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2019 | OriginalPaper | Buchkapitel

2. Temporal Artifacts from Edge Accumulation in Social Interaction Networks

verfasst von : Matt Revelle, Carlotta Domeniconi, Aditya Johri

Erschienen in: Neural Advances in Processing Nonlinear Dynamic Signals

Verlag: Springer International Publishing

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Abstract

There has been extensive research on social networks and methods for specific tasks such as: community detection, link prediction, and tracing information cascades; and a recent emphasis on using temporal dynamics of social networks to improve method performance. The underlying models are based on structural properties of the network, some of which we believe to be artifacts introduced from common misrepresentations of social networks. Specifically, representing a social network or series of social networks as an accumulation of network snapshots is problematic. In this paper, we use datasets with timestamped interactions to demonstrate how cumulative graphs differ from activity-based graphs and may introduce temporal artifacts.

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Vorheriges Kapitel Processing Nonlinearities

Nächstes Kapitel Data Mining by Evolving Agents for Clusters Discovery and Metric Learning

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Titel: Temporal Artifacts from Edge Accumulation in Social Interaction Networks
verfasst von: Matt Revelle
Carlotta Domeniconi
Aditya Johri
Verlag: Springer International Publishing
Buch: Neural Advances in Processing Nonlinear Dynamic Signals
Print ISBN: 978-3-319-95097-6

Electronic ISBN: 978-3-319-95098-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-95098-3_2

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