  Searching for local communities is an important research problem that
supports advanced data analysis in various complex networks, such as social
networks, collaboration networks, cellular networks, etc. The evolution of such
networks over time has motivated several recent studies to identify local
communities in dynamic networks. However, these studies only utilize the
aggregation of disjoint structural information to measure the quality and
ignore the reliability of the communities in a continuous time interval. To
fill this research gap, we propose a novel $(\theta,k)$-$core$ reliable
community (CRC) model in the weighted dynamic networks, and define the problem
of \textit{most reliable community search} that couples the desirable
properties of connection strength, cohesive structure continuity, and the
maximal member engagement. To solve this problem, we first develop a novel edge
filtering based online CRC search algorithm that can effectively filter out the
trivial edge information from the networks while searching for a
\textit{reliable} community. Further, we propose an index structure, Weighted
Core Forest-Index (WCF-index), and devise an index-based dynamic programming
CRC search algorithm, that can prune a large number of insignificant
intermediate results and support efficient query processing. Finally, we
conduct extensive experiments systematically to demonstrate the efficiency and
effectiveness of our proposed algorithms on eight real datasets under various
experimental settings.

Social and Information Networks;Databases