Network embedding has been an effective tool to analyze heterogeneous networks (HNs) by representing nodes in a low-dimensional space. Although many recent methods have been proposed for representation learning of HNs, there is still much room for improvement. Random walks based methods are currently popular methods to learn network embedding; however, they are random and limited by the length of sampled walks, and have dif culty capturing network structural information. Some recent researches proposed using meta paths to express the sample relationship in HNs. Another popular graph learning model, the graph convolutional network (GCN) is known to be capable of better exploitation of network topology, but the current design of GCN is intended for homogenous networks. This paper proposes a novel combination of meta-graph and graph convolution, the meta-graph based graph convolutional networks (MGCN). To fully
capture the complex long semantic information, MGCN utilizes different meta-graphs in HNs. As different meta-graphs express different semantic relationships, MGCN learns the weights of different meta-graphs to make up for the loss of semantics when applying GCN. In addition, we improve the current convolution design by adding node self-signi cance. To validate our model in learning feature representation, we present comprehensive experiments on four real-world datasets and two representation tasks: classi cation and link prediction. WMGCN's representations can improve accuracy scores by up to around 10% in comparison to other popular representation learning models. What's more, WMGCN'feature learning outperforms other popular baselines. The experimental results clearly show our model is superior over other state-of-the-art representation learning algorithms.
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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
