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Random initialization in pre-trained GNN

3 minute read

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Summary: The random initialized embeddings is equivalent to multiple a random matrix to the original feature matrix. The value of each dimension in generated embeddings are the weighted summation of values of each dimension in original features. In the random initialization, the embeddings are still distinguishable since the ‘weights’ are randomly generated and not identical. In ogb datasets with enough training data, the downstream classifier can still fit it.

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publications

Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework

Zhenshuo Zhang, Yun Zhu, Haizhou Shi, Siliang Tang

European Conference on Artificial Intelligence, ECAI, 2023

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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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