Feb 23, 2021 · Inspired by our analysis, we propose two techniques, Dynamic Labeling and Preferential Dynamic Labeling, that satisfy desired properties ...

Inspired by the analysis of existing approaches in two types of classification tasks, Dynamic Labeling and Preferential Dynamic La-beling, two techniques are ...

Figure 2 shows an overview of our approach. The basic idea is to assign embeddings by randomly labeling the nodes, and to use an ensemble of different node ...

Nov 9, 2022 · In this paper, we propose a novel informative pseudo-labeling framework (InfoGNN) to facilitate learning of GNNs with very few labels.

Relation updates: The edges between the users may be labeled where the label indicates the type of the connection, e.g., friendship, engagement, and siblings.

Feb 23, 2021 · Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content.

Oct 21, 2024 · Label propagation is based on the idea that if two nodes are connected by a strong edge, they probably share the same label.

Nov 6, 2018 · In this paper, we propose DGNN, a new Dynamic Graph Neural Network model, which can model the dynamic information as the graph evolving. In ...

This paper presents a novel Logic-Informed Graph Neural Network (LIGNN) that integrates the validity conditions of CEM topology diagrams into the learning ...

When no labeled data is available, SSL serves as an approach to learn representations from unlabeled data itself. When a limited number of labeled data is ...