An Efficient Procedure for Mining Egocentric Temporal Motifs

TL;DR

This paper introduces an efficient egocentric approach for mining temporal motifs in dynamic networks by encoding neighborhoods into bit vectors, enabling larger motif extraction and improved interpretability.

Contribution

The novel egocentric temporal neighborhood concept and bit vector encoding significantly enhance motif mining efficiency and interpretability over existing methods.

Findings

Outperforms alternative approaches in efficiency and motif size.

Enables analysis of larger, more complex temporal motifs.

Provides interpretable insights into social interaction dynamics.

Abstract

Temporal graphs are structures which model relational data between entities that change over time. Due to the complex structure of data, mining statistically significant temporal subgraphs, also known as temporal motifs, is a challenging task. In this work, we present an efficient technique for extracting temporal motifs in temporal networks. Our method is based on the novel notion of egocentric temporal neighborhoods, namely multi-layer structures centered on an ego node. Each temporal layer of the structure consists of the first-order neighborhood of the ego node, and corresponding nodes in sequential layers are connected by an edge. The strength of this approach lies in the possibility of encoding these structures into a unique bit vector, thus bypassing the problem of graph isomorphism in searching for temporal motifs. This allows our algorithm to mine substantially larger motifs with respect to alternative approaches. Furthermore, by bringing the focus on the temporal dynamics of the interactions of a specific node, our model allows to mine temporal motifs which are visibly interpretable. Experiments on a number of complex networks of social interactions confirm the advantage of the proposed approach over alternative non-egocentric solutions. The egocentric procedure is indeed more efficient in revealing similarities and discrepancies among different social environments, independently of the different technologies used to collect data, which instead affect standard non-egocentric measures.