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Latest papers with code

Structure-Enhanced Meta-Learning For Few-Shot Graph Classification

5 Mar 2021jiangshunyu/SMF-GIN

Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction. Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage. This work explores the potential of metric-based meta-learning for solving few-shot graph classification. We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph.

CLASSIFICATION GRAPH CLASSIFICATION META-LEARNING MOLECULAR PROPERTY PREDICTION PROTEIN FUNCTION PREDICTION

0
05 Mar 2021

Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations

12 Jun 2019xiangyue9607/BioNEV

Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.

GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION PROTEIN FUNCTION PREDICTION

160
12 Jun 2019

Strategies for Pre-training Graph Neural Networks

ICLR 2020 snap-stanford/pretrain-gnns

Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.

GRAPH CLASSIFICATION MOLECULAR PROPERTY PREDICTION PROTEIN FUNCTION PREDICTION REPRESENTATION LEARNING

358
29 May 2019