103 citations · 173 across the 12 of their papers we have counts for
8 papers · 1 filter
Towards Open Temporal Graph Neural Networks
Kaituo Feng, Changsheng Li, Xiaolu Zhang +1
Graph neural networks (GNNs) for temporal graphs have recently attracted increasing attentions, where a common assumption is that the class set for nodes is closed. However, in rea…
Robust Knowledge Adaptation for Dynamic Graph Neural Networks
Hanjie Li, Changsheng Li, Kaituo Feng +3
Graph structured data often possess dynamic characters in nature. Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling graph da…
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks
Kaituo Feng, Changsheng Li, Ye Yuan +1
Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teache…
Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering
Shiye Wang, Changsheng Li, Yanming Li +2
In this paper, we explore the problem of deep multi-view subspace clustering framework from an information-theoretic point of view. We extend the traditional information bottleneck…
Deep Unsupervised Active Learning on Learnable Graphs
Handong Ma, Changsheng Li, Xinchu Shi +2
Recently deep learning has been successfully applied to unsupervised active learning. However, current method attempts to learn a nonlinear transformation via an auto-encoder while…
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering
Yanming Li, Changsheng Li, Shiye Wang +2
Deep subspace clustering has attracted increasing attention in recent years. Almost all the existing works are required to load the whole training data into one batch for learning…
On Deep Unsupervised Active Learning
Changsheng Li, Handong Ma, Zhao Kang +3
Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating.…
Similarity Learning via Kernel Preserving Embedding
Zhao Kang, Yiwei Lu, Yuanzhang Su +2
Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning…