Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tree Structure-Aware Graph Representation Learning via Integrated
  Hierarchical Aggregation and Relational Metric Learning   While Graph Neural Network (GNN) has shown superiority in learning node
representations of homogeneous graphs, leveraging GNN on heterogeneous graphs
remains a challenging problem. The dominating reason is that GNN learns node
representations by aggregating neighbors' information regardless of node types.
Some work is proposed to alleviate such issue by exploiting relations or
meta-path to sample neighbors with distinct categories, then use attention
mechanism to learn different importance for different categories. However, one
limitation is that the learned representations for different types of nodes
should own different feature spaces, while all the above work still project
node representations into one feature space. Moreover, after exploring massive
heterogeneous graphs, we identify a fact that multiple nodes with the same type
always connect to a node with another type, which reveals the many-to-one
schema, a.k.a. the hierarchical tree structure. But all the above work cannot
preserve such tree structure, since the exact multi-hop path correlation from
neighbors to the target node would be erased through aggregation. Therefore, to
overcome the limitations of the literature, we propose T-GNN, a tree
structure-aware graph neural network model for graph representation learning.
Specifically, the proposed T-GNN consists of two modules: (1) the integrated
hierarchical aggregation module and (2) the relational metric learning module.
The integrated hierarchical aggregation module aims to preserve the tree
structure by combining GNN with Gated Recurrent Unit to integrate the
hierarchical and sequential neighborhood information on the tree structure to
node representations. The relational metric learning module aims to preserve
the heterogeneity by embedding each type of nodes into a type-specific space
with distinct distribution based on similarity metrics.

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The categories of this paper are: hep-th
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The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: nlin
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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph