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Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

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68 3 Algorithms <strong>and</strong> <strong>Techniques</strong>that the incurring cost will be minimized. This assumption is quite reasonable in real worldthat the evolution of real observations is gradually <strong>and</strong> properly in a longi<strong>tud</strong>inal manner.In [164], Lin et al. proposed a framework for analyzing communities <strong>and</strong> their evolutionin dynamic networks. In their approach, they first introduced a cost function to measure thequality of community structure at a certain time t <strong>and</strong> assumed the forming of stable communitystructure is really dependent on the minimization of the cost function. In particular, thecost function consists of two aspects - a snapshot cost <strong>and</strong> a temporal cost:cost = α ·CS+(1 − α) ·CT (3.15)The snapshot cost is then determined by the distribution difference betw<strong>ee</strong>n the real similaritymatrix of nodes within the network <strong>and</strong> the calculated similarity matrix of the formed communitystructures. That is, the snapshot cost is the KL-divergence betw<strong>ee</strong>n the above mentionedsimilarity matrix. On the other h<strong>and</strong>, the temporal cost indicates the distribution differencebetw<strong>ee</strong>n the similarity matrices at two consecutive time stamps using the same formula. Combiningthese two types of cost gives the whole cost measure, which is used to guide the communitystructure detection. Hence the analysis of community structures <strong>and</strong> their evolutionsis converted to an optimization problem of finding appropriate communities that minimizesthe whole cost. To solve the optimization problem, an iterative algorithm is devised to alternativelyupdate the requested community structures until an optimization solution is reached.More details is referred to [164].SummaryIn this chapter, we review <strong>and</strong> summarize some commonly used algorithms <strong>and</strong> techniques inthe applications of web data mining, web recommendation <strong>and</strong> social network analysis. Themathematical formulations <strong>and</strong> algorithmic descriptions are elaborately organized into ninesections. It is expected that this chapter provides a technically detailed reference resource forreaders to proc<strong>ee</strong>d the further chapters.

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