在社交網路平台逐漸發達的時代，使用者資訊快速的累積。使用者資訊來自不同屬性的網路，並且擁有不同的特徵。如何善用多樣化的使用者資訊，找到網路中隱藏的訊息，成為重要的議題。社交網路能呈現大量的使用者資訊，並且更好的利用這些異構訊息。在社交網路中進行社區檢測，能從網路中找到一組合適的使用者，有助於了解網路中所攜帶的訊息。如何使社區檢測結果更加精確，以便找到有用的資訊，便是當前社交網路社區檢測的目的，同時也是本研究的動機。
　　標籤傳播社區檢測是社區檢測方法中相當熱門的方法之一，該方法能夠應用在各種不同的大型社交網路中，但其檢測的結果相當不穩定，且社區劃分結果也尚有改良的空間。因此，本研究提出基於綜合標籤影響權重的標籤傳播方法。該方法同時考量節點在網路中的位置以及節點的穩定程度，來定義新的綜合標籤影響權重以評估標籤的影響力，並將綜合影響標籤權重用於選擇節點標籤的過程中，以提升社區檢測的穩定性和最終劃分結果的成效。
　　本研究提出的新的標籤傳播社區檢測法，經由實驗結果證實在考量網路結構和節點在網路結構中的穩定性後，能有效的改善原先檢測結果不穩定的問題，同時優化整體社區檢測的效益，以找到更好的社區劃分結果。

　　In the era of rapidly advancing social media platforms, user information originating from various online social networks with different attributes accumulates quickly and possesses diverse characteristics. How to leverage diverse user information to uncover hidden insights in networks has become an important research issue. Social networks provide a wealth of user information, enabling better utilization of these heterogeneous signals. Conducting community detection in social networks helps identify a suitable group of users and facilitates a better understanding of the information embedded in the network. How to improve the accuracy of community detection results and find valuable information in social networks is the objective of current social network community detection and also the motivation of this research.
　　Label propagation community detection is a popular method in community detection approaches, which can be applied to various large-scale social networks. However, the detection results of label propagation community detection may not meet expectations. Therefore, this thesis proposes a label propagation method based on comprehensive label influence weight for community detection. This method considers both the position of nodes in the network and the stability of nodes in order to define a new comprehensive label influence weight for evaluating the impact of labels. The comprehensive influence label weight is then used in the process of selecting node labels to enhance the stability of community detection and improve the effectiveness of the final partitioning results.
　　A lot of experiments have been made for the proposed label propagation community detection method. The experimental results reveal that this method can effectively improve the unstable detection results by considering the network structure and the stability of nodes within the network. It also optimizes the overall effectiveness of community detection to achieve improved community partitioning results.

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