Efficient Maximal Temporal Plex Enumeration

Maximal k-plex enumeration is an important problem in graph analysis and can find many real-world applications. A k-plex is a subgraph in which every vertex can miss edges to at most
$k$
vertices (including itself). Previous studies mainly focus on static graphs. However, in reality, relationships between two entities often occur at some specific timestamps, which can be modeled as temporal graphs. Directly extending the k-plex model may fail to find some critical groups in temporal graphs, which exhibit certain frequent occurring phenomenon. To fill the gap, in this paper, we propose a novel model called
$(k,\ l)$
-plex, which is a vertex set that exists in no less than
$I$
timestamps, at each of which the subgraph induced is a
$k$
-plex. To identify practical results, we introduce the concept of large maximal
$(k,\ l)$
-plex (MalKLP), i.e., maximal
$(k,\ l)$
-plex with size no less than a given threshold. In this paper, we conduct the first attempt to propose and investigate the MalKLP enumeration problem, which is proved to be NP-hard. A reasonable baseline method called KLPE-BK is developed by extending the Bron-Kerbosch framework. To overcome the three limitations in KLPE-BK and scale for larger graphs, novel optimized strategies are proposed, including graph reduction, search branch pruning and maximality checking approaches. Finally, we present our optimized algorithm KLPE+ by integrating the techniques proposed. Comprehensive experiments on 8 real-world datasets are conducted to validate the efficiency and scalability of the proposed techniques. Compared with the baseline method, KLPE + can achieve up to two orders of magnitude speedup. A case study is conducted to verify the effectiveness of our model.