-supervised graph classification, a crucial task with a wide range of applications in social network analysis and bioinformatics. Recent works typically adop…
Learning Graph ODE for Continuous-Time Sequential Recommendation
Yifang Qin, Wei Ju, Hongjun Wu +2
Sequential recommendation aims at understanding user preference by capturing successive behavior correlations, which are usually represented as the item purchasing sequences based…
A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu +13
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been…
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
Jianhao Shen, Chenguang Wang, Ye Yuan +5
This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM…
DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
Yifang Qin, Yifan Wang, Fang Sun +6
Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geograph…
GLCC: A General Framework for Graph-Level Clustering
Wei Ju, Yiyang Gu, Binqi Chen +5
This paper studies the problem of graph-level clustering, which is a novel yet challenging task. This problem is critical in a variety of real-world applications such as protein cl…
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Zhaocheng Zhu, Xinyu Yuan, Mikhail Galkin +4
Reasoning on large-scale knowledge graphs has been long dominated by embedding methods. While path-based methods possess the inductive capacity that embeddings lack, their scalabil…
FNAS: Uncertainty-Aware Fast Neural Architecture Search
Jihao Liu, Ming Zhang, Yangting Sun +4
Reinforcement learning (RL)-based neural architecture search (NAS) generally guarantees better convergence yet suffers from the requirement of huge computational resources compared…