
Heterogeneous Deep Graph Infomax
Graph representation learning is to learn universal node representations...
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Heterogeneous Graph Neural Network for Recommendation
The prosperous development of ecommerce has spawned diverse recommendat...
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Node Similarity Preserving Graph Convolutional Networks
Graph Neural Networks (GNNs) have achieved tremendous success in various...
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GTEA: Representation Learning for Temporal Interaction Graphs via Edge Aggregation
We consider the problem of representation learning for temporal interact...
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Heterogeneous Graph Neural Networks for Malicious Account Detection
We present, GEM, the first heterogeneous graph neural network approach f...
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Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation
Graph Neural Networks (GNNs) are powerful to learn the representation of...
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GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs
Heterogeneous graph representation learning aims to learn lowdimensiona...
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Tree StructureAware 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 metapath 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 manytoone schema, a.k.a. the hierarchical tree structure. But all the above work cannot preserve such tree structure, since the exact multihop path correlation from neighbors to the target node would be erased through aggregation. Therefore, to overcome the limitations of the literature, we propose TGNN, a tree structureaware graph neural network model for graph representation learning. Specifically, the proposed TGNN 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 typespecific space with distinct distribution based on similarity metrics.
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