| Abstract |
Complex networks that occur in nature, such as those from biochemistry, neurobiology, and engineering, often exhibit simple, network structural properties, or “motifs.” Network motifs refer to recurring, significant patterns of interaction between sets of nodes and represent basic building blocks of graphs. We examine patterns of motifs in 24 social networks, with four networks representing each of six types of interactions, including friendship, advice seeking, email communication, twitter, terrorist, and legislative cosponsorship. In our analyses we simulate random networks to examine the prevalence of subgraphs. We examine subgraph ratio profiles and use correlation plots to compare patterns between networks. Findings reveal several common substructures. Reciprocity of directed ties occurs much more frequently than expected by chance in all of our graphs. Similarly, we find that completely connected triads and tetrads (i.e., four-node subgraphs) occur more often than expected, highlighting the tendency of actors to form clusters of ties. We also identify motifs that suggest patterns of hierarchy. One interesting tetrad motif consisted of a “300” triad clique, in which one of the nodes was connected by a mutual tie to a fourth node. This configuration points to the possible bridging of gaps, or “structural holes” between nodes and implies that the networks in our sample are made of both strong ties that lead to clustering, and weaker ones that result in bridging. Certain motifs also occur in some social networks that are common among their biological counterparts, such as the “feed-forward loop,” or transitive triad, and the “bi-fan” tetrad. Results suggest that motifs could be used to fill in missing group ties when information is incomplete and to predict network genre from limited information. Motifs also contain the seeds of dynamic change. Networks with high levels of triangulation, for example, would be predicted to close open triads in the future. |