利益衝突是一種情況，在這情況下一個人具備某種條件，在對他人做決策時會以本身存有偏見做決定。利益衝突的偵測被廣泛的運用在個各領域在學術界裡面，本文則想在一般的學術活動層面來探討利益衝突的表現形式。在利益衝突偵測上，文件的蒐集與探勘是很重要的一環。很不幸的，大部分的學者都忽略從網路上蒐集回來的資料大都不完整，其原因歸於人為疏失、個人隱私保護的問題，最重要的是我們不能保證網際網路裡面所蒐集到的資料是最完整的。基於這些原因，使得所有利益關係的人無法在所建立的學術協同合作網路偵測出來，這對於某些需要比較嚴謹的利益衝突偵測系統，遺失的衝突關係會導致出現一些不被預期的結果。
本文強調在資料不完整的環境裡利用社群互動模式偵測出有利益衝突關係的研究。在學術合作的環境裡，我們觀察到（1）大部分的人喜歡與權位較高的學者合作在相同或是相似的研究領域；（2）人們經常透過朋友的朋友互相結識進而互相合作。經由所觀察到的人際互動關係型態，我們運用在偵測出遺失的衝突關係。一開始從現有的資料建出協同合作網路，再利用我們所觀察到的特殊網路形態推論出被遺失的關係。實驗結果顯示，我們所提出的方法最高可以有效的恢復95％利益衝突關係。此外我們的方法可以利用單一的資料來源就能找出大部分的關係，取代不同資料來源的繁瑣整合工作。

Conflict Of Interest (COI) detection for social contacts is a fundamental data mining
task to build a collaboration network. Unfortunately social network data are often
incomplete. Moreover, due to privacy issues, it is impractical to access several information
sources. In applications that require strict COI, unrevealed relationships caused
by incomplete data may be problematic. For such applications, a missing-relationship
recovering algorithm that possesses high recall rates is needed. In this paper, we study
the problem of recovering missing relationships underlying social interaction in academic
community. From the topology of collected social network, we observe that 1)
people like to cooperate with authoritative researchers in the same or similar domains;
2)people having friendships or friend of friendships more likely cooperates together
if they share the same research interests. Based on the observations, the potential relationships
are detected by discovering hidden social interaction among disconnected
nodes that have specific types of connectivity. Our experiment results show that our
approach can recover up to 95% relationships in COI detection, which means we can
explore most of missing relationships from a single source.

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