The World Wide Web, the Internet, coupled biological and chemical systems, neural networks, and social interacting species are only a few examples of systems comprising a large number of highly interconnected dynamical units. These networks contain characteristic patterns, _network motifs_, that occur far more often than in randomized networks with the same degree sequence. Several algorithms have been suggested for counting or detecting the number of occurrences of network motifs as trees and bounded treewidth subgraphs of size _O_(log _n_), at most 7 for some motifs. In addition, _local motif counting_, counting the number of motifs in which a node participates, was recently suggested as a method of classifying nodes in the network. The premise is that the distribution of motifs in which a node participates is an indication of its function in the network. Therefore, local counting of network motifs provides a major challenge. However, no such practical algorithm exists other than local counting of triangles. We present several algorithms with time complexity _O_(((3_e_)^k · _n_ · |_E_| · log )/∊²) that approximate for every vertex the number of occurrences of the motif in which the vertex participates, for _k_-length cycles and _k_-length cycles with a chord, where _k_ = _O_(log _n_), and algorithms with time complexity _O_((_n_ · |_E_| · log )/∊² + |_E_|² · log _n_ + |_E_| · _n_ log _n_) that approximate for every vertex the number of noninduced occurrences of the motif in which the vertex participates for all motifs of size four. In addition, we show algorithms that approximate the total number of occurrences of these network motifs when no efficient algorithm exists. Some of our algorithms use the “color-coding” technique.

Approximating the Number of Network Motifs