  Social event planning has received a great deal of attention in recent years
where various entities, such as event planners and marketing companies,
organizations, venues, or users in Event-based Social Networks, organize
numerous social events (e.g., festivals, conferences, promotion parties).
Recent studies show that "attendance" is the most common metric used to capture
the success of social events, since the number of attendees has great impact on
the event's expected gains (e.g., revenue, artist/brand publicity). In this
work, we study the Social Event Scheduling (SES) problem which aims at
identifying and assigning social events to appropriate time slots, so that the
number of events attendees is maximized. We show that, even in highly
restricted instances, the SES problem is NP-hard to be approximated over a
factor. To solve the SES problem, we design three efficient and scalable
algorithms. These algorithms exploit several novel schemes that we design. We
conduct extensive experiments using several real and synthetic datasets, and
demonstrate that the proposed algorithms perform on average half the
computations compared to the existing solution and, in several cases, are 3-5
times faster.
