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    政大機構典藏 > 理學院 > 資訊科學系 > 學位論文 >  Item 140.119/126582
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/126582

    Title: 基於圖形卷積神經網路之異質性圖譜表示法學習
    Heterogeneous Graph Embedding Based on Graph Convolutional Neural Networks
    Authors: 蘇裕勝
    Su, Yu-Sheng
    Contributors: 蔡銘峰
    Tsai, Ming-Feng
    Su, Yu-Sheng
    Keywords: 表示法學習
    Network Embedding
    Link prediction
    Date: 2019
    Issue Date: 2019-10-03 17:18:08 (UTC+8)
    Abstract: 近年來由於龐大的資料量,如何保存這些資料以及如何將這些資料
    法(Information Network Embedding),能有效將不同節點和關係投射
    到低維度空間,因而成了非常熱門的領域,近年來GNN(Graph Neural
    Network) 的概念也被加入到網路學習表示法領域,應用在分類、
    推薦等工作。本論文提出一個異質網路表示學習法(Heterogeneous Information
    Network Embedding)的架構:先透過學習表示法產生節點的
    表示法當作特徵值,並透過建立同質網路圖以及GraphSAGE 的訓練,
    特徵值嵌在一起,並做訓練,能夠有效的提升連結預測的F1-score 成
    績。在推薦工作中基於我們的建圖方式可以考慮到更多High order 資
    訊,進而提升推薦系統在MAP、Recall、Hit ratio的成績。
    In recent years, information network embedding has become popular because the techniques enable to encode information into low-dimensions representation, even for a graph/network with multiple types of nodes and relations. In addition, graph neural network (GNN) has also shown its effectiveness in learning large-scale node representations on node classification. In this paper, therefore, we propose a framework based on the heterogeneous network embedding and the idea of graph neural network. In our framework, we first generate node representations by various network embedding methods. Then, we split a homogeneous network graph into subgraphs and concatenate the learned node representations into the same embedding space. After that, we apply one of variant GNN, called GraphSAGE, to generate representations for the tasks of link prediction and recommendation. In our experiments, the results on the tasks of link prediction and recommendation both show the effectiveness of the proposed framework.
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    accepted as poster.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106753004
    Data Type: thesis
    DOI: 10.6814/NCCU201901186
    Appears in Collections:[資訊科學系] 學位論文

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