Graph representation learning aims to learn low-dimensional node embeddings for graphs . It is used in several real-world applications such as social network analysis and large-scale recommender systems. In this paper, we introduce CogDL, an extensive research toolkit for deep learning on graphs that allows researchers and developers to easily conduct experiments and build applications. It provides standard training and evaluation for the most important tasks in the graph domain, including node classification, link prediction, graph classification, and other graph tasks . For each task, it offers implementations of state-of-the-art models. The models in our toolkit are divided into two major parts, graph embedding methods and graph neural networks. Most of the graph embedding methods learn node-level or graph-level representations in an unsupervised way and preserves the graph properties such as structural information, while graph neural networks capture node features and work in semi-supervised or self-supervised settings. All models implemented in our toolkit can be easily reproducible for leaderboard results. Most models in CogDL are developed on top of PyTorch, and users can leverage the advantages of PyTorch to implement their own models. Furthermore, we demonstrate the effectiveness of CogDL for real-world applications in AMiner, which is a large academic database and system.
Deep learning on graphs has attracted tremendous attention from the graph learning community in recent years. It has been widely used in several real-world applications such as social network analysis and recommender systems. In this paper, we introduce CogDL, an extensive toolkit for deep learning on graphs that allows researchers and developers to easily conduct experiments and build applications. It provides standard training and evaluation for the most important tasks in the graph domain, including node classification, graph classification, etc . For each task, it provides implementations of state-of-the-art models. The models in our toolkit are divided into two major parts, graph embedding methods and graph neural networks. Most of the graph embedding methods learn node-level or graph-level representations in an unsupervised way and preserves the graph properties such as structural information, while graph neural networks capture node features and work in semi-supervised or self-supervised settings. All models implemented in our toolkit can be easily reproducible for leaderboard results. Most models in CogDL are developed on top of PyTorch, and users can leverage the advantages of PyTorch to implement their own models. Furthermore, we demonstrate the effectiveness of CogDL for real-world applications in AMiner, a large academic mining system.
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Graph representation learning aims to learn low-dimensional node embeddings for graphs . It is
Deep learning on graphs has attracted tremendous attention from the graph learning community in recent years. It has been widely
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link prediction,
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and other graph tasks
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database and
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