Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences
Significance Social ties are more common between individuals with similar traits, a feature known as homophily. Ties are also known to be stronger between individuals with multiple common acquaintances. Both of these two properties constrain the flow of information and ideas in social networks. We study time-dependent communication patterns in a large mobile phone communication dataset and show that both of these two properties are in fact stronger than can be observed in any static snapshot of a communication network. The methods developed to obtain these results can be used more generally to study various time-dependent networks. Recent studies on electronic communication records have shown that human communication has complex temporal structure. We study how communication patterns that involve multiple individuals are affected by attributes such as sex and age. To this end, we represent the communication records as a colored temporal network where node color is used to represent individuals’ attributes, and identify patterns known as temporal motifs. We then construct a null model for the occurrence of temporal motifs that takes into account the interaction frequencies and connectivity between nodes of different colors. This null model allows us to detect significant patterns in call sequences that cannot be observed in a static network that uses interaction frequencies as link weights. We find sex-related differences in communication patterns in a large dataset of mobile phone records and show the existence of temporal homophily, the tendency of similar individuals to participate in communication patterns beyond what would be expected on the basis of their average interaction frequencies. We also show that temporal patterns differ between dense and sparse neighborhoods in the network. Because also this result is independent of interaction frequencies, it can be seen as an extension of Granovetter’s hypothesis to temporal networks.