Connector 0.5: A unified framework for graph representation learning
- URL: http://arxiv.org/abs/2304.13195v1
- Date: Tue, 25 Apr 2023 23:28:38 GMT
- Title: Connector 0.5: A unified framework for graph representation learning
- Authors: Thanh Sang Nguyen, Jooho Lee, Van Thuy Hoang, O-Joun Lee
- Abstract summary: We introduce a novel graph representation framework covering various graph embedding models, ranging from shallow to state-of-the-art models.
We plan to build an efficient open-source framework that can provide deep graph embedding models to represent structural relations in graphs.
- Score: 5.398580049917152
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Graph representation learning models aim to represent the graph structure and
its features into low-dimensional vectors in a latent space, which can benefit
various downstream tasks, such as node classification and link prediction. Due
to its powerful graph data modelling capabilities, various graph embedding
models and libraries have been proposed to learn embeddings and help
researchers ease conducting experiments. In this paper, we introduce a novel
graph representation framework covering various graph embedding models, ranging
from shallow to state-of-the-art models, namely Connector. First, we consider
graph generation by constructing various types of graphs with different
structural relations, including homogeneous, signed, heterogeneous, and
knowledge graphs. Second, we introduce various graph representation learning
models, ranging from shallow to deep graph embedding models. Finally, we plan
to build an efficient open-source framework that can provide deep graph
embedding models to represent structural relations in graphs. The framework is
available at https://github.com/NSLab-CUK/Connector.
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