… , Graph Neural Networks (GNNs) have shown great expressive power for learning graph
representations … and challenges of motif learning, we propose MICRO-Graph: a framework for …

… contrastive learning is to sample informative subgraphs that are semantically meaningful.
To solve it, we propose to learn graph … learning, we propose MICRO-Graph: a framework for …

… We propose a MOTIF-driven contrastive framework to pretrain a graph neural network in a
… from large graph datasets. Our framework achieves state-of-the-art results on various graph-…

… -contrastive learning to capture motif regularity in the constructed manifold and learn motif-aware
node representation … Motif-Driven Contrastive Learning of Graph Representations. In …

… Contrastive methods force views from the same graph (eg, sampling nodes and edges …
Graph Self-supervised learning aims to learn the intermediate representations of unlabeled graph …

… MICRO-Graph [33] is a motif-driven contrastive learning approach for pretraining GNNs in
a self-supervised manner. MGSSL [34] incorporates motif generation into self-supervised pre-…

… To solve the second challenge, we propose a motif-driven … a contrastive loss that dynamically
guide structure learning on … GNN f to learn representations for downstream tasks (here …

… across domains for motif-driven self-supervised learning. … training graphs, contrastive learning
aims to learn graph … similar graph instances exhibit concordance while representations of …

… a novel molecular graph representation method that uses motif … mutual information of the
molecular graph. First, the functional … contrastive learning by exploiting both 2D and 3D graph …

… representation learning, we propose a heterogeneous learning module. This module is
designed to learn both … In contrast, our approach offers a unique capability: it can integrate and …