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Erschienen in:
A Machine Learning Perspective on Big Data Analysis Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Analysis of Text-Enriched Heterogeneous Information Networks

verfasst von : Jan Kralj, Anita Valmarska, Miha Grčar, Marko Robnik-Šikonja, Nada Lavrač

Erschienen in: Big Data Analysis: New Algorithms for a New Society

Verlag: Springer International Publishing

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Abstract

This chapter addresses the analysis of information networks, focusing on heterogeneous information networks with more than one type of nodes and arcs. After an overview of tasks and approaches to mining heterogeneous information networks, the presentation focuses on text-enriched heterogeneous information networks whose distinguishing property is that certain nodes are enriched with text information. A particular approach to mining text-enriched heterogeneous information networks is presented that combines text mining and network mining approaches. The approach decomposes a heterogeneous network into separate homogeneous networks, followed by concatenating the structural context vectors calculated from separate homogeneous networks with the bag-of-words vectors obtained from textual information contained in certain network nodes. The approach is show-cased on the analysis of two real-life text-enriched heterogeneous citation networks.

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Vorheriges Kapitel An Overview of Concept Drift Applications
Nächstes Kapitel Implementing Big Data Analytics Projects in Business
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Metadaten
Titel
Analysis of Text-Enriched Heterogeneous Information Networks
verfasst von
Jan Kralj
Anita Valmarska
Miha Grčar
Marko Robnik-Šikonja
Nada Lavrač
Copyright-Jahr
2016
Buch
Big Data Analysis: New Algorithms for a New Society

Print ISBN: 978-3-319-26987-0

Electronic ISBN: 978-3-319-26989-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-26989-4_5

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