Distance-Aware DAG Embedding for Proximity Search on Heterogeneous Graphs

By Zemin Liu, Vincent Wenchen Zheng, Zhou Zhao, Fanwei Zhu, Kevin Chen-Chuan Chang, Minghui Wu, Jing Ying. 2018

35 citations recorded by the source corpus.

Proximity search on heterogeneous graphs aims to measure the proximity between two nodes on a graph w.r.t. some semantic relation for ranking. Pioneer work often tries to measure such proximity by paths connecting the two nodes. However, paths as linear sequences have limited expressiveness for the complex network connections. In this paper, we explore a more expressive DAG (directed acyclic graph) data structure for modeling the connections between two nodes. Particularly, we are interested in learning a representation for the DAGs to encode the proximity between two nodes. We face two challenges to use DAGs, including how to efficiently generate DAGs and how to effectively learn DAG embedding for proximity search. We find distance-awareness as important for proximity search and the key to solve the above challenges. Thus we develop a novel Distance-aware DAG Embedding (D2AGE) model. We evaluate D2AGE on three benchmark data sets with six semantic relations, and we show that D2AGE outperforms the state-of-the-art baselines. We release the code on https://github.com/shuaiOKshuai.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing

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Citations in the corpus

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “D2AGE [152] solves the same problem by embed-”
Subgraph-augmented Path Embedding for Semantic User Search on Heterogeneous Social Network (2018, background): “Similarly, although D2AGE [21] manages to model multiple object paths as one directed acyclic graph for proximity embedding, it is also unable to leverage the offline mined subgraphs.”
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources (2020, unknown): “D2AGE [110] explores the DAG structure for better measuring the similarity between two”
Network Representation Learning: A Survey (2017, background): “theway for awide range of network analytic tasks to be easily and efficiently tackled in the new vector space, such as node classification [10], [11], link prediction [12], [13], clustering [2], recommendation [14], [15], similarity search [16], and visualization [17].”