An Effective Index for Truss-based Community Search on Large Directed Graphs

TL;DR

The paper tackles efficient online community search in large directed graphs by introducing D-truss-connected, a relation that groups edges into cohesion-preserving classes, and ConDTruss, a compact index that preserves D-truss information. By performing a D-truss decomposition to obtain skyline trussness and then constructing a summarized graph of D-truss-connected classes, the authors enable direct M-D-truss retrieval on the index with decompress-on-output guarantees. The approach yields substantial index compression and faster query times, demonstrated through experiments on real networks (e.g., EAT, Slashdot, Twitter, Pokec) and case studies showing adjustable query cohesion via $(k_c,k_f)$. Overall, ConDTruss provides a scalable, space-efficient solution for directed-graph CS with practical impact on personalized discovery and targeted analysis.

Abstract

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.

Figures (10)

• Figure 1: An example of M-D-truss in directed graph.
• Figure 2: An example of construct summarized graph. (a)Calculate skyline trussnesses for each edge $e\in E_{G_d}$ and partition directed graph $G_d$ into connected classes which preserve D-truss information. (b)Each connected class is represented by a s-node. (c)Construct the summarized graph of $G_d$. And each s-edge depicts the connections between supernodes.
• Figure 3: Summarized Graph Construction
• Figure 4: ConDTruss-based M-D-truss find.
• Figure 5: An example of ConDTruss-based M-D-truss find