近年來，關於如何破壞社群網絡的相關研究受到人們廣大的討論，這包括社
群網路偵測、異常檢測和圖分析等具有廣泛重要性和應用的領域。此外，一些
人嘗試使用基於機器學習或演算法的方法來攻擊這些任務。他們可能攻擊數據
集、演算法或機器學習模型，並設計一些對應的算法來降低目標效能。攻擊社
群網路偵測是其中最受歡迎的任務之一。例如，人們可以創建虛假的帳戶並冒
充特定社群的成員，並透過發散各種虛假訊息使我們對於當前社群網路狀態產
生錯誤的認知。如果他們能使社群網路偵測的結果變差，就有更高的機會實現
某些目標。儘管當前存在各種攻擊方法，但我們注意到大多數方法存在某些缺
失。首先，他們在開發攻擊方法時經常忽視了潛在的社群網路結構。其次，他
們的攻擊策略往往具有有限的應用範圍。為了解決這些問題，我們提出了一個
新的度量標準，稱為θ-trio。在這篇論文中，我們首先分析與驗證θ-trio 在攻擊
社群網絡偵測任務中較其他度量標準較為有效。接著我們基於θ-trio 提出了一種
可以有效地干擾社群網路偵測的演算法，稱為”θ-Trio Minimization Via Adding
Edge (θMAE)”。在實驗中，我們可以看到我們的方法比其他現有的方法更為高
效。

In recent years, there has been a significant discussion about research related to undermining social network structures. This encompasses areas of widespread importance and application, including community network detection, anomaly detection, and graph analysis. Additionally, some individuals are attempting to employ machine learning or algorithm-based methods to attack these tasks. They
may target datasets, algorithms, or machine learning models, and design corresponding algorithms to reduce the target’s performance. Attacking community network detection is one of the most popular tasks in this context. For instance, individuals may create fake accounts and impersonate members of specific communities, disseminating false information to create misleading perceptions of the current state of social network connections. If they can degrade the results of
community detection, they have a higher chance of achieving certain objectives. Despite the various existing attack methods, most of them exhibit certain deficiencies. Firstly, they often disregard the underlying network structures of the community network when developing their attack methods. Secondly, their attack strategies tend to have limited applicability. To address these issues, we propose a novel metric called θ-trio. In this paper, we analyze and validate the effectiveness of θ-trio in undermining community detection tasks compared to other metrics. We propose an algorithm based on θ-trio called ”θ-Trio Minimization Via Adding Edge (θMAE). In our experiments, we can observe that our method is more efficient than other existing approaches.

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