A semantic-rich similarity measure in heterogeneous information networks

Zhou, Yu; Huang, Jianbin; Li, He; Sun, Heli; Peng, Yan; Xu, Yueshen

doi:10.1016/j.knosys.2018.05.010

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…Due to the high computational cost of subgraph matching, they propose efficient indices and matching algorithms for similarity computation. Zhou et al [235] propose a stratified meta-structure which can be constructed automatically and capture rich semantics. They design a similarity measure called SMSS based on stratified meta-structure.…”

Section: Other Path-based Methodsmentioning

confidence: 99%

A survey of typical attributed graph queries

Wang

Fan

et al. 2020

World Wide Web

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Graphs are commonly used for representing complex structures such as social relationships, biological interactions, and knowledge bases. In many scenarios, graphs not only represent topological relationships but also store the attributes that denote the semantics associated with their vertices and edges, known as attributed graphs. Attributed graphs can meet demands for a wide range of applications, and thus a variety of queries on attributed graphs have been proposed. However, these diverse types of attributed graph queries have not been systematically investigated yet. In this paper, we provide an extensive survey of several typical types of attributed graph queries. We propose a taxonomy of attributed graph queries based on query inputs and outputs. We summarize the definitions of queries that fall into each category and present a fine-grained classification of queries within each category by analyzing the semantics and algorithmic motivations behind these queries. Moreover, we discuss the insights of how existing studies address the technical challenges of query processing and outline several promising future research directions.

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Section: Other Path-based Methodsmentioning

confidence: 99%

A survey of typical attributed graph queries

Wang

Fan

et al. 2020

World Wide Web

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“…For high‐order heterogeneous networks clustering, methods have different emphasis. Some work focus on direct link (Yin, Han, & Yu, 2005), and some work dig into the similarity measurement in heterogeneous networks (Yin, Han, & Yu, 2006; Zhang, Wang, & Wang, 2018; Zhao, Han, & Sun, 2009; Zhou et al, 2018). Some work integrate clustering with ranking for heterogeneous networks (Sun, Han, Zhao, Yin, Chen, & Wu, 2009; Cao et al, 2012; Tang & Lin, 2018).…”

Section: Related Workmentioning

confidence: 99%

Incorporating domain ontology information into clustering in heterogeneous networks

Huang

2021

WIREs Data Min & Knowl

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Clustering of structure‐rich heterogeneous information networks composed of multiple types of objects and relationships, which has become a challenge in data mining. Most of the existing clustering heterogeneous network methods focus on the internal information of the dataset while ignoring the domain knowledge outside the dataset. However, in real‐world scenarios, domain knowledge can often offer valuable information for clustering. In this study, we propose a three‐layer model OntoHeteClus, which is able to cluster multitype objects in star‐structured heterogeneous networks by considering both the dataset itself and the background information quantified via the ontology. OntoHeteClus first evaluates the similarity between central objects according to formalized domain ontology information, based on which central objects are subsequently clustered. Finally, attribute objects are clustered according to the central object clustering result. A numerical example is presented to illustrate the modeling concept and working principle of the proposed method, and experiments on a real‐world dataset demonstrate the effectiveness of the proposed algorithms. This article is categorized under: Technologies > Structure Discovery and Clustering Algorithmic Development > Structure Discovery

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Stratified Meta Structure Based Similarity Measure in Heterogeneous Information Networks for Medical Diagnosis

Gireesan¹,

Mathew²

2020

Computational Vision and Bio-Inspired Computing

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A semantic-rich similarity measure in heterogeneous information networks

Cited by 8 publications

References 24 publications

A survey of typical attributed graph queries

A survey of typical attributed graph queries

Incorporating domain ontology information into clustering in heterogeneous networks

Stratified Meta Structure Based Similarity Measure in Heterogeneous Information Networks for Medical Diagnosis

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