چكيده به لاتين
Community detection is the basic topics in network analysis. Local community detection or seed expansion is designed to run on large scale network. These methods can not accurately estimate the number, distance and location of communities because there is no local knowledge about network structure. These approaches have a global pre- and post-processing that conflict with local processing. In this research, we propose sliding window algorithm to identify communities in local areas. This algorithm moves towards a community with high quality, based on crawling movement. Quality is determined based on conductance measure. The sliding window algorithm based on local knowledge that is gained during run, estimate number of communities and set the distance of community Based on community’s center. Our algorithm is less complex than previous methods because the computation is purely local. The proposed algorithm extracts overlapping communities, also with change the structure of algorithm can detect disjoint communities.
We evaluate the sliding window method against the new community detection methods on large real networks. There are two types of evaluation, structure measure and identify ground-truth communities. Experiments show that the proposed method, in both evaluations have a better performance relative to other algorithms.
Keywords: community detection, network, local approach, Sliding window, conductance, ground-truth community
