Few-Shot Semantic Relation Prediction Across Heterogeneous Graphs

نویسندگان

چکیده

Semantic relation prediction aims to mine the implicit relationships between objects in heterogeneous graphs, which consist of different types and links. In real-world scenarios, new semantic relations constantly emerge they typically appear with only a few labeled data. Since variety exist multiple transferable knowledge can be mined from some existing help predict This inspires novel problem few-shot across graphs. However, methods cannot solve this because not require large number samples as input, but also focus on single graph fixed heterogeneity. Targeting challenging problem, paper, we propose Meta-learning based Graph neural network for prediction, named MetaGS. Firstly, MetaGS decomposes structure into normalized subgraphs, then adopts two-view capture local information global these subgraphs. Secondly, aggregates subgraphs hyper-prototypical network, learn adapt relations. Thirdly, using well-initialized effectively graphs while overcoming limitation Extensive experiments three datasets have demonstrated superior performance over state-of-the-art methods.

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ژورنال

عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering

سال: 2023

ISSN: ['1558-2191', '1041-4347', '2326-3865']

DOI: https://doi.org/10.1109/tkde.2023.3251951