  Community search is a derivative of community detection that enables online
and personalized discovery of communities and has found extensive applications
in massive real-world networks. Recently, there needs to be more focus on the
community search issue within directed graphs, even though substantial research
has been carried out on undirected graphs. The recently proposed D-truss model
has achieved good results in the quality of retrieved communities. However,
existing D-truss-based work cannot perform efficient community searches on
large graphs because it consumes too many computing resources to retrieve the
maximal D-truss. To overcome this issue, we introduce an innovative merge
relation known as D-truss-connected to capture the inherent density and
cohesiveness of edges within D-truss. This relation allows us to partition all
the edges in the original graph into a series of D-truss-connected classes.
Then, we construct a concise and compact index, ConDTruss, based on
D-truss-connected. Using ConDTruss, the efficiency of maximum D-truss retrieval
will be greatly improved, making it a theoretically optimal approach.
Experimental evaluations conducted on large directed graph certificate the
effectiveness of our proposed method.
