Simple contrastive graph clustering

Y Liu, X Yang, S Zhou, X Liu, S Wang… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
Contrastive learning has recently attracted plenty of attention in deep graph clustering due to
its promising performance. However, complicated data augmentations and time-consuming …

Dink-net: Neural clustering on large graphs

Y Liu, K Liang, J Xia, S Zhou, X Yang… - International …, 2023 - proceedings.mlr.press
Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with
deep neural networks, has achieved promising progress in recent years. However, the …

Learn from relational correlations and periodic events for temporal knowledge graph reasoning

K Liang, L Meng, M Liu, Y Liu, W Tu, S Wang… - Proceedings of the 46th …, 2023 - dl.acm.org
Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the
timeline, has been widely studied to alleviate incompleteness issues in TKG, which is …

Substructure aware graph neural networks

D Zeng, W Liu, W Chen, L Zhou, M Zhang… - Proceedings of the AAAI …, 2023 - ojs.aaai.org
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning,
conventional GNNs struggle to break through the upper limit of the expressiveness of first …

Knowledge graph contrastive learning based on relation-symmetrical structure

K Liang, Y Liu, S Zhou, W Tu, Y Wen… - … on Knowledge and …, 2023 - ieeexplore.ieee.org
Knowledge graph embedding (KGE) aims at learning powerful representations to benefit
various artificial intelligence applications. Meanwhile, contrastive learning has been widely …

Deep incomplete multi-view clustering with cross-view partial sample and prototype alignment

J Jin, S Wang, Z Dong, X Liu… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
The success of existing multi-view clustering relies on the assumption of sample integrity
across multiple views. However, in real-world scenarios, samples of multi-view are partially …

Mole-bert: Rethinking pre-training graph neural networks for molecules

J Xia, C Zhao, B Hu, Z Gao, C Tan, Y Liu, S Li, SZ Li - 2023 - chemrxiv.org
Recent years have witnessed the prosperity of pre-training graph neural networks (GNNs)
for molecules. Typically, atom types as node attributes are randomly masked and GNNs are …

Dealmvc: Dual contrastive calibration for multi-view clustering

X Yang, J Jiaqi, S Wang, K Liang, Y Liu, Y Wen… - Proceedings of the 31st …, 2023 - dl.acm.org
Benefiting from the strong view-consistent information mining capacity, multi-view
contrastive clustering has attracted plenty of attention in recent years. However, we observe …

A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource

Y Liu, J Xia, S Zhou, X Yang, K Liang, C Fan… - arXiv preprint arXiv …, 2022 - arxiv.org
Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …

Convert: Contrastive graph clustering with reliable augmentation

X Yang, C Tan, Y Liu, K Liang, S Wang… - Proceedings of the 31st …, 2023 - dl.acm.org
Contrastive graph node clustering via learnable data augmentation is a hot research spot in
the field of unsupervised graph learning. The existing methods learn the sampling …