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    题名: 基於高階相鄰關係與最短路徑之圖表示法學習
    Exploring High-order Proximity and Shortest-path Walking for Graph Representation Learning
    作者: 林柏宇
    Lin, Bo-Yu
    贡献者: 蔡銘峰
    林柏宇
    Lin, Bo-Yu
    关键词: 圖學習表示法
    最短路徑
    鏈結預測
    Graph Representation Learning
    Shortest Path
    Link Prediction
    日期: 2023
    上传时间: 2023-09-01 15:24:12 (UTC+8)
    摘要: 圖學習表示法作為一種重要的圖分析方法,旨在將圖中的節點和邊映射到低維度的向量空間,以更精確地捕捉其結構和關係特徵。在這一框架中,高階相鄰關係扮演著關鍵角色。相對於傳統的低階表示法,高階相鄰關係提供了更深入的分析和處理能力。 它能夠捕捉到節點之間的複雜連接關係,包括間接連接和共同鄰居等。此外,高階相 鄰關係在圖的結構中具有重要性,如三角形關係和子圖模式等。通過學習這些關係,模型能夠更準確地理解圖的整體特徵。因此,探索高階相鄰關係對於進一步提升圖學習表示法的性能和能力至關重要。
    本研究旨在引入「最短路徑」作為主要概念,以改善圖學習表示法對高階相鄰關係的學習能力。通過利用最短路徑可達到 k 步為 k 階鄰 居的特性,我們希望表示法能更加直觀且準確地捕捉高階相鄰關係。此外,我們進一步探索以最短路徑長的分佈來動態決定高階鄰居的取值範圍。 這種方法使得圖學習表示法能夠自動調整參數,而無需耗費大量時間和計算資源。透過基於最短路徑長的分佈,我們能夠更有效地決定高階鄰居的範圍,從而提高圖學習表示法的性能和效率。
    透過以上方法,本研究希望為圖學習表示法在學習高階相鄰關係方面提供一個新的視角和改進策略,期待過程中的實驗結果與觀察發現能為未來相關的研究提供幫助。
    Graph representation learning is a critical technique in graph analysis that strives to project nodes and edges of a graph into a compressed vector space, thereby better grasping structural and relational aspects. Central to this are higher-order neighboring relationships. These relationships, unlike traditional lower-order ones, offer enhanced analysis and processing potential. They excel at detecting nuanced connections between nodes, such as indirect ties and shared neighbors. Importantly, these higher-order relation- ships highlight specific patterns in the graph structure, like triangle relation- ships and subgraph designs. By mastering these, models can more thoroughly comprehend the overarching graph features. Hence, examining higher-order neighboring relationships is essential for refining the efficacy of graph representation learning.
    In this research, we propose the ”shortest paths” principle to boost the learning capacity of graph representation concerning higher-order neighbor- ing relationships. By harnessing that the shortest paths of up to k steps produce kth-order neighbors, we aim for a more precise portrayal of these relationships. Additionally, we explore the adaptive determination of the span of higher-order neighbors using the spread of shortest path lengths. Such a strategy enables graph representation learning to self-regulate parameters with- out excessive resource expenditure. Leveraging the spread of shortest path lengths helps swiftly determine the range of higher-order neighbors, hence enhancing graph representation learning’s effectiveness.
    With these methodologies, our research offers a novel viewpoint and enhancement approach for graph representation learning, focusing on mastering higher-order neighboring relationships. The anticipated findings from this study will likely benefit subsequent studies in this domain.
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    2. Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international con- ference on Knowledge discovery and data mining, pages 701–710, 2014.
    3. Aditya Grover and Jure Leskovec.node2vec:Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855–864, 2016.
    4. XinRong.word2vec parameter learning explained.arXiv preprint arXiv:1411.2738, 2014.
    5. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
    6. Lisa Ehrlinger and Wolfram Wo ̈ß. Towards a definition of knowledge graphs. SE- MANTiCS (Posters, Demos, SuCCESS), 48(1-4):2, 2016.
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    11. Albert-La ́szlo ́ Baraba ́si and Re ́ka Albert. Emergence of scaling in random networks. science, 286(5439):509–512, 1999.
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    18. Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in neural information processing systems, 30, 2017.
    19. Muhan Zhang and Yixin Chen. Link prediction based on graph neural networks. Advances in neural information processing systems, 31, 2018.
    20. Anh Viet Phan, Minh Le Nguyen, Yen Lam Hoang Nguyen, and Lam Thu Bui. Dgcnn: A convolutional neural network over large-scale labeled graphs. Neural Networks, 108:533–543, 2018.
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    描述: 碩士
    國立政治大學
    資訊科學系
    110753109
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110753109
    数据类型: thesis
    显示于类别:[資訊科學系] 學位論文

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