近年來，保護隱私的話題風起雲湧，隱私保護成為現代人越來越注重的議題；然而社群軟體需要大量的資訊，資訊越多越能準確的分析使用者，但越來越多使用者基於隱私考量，不願意透露更多自己的屬性，或是交友圈等，造成資料的不完整。而目前現有的作法，都是假設資料是完整的情況去進行分析，並沒有考慮隱私保護下資料不完整，甚至稀疏的情況；且在社群網路嵌入特徵向量學習方法 (network embedding)中，多只考慮結構資訊，或是考慮屬性資訊時還需額外資訊，綜合以上問題，本研究提出一將結構與屬性融合之方式(Structure-Attributed Enhanced Matrix, SAEM)，克服資料稀疏性問題，並提出四種改良的嵌入學習，其一為在嵌入學習時同時加入屬性與結構資訊做學習(cc-n2v)，只加入結構資訊(com-n2v)與只加入屬性資訊(clu-n2v)，將com-n2v與clu-n2v結合成cat-n2v。
對於移除50%的隨機移除隱私設定下，本研究提出的SAEM結合cat-n2v平均而言相比node2vec基於網路結構，在社群偵測、節點分類與連結預測分別獲得80%、15%與27%的顯著效果提升，在不同隱私設定下結果也大同小異，尤其在使用者愈注重隱私、網路結構愈稀疏的狀況下，能做出更準確的預測，足以顯示本研究的方法得以在隱私顧慮下提供有效的社群網路應用服務。使用者能夠被允許提供不完整資訊，呼應歐盟於2018年5月頒布的一般資料保護規範 (General Data Protection Regulation, GDPR)，只需少量的使用者個資，在有效的特徵表示學習下，依然能提供穩定準確效能的網路推薦系統服務。
其中針對社群偵測，提出Egocentric Partitioning (EgoPart)演算法，適用於偵測重疊社群(overlapped communities)，亦可用在偵測非重疊社群(non-overlapped communities)，先從各節點角度去偵測鄰近結構分布(local clusters)，局部觀察後再結合成整體的社群(global clusters)，在偵測重疊社區與非重疊社區時，與現今最最廣為被採用的社群偵測演算法Label Propagation相比，分別獲得32%、42%的顯著效果提升，尤其對於偵測非重疊社區，EgoPart表現優於其他常見方法，顯示本研究提供有效之方法在社群偵測上。

In recent years, privacy protection has become an issue that more people pay attention to. However, community software requires lots of information. More and more users are not willing to disclosure their own attributes, circle of friends, etc., resulting in incomplete information. The current practice assume that the data is complete to analyze, not consider the privacy of the data is incomplete or even sparse happening.
In network embedding feature vector learning method, more only consider structural information, or need additional information. To summarize the above problems, this study proposes a way to integrate structure and attributes (SAEM). To overcome data sparsity problem, and propose four improved embedded learning. Add attribute and structure information to learn while embedding learning (cc-n2v), only add structural information (com-n2v) and only attributes information (clu-n2v), combining com-n2v and clu-n2v into cat-n2v.
For removing 50% of the random privacy settings, the SAEM combined with cat-n2v compared to node2vec based on network structure proposed in this study is 80%, 15%, and 27% in community detection, node classification, and link prediction. Respectively, when users pay more attention to privacy and the sparse network structure, more accurate predictions can be made. It shows that the research method can provide effective social network application services under privacy concerns.
For community detection, Egocentric Partitioning algorithm is proposed to detect overlapping or non-overlapping communities. Observe the structure distribution from each node, and then analyze the whole. EgoPart outperforms other common methods show that it provides an effective method for community detection.

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