Abstract
Analyzing large-scale evolving graphs are crucial
for understanding the dynamic and evolutionary nature of social networks. Most existing works focus
on discovering repeated and consistent temporal patterns, however, such patterns cannot fully explain
the complexity observed in dynamic networks. For
example, in recommendation scenarios, users sometimes purchase products on a whim during a window shopping. Thus, in this paper, we design and
implement a novel framework called BurstGraph
which can capture both recurrent and consistent patterns, and especially unexpected bursty network
changes. The performance of the proposed algorithm is demonstrated on both a simulated dataset
and a world-leading E-Commerce company dataset,
showing that they are able to discriminate recurrent
events from extremely bursty events in terms of action propensity