research-article

Modeling and Mining Feature-Rich Networks

Authors:

Rushed Kanawati,

Martin AtzmuellerAuthors Info & Claims

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Pages 1306 - 1307

https://doi.org/10.1145/3308560.3320098

Published: 13 May 2019 Publication History

Abstract

In the field of web mining and web science, as well as data science and data mining there has been a lot of interest in the analysis of (social) networks. With the growing complexity of heterogeneous data, feature-rich networks have emerged as a powerful modeling approach: They capture data and knowledge at different scales from multiple heterogeneous data sources, and allow the mining and analysis from different perspectives. The challenge is to devise novel algorithms and tools for the analysis of such networks.

This tutorial provides a unified perspective on feature-rich networks, focusing on different modeling approaches, in particular multiplex and attributed networks. It outlines important principles, methods, tools and future research directions in this emerging field.

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Cited By

Ahmed MWilming NAtzmueller M(2023)Graph Neural Network-Based Measurement Inference on Irregular Sensor Geometries2023 IEEE 21st International Conference on Industrial Informatics (INDIN)10.1109/INDIN51400.2023.10218172(1-8)Online publication date: 18-Jul-2023
https://doi.org/10.1109/INDIN51400.2023.10218172
Atzmueller MKanawati R(2022)Explainability in Cyber Security using Complex Network Analysis: A Brief Methodological OverviewProceedings of the 2022 European Interdisciplinary Cybersecurity Conference10.1145/3528580.3532839(49-52)Online publication date: 15-Jun-2022
https://dl.acm.org/doi/10.1145/3528580.3532839
Guven CSeipel DAtzmueller M(2021)Applying ASP for Knowledge-Based Link Prediction With Explanation Generation in Feature-Rich NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2020.30475808:2(1305-1315)Online publication date: 1-Apr-2021
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cover image ACM Other conferences

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

May 2019

1331 pages

ISBN:9781450366755

DOI:10.1145/3308560

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 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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IW3C2: International World Wide Web Conference Committee

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Association for Computing Machinery

New York, NY, United States

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Published: 13 May 2019

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WWW '19

WWW '19: The Web Conference

May 13 - 17, 2019

San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Ahmed MWilming NAtzmueller M(2023)Graph Neural Network-Based Measurement Inference on Irregular Sensor Geometries2023 IEEE 21st International Conference on Industrial Informatics (INDIN)10.1109/INDIN51400.2023.10218172(1-8)Online publication date: 18-Jul-2023
https://doi.org/10.1109/INDIN51400.2023.10218172
Atzmueller MKanawati R(2022)Explainability in Cyber Security using Complex Network Analysis: A Brief Methodological OverviewProceedings of the 2022 European Interdisciplinary Cybersecurity Conference10.1145/3528580.3532839(49-52)Online publication date: 15-Jun-2022
https://dl.acm.org/doi/10.1145/3528580.3532839
Guven CSeipel DAtzmueller M(2021)Applying ASP for Knowledge-Based Link Prediction With Explanation Generation in Feature-Rich NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2020.30475808:2(1305-1315)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TNSE.2020.3047580
Tsitseklis KKrommyda MKaryotis VKantere VPapavassiliou S(2021)Scalable Community Detection for Complex Data Graphs via Hyperbolic Network Embedding and Graph DatabasesIEEE Transactions on Network Science and Engineering10.1109/TNSE.2020.30222488:2(1269-1282)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TNSE.2020.3022248
Castiglione ACozzolino GMoscato FMoscato V(2021)Cognitive Analysis in Social Networks for Viral MarketingIEEE Transactions on Industrial Informatics10.1109/TII.2020.302601317:9(6162-6169)Online publication date: Sep-2021
https://doi.org/10.1109/TII.2020.3026013
Schoenke JAschenbruck NInterdonato RKanawati RMeisener AThierart FVial GAtzmueller M(2021)Gaia-AgStream: An Explainable AI Platform for Mining Complex Data Streams in AgricultureSmart and Sustainable Agriculture10.1007/978-3-030-88259-4_6(71-83)Online publication date: 11-Nov-2021
https://doi.org/10.1007/978-3-030-88259-4_6
Bloemheuvel SAtzmueller MPostma M(2019)Stratification-Oriented Analysis of Community Structure in Networks of Face-to-Face ProximityBehavioral Analytics in Social and Ubiquitous Environments10.1007/978-3-030-34407-8_2(28-43)Online publication date: 18-Nov-2019
https://doi.org/10.1007/978-3-030-34407-8_2

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