In 2011, we proposed PathSim to systematically define and compute similarity between nodes in a heterogeneous information network (HIN), where nodes and links are from different types. In the PathSim paper, we for the first time introduced HIN with general network schema and proposed the concept of meta-paths to systematically define new relation types between nodes. In this paper, we summarize the impact of PathSim paper in both academia and industry. We start from the algorithms that are based on meta-path-based feature engineering, then move on to the recent development in heterogeneous network representation learning, including both shallow network embedding and heterogeneous graph neural networks. In the end, we make the connection between knowledge graphs and HINs and discuss the implication of meta-paths in the symbolic reasoning scenario. Finally, we point out several future directions.
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HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
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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- Award ID(s):
- 1717084
- NSF-PAR ID:
- 10065397
- Date Published:
- Journal Name:
- Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
- Page Range / eLocation ID:
- 1797 to 1806
- 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/10.1145/3132847.3132953
