基於區域最大化節點在社群網路中探索朋友關係及個人興趣

Exploring the Friendship and Personal Interests in Social Network based on the Local Maximal Nodes

江信則(Hsin-Tse Chiang)

指導教授 ： 李新林

國立中正大學/工學院/資訊工程學系/碩士(2014年)

摘要

本篇論文提出了以挑選可靠的候選集為基礎來解決探索朋友關係及個人興趣的問題。為了解決上述這些因不完整的個人資料所導致的問題。區域中朋友數最多的節點被拿來建立預測朋友關係的分類器，以還原真正的個人資訊，並且利用其在不同社群中分佈的密度來預測每個人隱藏朋友的比例。在每一回合中，分類結果信心指數最高的候選集才會被執行，進而避免產生不正確的朋友關係及個人興趣。為了評估我們方法的效果，我們與信心指數決定次序的方法及固定推薦次序的方法做比較。結果顯示，在不同的資料集中皆改善了效能，特別是在連結稀疏的網路。

關鍵字

探索朋友關係

並列摘要

The thesis presents a reliable candidate selection approach for link prediction
and interest inference. To deal with the above problems caused by the in-
complete personal information, the nodes with local maximal degree are used
to build the classifi ers for recovering the hidden information. Moreover, the
hidden friendship of person is estimated based on the local maximum density
of the community, and filtering the incorrect inference and prediction as well.
In each round, the candidate with maximum con fidence of the classi fication
will be recovered iteratively to reduce the generation of mistaken interest and
friendship. To evaluate the proposed method, we compare the performance
with con fidence-based and iterative method. We improve the performance in
various data sets, especially the sparse network.

並列關鍵字

Exploring the Friendship and Personal Interests

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