43,710 research outputs found
Event Organization 101: Understanding Latent Factors of Event Popularity
The problem of understanding people's participation in real-world events has
been a subject of active research and can offer valuable insights for human
behavior analysis and event-related recommendation/advertisement. In this work,
we study the latent factors for determining event popularity using large-scale
datasets collected from the popular Meetup.com EBSN in three major cities
around the world. We have conducted modeling analysis of four contextual
factors (spatial, group, temporal, and semantic), and also developed a
group-based social influence propagation network to model group-specific
influences on events. By combining the Contextual features And Social Influence
NetwOrk, our integrated prediction framework CASINO can capture the diverse
influential factors of event participation and can be used by event organizers
to predict/improve the popularity of their events. Evaluations demonstrate that
our CASINO framework achieves high prediction accuracy with contributions from
all the latent features we capture.Comment: International AAAI Conference on Web and Social Media (ICWSM) 2017
https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/1557
Structure controllability of complex network based on preferential matching
Minimum driver node sets (MDSs) play an important role in studying the
structural controllability of complex networks. Recent research has shown that
MDSs tend to avoid high-degree nodes. However, this observation is based on the
analysis of a small number of MDSs, because enumerating all of the MDSs of a
network is a #P problem. Therefore, past research has not been sufficient to
arrive at a convincing conclusion. In this paper, first, we propose a
preferential matching algorithm to find MDSs that have a specific degree
property. Then, we show that the MDSs obtained by preferential matching can be
composed of high- and medium-degree nodes. Moreover, the experimental results
also show that the average degree of the MDSs of some networks tends to be
greater than that of the overall network, even when the MDSs are obtained using
previous research method. Further analysis shows that whether the driver nodes
tend to be high-degree nodes or not is closely related to the edge direction of
the network
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