Ziyu Guan

Interpretable and Efficient Heterogeneous Graph Convolutional Network

2021 · IEEE Transactions on Knowledge and Data Engineering

Graph Convolutional Network (GCN) has achieved extraordinary success in learning representations of nodes in graphs. However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore …

A Simple Weakly-Supervised Contrastive Learning Framework for Few-shot Sentiment Classification

2023 · Research Square

Abstract Most existing deep learning-based sentiment classification methods need large human-annotated data, but labeling large amounts of high-quality emotional texts is labor-intensive. Users on various social platforms generate massive amounts of tagged opinionated text (e.g., …

Aligning Multiple Knowledge Graphs in A Single Pass

2026

Entity alignment (EA) is to identify equivalent entities across different knowledge graphs (KGs), which can help fuse these KGs into a more comprehensive one. Previous EA methods mainly focus on aligning a pair of KGs, …

What to Do Next: Modeling User Behaviors by Time-LSTM

2017

Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users' actions, and RNN has been proved to …

IntentGC

2019

The remarkable progress of network embedding has led to state-of-the-art algorithms in recommendation. However, the sparsity of user-item interactions (i.e., explicit preferences) on websites remains a big challenge for predicting users' behaviors. Although research efforts …