GCN for HIN via Implicit Utilization of Attention and Meta-paths

by   Di Jin, et al.

Heterogeneous information network (HIN) embedding, aiming to map the structure and semantic information in a HIN to distributed representations, has drawn considerable research attention. Graph neural networks for HIN embeddings typically adopt a hierarchical attention (including node-level and meta-path-level attentions) to capture the information from meta-path-based neighbors. However, this complicated attention structure often cannot achieve the function of selecting meta-paths due to severe overfitting. Moreover, when propagating information, these methods do not distinguish direct (one-hop) meta-paths from indirect (multi-hop) ones. But from the perspective of network science, direct relationships are often believed to be more essential, which can only be used to model direct information propagation. To address these limitations, we propose a novel neural network method via implicitly utilizing attention and meta-paths, which can relieve the severe overfitting brought by the current over-parameterized attention mechanisms on HIN. We first use the multi-layer graph convolutional network (GCN) framework, which performs a discriminative aggregation at each layer, along with stacking the information propagation of direct linked meta-paths layer-by-layer, realizing the function of attentions for selecting meta-paths in an indirect way. We then give an effective relaxation and improvement via introducing a new propagation operation which can be separated from aggregation. That is, we first model the whole propagation process with well-defined probabilistic diffusion dynamics, and then introduce a random graph-based constraint which allows it to reduce noise with the increase of layers. Extensive experiments demonstrate the superiority of the new approach over state-of-the-art methods.


Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs

Heterogeneous graph neural networks aim to discover discriminative node ...

Interpretable and Efficient Heterogeneous Graph Convolutional Network

Graph Convolutional Network (GCN) has achieved extraordinary success in ...

Hierarchical Opacity Propagation for Image Matting

Natural image matting is a fundamental problem in computational photogra...

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

Meta-graph is currently the most powerful tool for similarity search on ...

HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding

Given the intractability of large scale HIN, network embedding which lea...

Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs

Graph Convolutional Networks (GCNs) have gained significant developments...

Local Neighbor Propagation Embedding

Manifold Learning occupies a vital role in the field of nonlinear dimens...

Please sign up or login with your details

Forgot password? Click here to reset