Results 231 to 240 of about 533,476 (267)

Graph Representation Learning

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
Graphs such as social networks and molecular graphs are ubiquitous data structures in the real world. Due to their prevalence, it is of great research importance to extract meaningful patterns from graph structured data so that downstream tasks can be facilitated.
Wei Jin, Yao Ma, Yiqi Wang, Xiaorui Liu, Jiliang Tang, Yukuo Cen, Jiezhong Qiu, Jie Tang, Chuan Shi, Yanfang Ye, Jiawei Zhang, Philip S. Yu   +11 more
openaire   +1 more source

Graph explicit pooling for graph-level representation learning

Neural Networks
Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages: selecting top-ranked nodes and discarding the remaining to construct coarsened graph representations.
Chuang Liu, Wenhang Yu, Kuang Gao, Xueqi Ma, Yibing Zhan, Jia Wu, Wenbin Hu, Bo Du   +7 more
openaire   +2 more sources

Representation Learning on Graphs

2018
The primary challenge of applying machine learning in graph theory is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph.
Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa   +2 more
openaire   +1 more source

GRLC: Graph Representation Learning With Constraints

IEEE Transactions on Neural Networks and Learning Systems
Contrastive learning has been successfully applied in unsupervised representation learning. However, the generalization ability of representation learning is limited by the fact that the loss of downstream tasks (e.g., classification) is rarely taken into account while designing contrastive methods.
Liang Peng, Yujie Mo, Jie Xu, Jialie Shen, Xiaoshuang Shi, Xiaoxiao Li, Heng Tao Shen, Xiaofeng Zhu   +7 more
openaire   +2 more sources

Topological Graph Representation Learning on Property Graph

2020
Property graph representation learning is using the property features from the graph to build the embeddings over the nodes and edges. There are many graph application tasks are using the property graph representation learning as part of the process. However, existing methods on Property graph representation learning ignore either the property features
Yishuo Zhang, Daniel Gao, Aswani Kumar Cherukuri, Lei Wang, Shaowei Pan, Shu Li   +5 more
openaire   +1 more source

AGCL: Adaptive Graph Contrastive Learning for graph representation learning

Neurocomputing, 2023
Jiajun Yu, Adele Lu Jia
openaire   +1 more source

Graph Joint Representation Clustering via Penalized Graph Contrastive Learning

IEEE Transactions on Neural Networks and Learning Systems
Graph clustering based on graph contrastive learning (GCL) is one of the dominant paradigms in the current graph clustering research field. However, those GCL-based methods often yield false negative samples, which can distort the learned representations and limit clustering performance.
Zihua Zhao, Rong Wang, Zheng Wang, Feiping Nie, Xuelong Li   +4 more
openaire   +2 more sources

Integrative oncology: Addressing the global challenges of cancer prevention and treatment

Ca-A Cancer Journal for Clinicians, 2022
Jun J Mao,, Msce, Lorenzo Cohen, Karen M Mustian   +2 more
exaly  

Representation Learning on Graphs

2021
Łukasz Brzozowski, Siemaszko, Kacper
openaire   +1 more source

Graph Representation Learning

2020
openaire   +1 more source