Network Motif Model: An Efficient Approach for Extracting Features from Relational Data

This paper proposes the Network Motif Model (NMM), a novel and efficient approach for extracting features from relational data. First, our approach constructs a data network according to the data relation. Then significant sub-graphs are identified by extracting the basic network motifs from the data network, inspired by the motif concepts of complex network. At last, the first-order information of original data can be integrated with extracted significant sub-graphs to create the network motif features of relational data. Since basic motifs are easy to detect, the computation is efficient. Also, this kind of feature extraction not only preserves the relation of the data, but also keeps the label information of original data. Our experiments show that NMM has better classification accuracy than some inductive logic programming methods and probabilistic relational models. Thus, this model can be a potentially useful feature extraction strategy for statistical learning on Multi-relational data.