research-article

Interactive Paths Embedding for Semantic Proximity Search on Heterogeneous Graphs

Authors:

Vincent W. Zheng,

Jing YingAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 1860 - 1869

https://doi.org/10.1145/3219819.3219953

Published: 19 July 2018 Publication History

Abstract

Semantic proximity search on heterogeneous graph is an important task, and is useful for many applications. It aims to measure the proximity between two nodes on a heterogeneous graph w.r.t. some given semantic relation. Prior work often tries to measure the semantic proximity by paths connecting a query object and a target object. Despite the success of such path-based approaches, they often modeled the paths in a weakly coupled manner, which overlooked the rich interactions among paths. In this paper, we introduce a novel concept of interactive paths to model the inter-dependency among multiple paths between a query object and a target object. We then propose an Interactive Paths Embedding (IPE) model, which learns low-dimensional representations for the resulting interactive-paths structures for proximity estimation. We conduct experiments on seven relations with four different types of heterogeneous graphs, and show that our model outperforms the state-of-the-art baselines.

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Index Terms

Interactive Paths Embedding for Semantic Proximity Search on Heterogeneous Graphs
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Statistical relational learning

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KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 19 July 2018

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National Natural Science Foundation of China
Zhejiang Science and Technology Plan Project
National Research Foundation Prime Minister's Office Singapore under its Campus for Research Excellence and Technological Ente
Alibaba Innovative Research program

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KDD '18

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://doi.org/10.1038/s41598-024-54376-3
Liu JXia FRen JXu BPang GChi L(2023)MIRROR: Mining Implicit Relationships via Structure-Enhanced Graph Convolutional NetworksACM Transactions on Knowledge Discovery from Data10.1145/356453117:4(1-24)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3564531
Ding PWang YLiu GZhou X(2023)Few-Shot Semantic Relation Prediction Across Heterogeneous GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325195135:10(10265-10280)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TKDE.2023.3251951
Wang XBo DShi CFan SYe YYu P(2023)A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and SourcesIEEE Transactions on Big Data10.1109/TBDATA.2022.31774559:2(415-436)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3177455
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Jin JDu KZhang WQin JFang YYu YZhang ZSmola A(2022)GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information NetworkACM Transactions on Information Systems10.1145/347295640:3(1-35)Online publication date: 24-Mar-2022
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Yang CXiao YZhang YSun YHan J(2022)Heterogeneous Network Representation Learning: A Unified Framework With Survey and BenchmarkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.304592434:10(4854-4873)Online publication date: 1-Oct-2022
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Wang XLu YShi CWang RCui PMou S(2022)Dynamic Heterogeneous Information Network Embedding With Meta-Path Based ProximityIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299387034:3(1117-1132)Online publication date: 1-Mar-2022
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Zhang WFang YLiu ZWu MZhang X(2022)mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations via Metagraph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299250034:3(1317-1329)Online publication date: 1-Mar-2022
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