1 code implementation • 18 Oct 2023 • Junjun Pan, Yixin Liu, Yizhen Zheng, Shirui Pan
Comprising two modules - a pre-processing module and an ego-neighbor matching module - PREM eliminates the necessity for message-passing propagation during training, and employs a simple contrastive loss, leading to considerable reductions in training time and memory usage.
1 code implementation • 12 Oct 2023 • Yizhen Zheng, Huan Yee Koh, Jiaxin Ju, Anh T. N. Nguyen, Lauren T. May, Geoffrey I. Webb, Shirui Pan
We present a method for using general-purpose large language models to make inferences from scientific datasets of the form usually associated with special-purpose machine learning algorithms.
no code implementations • 9 Oct 2023 • Shirui Pan, Yizhen Zheng, Yixin Liu
Large language models (LLMs) such as GPT-4 have emerged as frontrunners, showcasing unparalleled prowess in diverse applications, including answering queries, code generation, and more.
1 code implementation • 13 Jun 2023 • Yizhen Zheng, He Zhang, Vincent CS Lee, Yu Zheng, Xiao Wang, Shirui Pan
Real-world graphs generally have only one kind of tendency in their connections.
no code implementations • 1 Jun 2023 • Di Jin, Luzhi Wang, He Zhang, Yizhen Zheng, Weiping Ding, Feng Xia, Shirui Pan
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era.
1 code implementation • 10 May 2023 • Di Jin, Luzhi Wang, Yizhen Zheng, Guojie Song, Fei Jiang, Xiang Li, Wei Lin, Shirui Pan
We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item.
1 code implementation • 25 Nov 2022 • Yixin Liu, Yizhen Zheng, Daokun Zhang, Vincent CS Lee, Shirui Pan
Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges.
1 code implementation • 17 Oct 2022 • Yizhen Zheng, Yu Zheng, Xiaofei Zhou, Chen Gong, Vincent CS Lee, Shirui Pan
To address aforementioned problems, we present a simple self-supervised learning method termed Unifying Graph Contrastive Learning with Flexible Contextual Scopes (UGCL for short).
1 code implementation • 3 Jun 2022 • Yizhen Zheng, Shirui Pan, Vincent CS Lee, Yu Zheng, Philip S. Yu
Instead of similarity computation, GGD directly discriminates two groups of node samples with a very simple binary cross-entropy loss.
1 code implementation • 30 May 2022 • Di Jin, Luzhi Wang, Yizhen Zheng, Xiang Li, Fei Jiang, Wei Lin, Shirui Pan
As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score.
no code implementations • 20 Nov 2021 • Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, Zhao Li
To overcome the aforementioned problems, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.
1 code implementation • 12 May 2021 • Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan
To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.