基于二部聯(lián)合網(wǎng)絡(luò)的屬性缺失圖學(xué)習(xí)方法

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中圖分類號：TP18;O157.5文獻標(biāo)識碼：A

Abstract：Aiming at the problem of missing node attributes in graph data，we proposes a novel attribute missing graph learning framework. The framework maps node attributes to edge attributes by reconstructing the structural joint bipartite network. This enables attribute completion and graph tasks to be performed together under a unified framework that can handle both continuous and discrete missing data. According to the attribute homogeneity and structural homogeneity of the attribute graph，we propose an attribute missing representation learning method，which introduces edge embeddings and attention mechanisms to capture the correlations between attribute-attribute and structure-attribute in structural joint bipartite network to enhance the missing attribute learning. Experimental results on four real-world datasets show that our framework outperforms the baselines in both atribute completion tasks，validating the effectiveness of the method.

Keywords： graph neural network；attribute completion; node classification; bipartite graph;topology of networks

0 引言

圖結(jié)構(gòu)數(shù)據(jù)可以自然地模擬來自社會[1-2]、金融[3-4]、生物[5]等領(lǐng)域的真實數(shù)據(jù)。（剩余12959字）