2019
AAAI
AAAI 2019
Type Sequence Preserving Heterogeneous Information Network Embedding
Abstract
Abstract Lacking in sequence preserving mechanism, existing heterogeneous information network (HIN) embedding discards the essential type sequence information during embedding. We propose a Type Sequence Preserving HIN Embedding model (SeqHINE) which expands the HIN embedding to sequence level. SeqHINE incorporates the type sequence information via type-aware GRU and preserves representative sequence information by decay function. Abundant experiments show that SeqHINE can outperform state-of-the-art even with 50% less labeled data.
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Conference Pioneer
- AAAI 2019
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Interdisciplinary Bridge
- Artificial Intelligence and Knowledge & Reasoning
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Core Methods > Embedding Learning
Deep Learning > Architectures > Neural Networks
Deep Learning > Architectures > Graph Neural Networks
Knowledge & Reasoning > Representation > Knowledge Graphs
Machine Learning > Core Methods > Graph Neural Networks
Deep Learning > Learning Types > Representation Learning