Efficient Algorithms to Mine Maximal Span-Trusses From Temporal Graphs

TL;DR

This paper introduces the concept of span-trusses in temporal graphs, generalizing dense subgraph detection to include temporal span, and proposes efficient algorithms for mining these structures from large datasets.

Contribution

It defines the novel $(k, riangle)$-truss model for temporal graphs and develops algorithms to efficiently find maximal span-trusses, filling a gap in temporal network analysis tools.

Findings

Algorithms effectively identify maximal span-trusses in real datasets.

The approach outperforms baseline methods in efficiency and scalability.

Experimental results demonstrate the utility of span-trusses in temporal network analysis.

Abstract

Over the last decade, there has been an increasing interest in temporal graphs, pushed by a growing availability of temporally-annotated network data coming from social, biological and financial networks. Despite the importance of analyzing complex temporal networks, there is a huge gap between the set of definitions, algorithms and tools available to study large static graphs and the ones available for temporal graphs. An important task in temporal graph analysis is mining dense structures, i.e., identifying high-density subgraphs together with the span in which this high density is observed. In this paper, we introduce the concept of $(k, \Delta)$-truss (span-truss) in temporal graphs, a temporal generalization of the $k$-truss, in which $k$ captures the information about the density and $\Delta$ captures the time span in which this density holds. We then propose novel and efficient algorithms to identify maximal span-trusses, namely the ones not dominated by any other span-truss neither in the order $k$ nor in the interval $\Delta$, and evaluate them on a number of public available datasets.