In this paper, we propose a novel representation learning framework,
namely HIN2Vec, for heterogeneous information networks
(HINs). The core of the proposed framework is a neural network
model, also called HIN2Vec, designed to capture the rich semantics
embedded in HINs by exploiting different types of relationships
among nodes. Given a set of relationships specified in forms
of meta-paths in an HIN, HIN2Vec carries out multiple prediction
training tasks jointly based on a target set of relationships to learn
latent vectors of nodes and meta-paths in the HIN. In addition to
model design, several issues unique to HIN2Vec, including regularization
of meta-path vectors, node type selection in negative sampling,
and cycles in random walks, are examined. To validate our
ideas, we learn latent vectors of nodes using four large-scale real
HIN datasets, including Blogcatalog, Yelp, DBLP and U.S. Patents,
and use them as features for multi-label node classification and
link prediction applications on those networks. Empirical results
show that HIN2Vec soundly outperforms the state-of-the-art representation
learning models for network data, including DeepWalk,
LINE, node2vec, PTE, HINE and ESim, by 6.6% to 23.8% ofmicro-f1
in multi-label node classification and 5% to 70.8% of MAP in link
prediction.
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Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. However, the heterogeneity in HINs introduces not only rich information but also potentially incompatible semantics, which poses special challenges to embedding learning in HINs. With the intention to preserve the rich yet potentially incompatible information in HIN embedding, we propose to study the problem of comprehensive transcription of heterogeneous information networks. The comprehensive transcription of HINs also provides an easy-to-use approach to unleash the power of HINs, since it requires no additional supervision, expertise, or feature engineering. To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics. To corroborate the efficacy of HEER, we conducted experiments on two large-scale real-words datasets with an edge reconstruction task and multiple case studies. Experiment results demonstrate the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneous metrics. The code and data are available at https://github.com/GentleZhu/HEER.
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- NSF-PAR ID:
- 10079173
- Date Published:
- Journal Name:
- Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018
- Volume:
- 2018
- Issue:
- 1
- Page Range / eLocation ID:
- 2190 to 2199
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3219819.3220006
