基于自適應差異化圖卷積的圖注意力網(wǎng)絡表示學習算法

摘" 要： 為解決傳統(tǒng)圖卷積網(wǎng)絡在處理節(jié)點間復雜關(guān)系時存在的局限性，提出一種基于自適應差異化圖卷積的圖注意力網(wǎng)絡表示學習算法。采用差異化圖卷積網(wǎng)絡，依據(jù)每個節(jié)點自身特征和鄰居信息進行差異化采樣，捕捉節(jié)點間的復雜關(guān)系；再結(jié)合二階段關(guān)鍵相鄰采樣方式優(yōu)先挖掘重要節(jié)點并保留隨機性，完成關(guān)鍵鄰居節(jié)點的采樣；然后結(jié)合圖注意力網(wǎng)絡，通過局部關(guān)注和自適應學習權(quán)重分配將關(guān)鍵鄰居節(jié)點特征聚合到自身節(jié)點上，增強節(jié)點的特征表示；最后經(jīng)網(wǎng)絡訓練，進一步增強網(wǎng)絡表示學習能力。實驗結(jié)果表明，所提出的算法優(yōu)化了節(jié)點聚合程度和邊界清晰度，提高了節(jié)點分類的準確性和可視化效果，并且通過關(guān)注二階鄰居和使用雙頭注意力，在網(wǎng)絡表示學習上也展現(xiàn)出了優(yōu)越性能。

關(guān)鍵詞： 網(wǎng)絡表示學習； 圖卷積網(wǎng)絡； 自適應差異化機制； 節(jié)點采樣； 特征聚合； 網(wǎng)絡訓練； 圖注意力網(wǎng)絡

中圖分類號： TN912?34； TP391" " " " " " " " " "文獻標識碼： A" " " " " " " " " " " 文章編號： 1004?373X（2025）02?0051?04

Graph attention network representation learning algorithm based on adaptive differentiation graph convolution

WU Yulan1， SHU Jianwen2

（1. School of Science and Technology， Nanchang Hangkong University， Jiujiang 332020， China; 2. Nanchang Hangkong University， Nanchang 330063， China）

Abstract： In order to solve the limitation of traditional graph convolution network in dealing with complex relationships between nodes， a graph attention network representation learning algorithm based on adaptive differentiation graph convolution network is proposed. The differentiation graph convolution network is used to conduct differential sampling according to each node's own characteristics and neighbor information， so as to capture the complex relationships between nodes. The two?stage key neighbor sampling method is used to mine important nodes first and retain randomness to complete the sampling of key neighbor nodes. In combination with graph attention network， the key neighbor node features are aggregated to their own nodes by means of local attention and adaptive learning weight distribution， so as to enhance the node feature representation. After training the network， the learning ability of network representation is enhanced further. The experimental results show that the proposed algorithm can optimize the degree of node aggregation and boundary clarity， and improve the accuracy and visualization of node classification. The algorithm also shows superior performance in network representation learning by paying attention to second?order neighbors and using double attention.

Keywords： network representation learning; graph convolution network; adaptive differentiation mechanism; node sampling; feature aggregation; network training; graph attention network

網(wǎng)絡表示學習可以確保學習節(jié)點獲取其低維向量描述[1]，確保相似節(jié)點在向量空間內(nèi)的距離最小化。通過學習節(jié)點表示[2]可以更好地理解圖的內(nèi)在結(jié)構(gòu)和模式，從而為相關(guān)任務提供有力的支持。圖數(shù)據(jù)在各個領(lǐng)域的應用越來越廣泛，如社交網(wǎng)絡、推薦系統(tǒng)、生物信息學等，這些領(lǐng)域中的圖數(shù)據(jù)通常具有復雜的結(jié)構(gòu)和豐富的信息，如何有效地表示和處理這些圖數(shù)據(jù)成為一個亟待解決的問題。……