  The collective attention on online items such as web pages, search terms, and
videos reflects trends that are of social, cultural, and economic interest.
Moreover, attention trends of different items exhibit mutual influence via
mechanisms such as hyperlinks or recommendations. Many visualisation tools
exist for time series, network evolution, or network influence; however, few
systems connect all three. In this work, we present AttentionFlow, a new system
to visualise networks of time series and the dynamic influence they have on one
another. Centred around an ego node, our system simultaneously presents the
time series on each node using two visual encodings: a tree ring for an
overview and a line chart for details. AttentionFlow supports interactions such
as overlaying time series of influence and filtering neighbours by time or
flux. We demonstrate AttentionFlow using two real-world datasets, VevoMusic and
WikiTraffic. We show that attention spikes in songs can be explained by
external events such as major awards, or changes in the network such as the
release of a new song. Separate case studies also demonstrate how an artist's
influence changes over their career, and that correlated Wikipedia traffic is
driven by cultural interests. More broadly, AttentionFlow can be generalised to
visualise networks of time series on physical infrastructures such as road
networks, or natural phenomena such as weather and geological measurements.
