  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.
