HetFS: A Method for Fast Similarity Search with Ad-hoc Meta-paths on Heterogeneous Information Networks
Xuqi Mao, Zhenyi Chen, Zhenying He, Yinan Jing, Kai Zhang, X. Sean, Wang

TL;DR
HetFS is a fast, flexible similarity search method for heterogeneous information networks that effectively combines path-based and node content information, outperforming existing approaches in accuracy and efficiency for ad-hoc meta-path queries.
Contribution
HetFS introduces a novel similarity search approach that efficiently handles ad-hoc meta-path queries without retraining, improving accuracy over existing path-based methods and HGNNs.
Findings
HetFS outperforms state-of-the-art methods in accuracy and speed.
Effective in downstream tasks like link prediction, node classification, and clustering.
Demonstrates strong performance in ad-hoc query scenarios.
Abstract
Numerous real-world information networks form Heterogeneous Information Networks (HINs) with diverse objects and relations represented as nodes and edges in heterogeneous graphs. Similarity between nodes quantifies how closely two nodes resemble each other, mainly depending on the similarity of the nodes they are connected to, recursively. Users may be interested in only specific types of connections in the similarity definition, represented as meta-paths, i.e., a sequence of node and edge types. Existing Heterogeneous Graph Neural Network (HGNN)-based similarity search methods may accommodate meta-paths, but require retraining for different meta-paths. Conversely, existing path-based similarity search methods may switch flexibly between meta-paths but often suffer from lower accuracy, as they rely solely on path information. This paper proposes HetFS, a Fast Similarity method for…
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Taxonomy
MethodsGraph Neural Network
