  Temporal graphs are ubiquitous. Mining communities that are bursting in a
period of time is essential to seek emergency events in temporal graphs.
Unfortunately, most previous studies for community mining in temporal networks
ignore the bursting patterns of communities. In this paper, we are the first to
study a problem of seeking bursting communities in a temporal graph. We propose
a novel model, called (l, {\delta})-maximal dense core, to represent a bursting
community in a temporal graph. Specifically, an (l, {\delta})-maximal dense
core is a temporal subgraph in which each node has average degree no less than
{\delta} in a time segment with length no less than l. To compute the (l,
{\delta})-maximal dense core, we first develop a novel dynamic programming
algorithm which can calculate the segment density efficiently. Then, we propose
an improved algorithm with several novel pruning techniques to further improve
the efficiency. In addition, we also develop an efficient algorithm to
enumerate all (l, {\delta})-maximal dense cores that are not dominated by the
others in terms of the parameters l and {\delta}. The results of extensive
experiments on 9 real-life datasets demonstrate the effectiveness, efficiency
and scalability of our algorithms.
