Topology-aware Tensor Decomposition for Meta-graph Learning
- URL: http://arxiv.org/abs/2101.01078v2
- Date: Fri, 1 Sep 2023 10:49:08 GMT
- Title: Topology-aware Tensor Decomposition for Meta-graph Learning
- Authors: Hansi Yang and Peiyu Zhang and Quanming Yao
- Abstract summary: A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs.
We propose a new viewpoint from tensor on learning meta-graphs.
We also propose a topology-aware tensor decomposition, called TENSUS, that reflects the structure of DAGs.
- Score: 33.70569156426479
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Heterogeneous graphs generally refers to graphs with different types of nodes
and edges. A common approach for extracting useful information from
heterogeneous graphs is to use meta-graphs, which can be seen as a special kind
of directed acyclic graph (DAG) with same node and edge types as the
heterogeneous graph. However, how to design proper meta-graphs is challenging.
Recently, there have been many works on learning suitable meta-graphs from a
heterogeneous graph. Existing methods generally introduce continuous weights
for edges that are independent of each other, which ignores the topological
stucture of meta-graphs and can be ineffective. To address this issue, we
propose a new viewpoint from tensor on learning meta-graphs. Such a viewpoint
not only helps interpret the limitation of existing works by CANDECOMP/PARAFAC
(CP) decomposition, but also inspires us to propose a topology-aware tensor
decomposition, called TENSUS, that reflects the structure of DAGs. The proposed
topology-aware tensor decomposition is easy to use and simple to implement, and
it can be taken as a plug-in part to upgrade many existing works, including
node classification and recommendation on heterogeneous graphs. Experimental
results on different tasks demonstrate that the proposed method can
significantly improve the state-of-the-arts for all these tasks.
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