For modeling nonlinear data, complex networks are effective method [27]. Complex network is a weighted undirected graph G = (V, E, W), where V is the set of nodes, E denotes the set of edges e (vi, vj) between the pairs of the nodes vi and vj and W is the weights w (vi, vj) assigned to their corresponding edges e (vi, vj) of E.

Three complex networks are constructed from the training datasets and one data record which should be classified independent from it belongs to training or test dataset. The first one is comprised of all the training data records and one data record which should be classified as its nodes and is called CN1. The second and the third complex networks consist of one data record which should be classified and all training data records excluding the negative and positive classes and named as CN2 and CN3, respectively. If the considered data record belongs to training dataset, its class label is excluded from its corresponding complex networks.

In other words, the nodes of CN1, CN2 and CN3 are one data record which should be classified and all the training data records, positive labeled and negative labeled training data records, respectively. Therefore, for each data record, three complex networks are constructed.

An edge between node vi and vj is drawn if the distance between the input features of the ith and jth training data records is smaller than a user-defined threshold. For calculating the pairwise distance between data records, Euclidean distance function is used and can be calculated as Eq. (2):

where m is the number of the input features, Fi,p and Fj,p denote the pth input feature values for data records corresponding to vi and vj.

The weight of the edge e(vi,vj) is calculated as Eq. (3):

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