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[20 marks] Social network data is usually modelled as a graph with nodes depicting users and edges showing the relationship between them. Table 1, Table

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[20 marks] Social network data is usually modelled as a graph with nodes depicting users and edges showing the relationship between them. Table 1, Table II, and Table III in question I can be represented by Fig. 1(a), Fig. 1(b), and Fig.1(c) respectively. Following question 1, you are asked to carry out the following mining task. (a) (b) (c) Fig. 1 Social network graph of (a) Table I. (b) Table II, (c) Table III a) Propose a dissimilarity metric for clustering the nodes in the network graph of Fig. 1(a) so that the relationship between nodes can be properly modeled. Based on your proposed metric, compute the distance between nodes A and B in Fig. 1(a). b) After evaluating your proposed metric in part (a), your development team considered that it is too simple and would like to include information about how many friends a user has. Propose an enhance dissimilarity metric that can take into considerations of both the relationship between nodes and the number of friends cach node has. Based on your new metric, compute the distance between nodes D and E in Fig. 1(c). [20 marks] Social network data is usually modelled as a graph with nodes depicting users and edges showing the relationship between them. Table 1, Table II, and Table III in question I can be represented by Fig. 1(a), Fig. 1(b), and Fig.1(c) respectively. Following question 1, you are asked to carry out the following mining task. (a) (b) (c) Fig. 1 Social network graph of (a) Table I. (b) Table II, (c) Table III a) Propose a dissimilarity metric for clustering the nodes in the network graph of Fig. 1(a) so that the relationship between nodes can be properly modeled. Based on your proposed metric, compute the distance between nodes A and B in Fig. 1(a). b) After evaluating your proposed metric in part (a), your development team considered that it is too simple and would like to include information about how many friends a user has. Propose an enhance dissimilarity metric that can take into considerations of both the relationship between nodes and the number of friends cach node has. Based on your new metric, compute the distance between nodes D and E in Fig. 1(c)

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