社會網絡在電腦科學的研究範疇中扮演一個日漸重要的角色，類別預測正是其中 一項熱門的議題。類別預測的研究目標，是利用網絡中部分已知類別的節點，預 測出其他未知類別節點之類別。
以往類別預測之研究，皆以靜態社會網絡為主;然而，社會網絡往往是隨著 時間動態演進的。在動態網絡中，網絡中的節點、連結、類別，皆可能隨著時間 演進而更動。連帶的，節點之間相互影響的關係也會隨著時間改變。此變動可以 視為一種概念飄移 (Concept Drift)。

不同於過往的研究，我們指出了動態網絡中類別分類的問題，並利用靜態網絡中類別分類的技術，結合概念飄移的方法，提出能夠在動態網絡中預測類別的 解法。

實驗所採用的資料是 IMDb (Internet Movie Database) 的社會網路，我們用以 預測演員的類別，根據實驗結果顯示，將動態社會網絡的演化過程，加入作為類 別預測的參考指標，能夠提高動態網絡中類別分類的準確性。

Label prediction is one of the central questions of social network research. The core of label prediction is the use of labeled nodes to predict labels of un-labeled nodes in a social network. The definition of a labeled social network is a social network of partial or complete labeled nodes. The nodes in the same social network have a mutual impact on each other’s labels.

Previous research on label prediction have been focused on static social networks. However, social networks are more dynamic in reality. In a dynamic social network, the links of nodes, even the labels of nodes, can be changed with time. The mutual influence of nodes can also be changed. The changing is called “Concept Drift.”

This thesis predicts the labels on a dynamic labeled social work. We address the problems of classification for a dynamic social network. The technique of label prediction on static social networks and algorithms used to tackle concept drift are combined to solve the label prediction problem on dynamic social networks.

Experiments were performed on a labeled social network constructed from the Internet Movie Database. The results show that we can use the evolution of dynamic social networks to generate a more precise prediction of labels.

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