【作者】 李玉；

【导师】 常毅；

【作者基本信息】 吉林大学 ， 计算机软件与理论， 2023， 博士

【摘要】 随着信息技术发展和大数据时代来临,实际生活中的数据规模呈现爆炸式增长,数据之间通过对应的关联规则相互连接,形成了多种形式的网络数据。如何从网络数据中挖掘潜在的复杂关联模式,并构建预测演化模型,对社会发展具有重要意义。随着网络规模的不断增长,传统的网络分析方法受到时间和空间等计算成本的限制,无法得到灵活应用。网络表示学习旨在将网络中的节点映射到低维的向量表示空间,凭借其在网络分析领域所展现出的卓越网络表征能力,得到了研究者广泛关注。现实世界网络中通常蕴含多种维度的信息,而当前的网络表示学习算法主要针对网络中单一维度信息进行建模,或对多维信息挖掘不充分,影响网络表示学习算法的性能。鉴于此,本文将开展基于多维信息融合的网络表示学习算法研究,探索更有效的多维信息融合和模型构建方法,充分挖掘和利用网络中的多维信息,实现更全面、准确的网络表征,提升节点向量表示质量。具体研究内容和贡献总结如下:(1)提出基于高阶近似与社群结构信息融合的网络表示学习算法,实现网络中隐性社群结构信息的有效挖掘与利用。该算法基于相同社群内部的节点具有相似向量表示,以及节点之间近似程度越大则节点的向量表示越相似的基本假设,利用非负矩阵分解技术将节点间的高阶近似关系和社群结构信息,分别映射到低维向量表示空间,并引入低维社群向量表示建立起他们之间的关系,进而构建多维结构信息融合表征模型,同时提出一种高效的交替迭代优化求解策略,以解决模型求解过程中遇到的非凸优化问题。该算法能够学习到表征能力更强的节点向量表示,其有效性在节点分类、网络重构和链接预测等网络分析任务得到了有效验证。(2)提出基于双重语义信息融合的网络表示学习算法,实现节点冲突语义下的双重语义信息融合。该算法通过为网络中节点学习双重的向量表示,来表征节点在网络中不同角色和相互冲突的语义,引入注意力机制评估不同语义下节点之间的重要性系数,同时基于平衡理论提出图注意力神经网络模型,有效捕获节点之间复杂的语义交互关系,融合节点相互冲突的双重语义信息,并设计一个新颖的目标函数优化该模型。该算法的有效性在真实符号网络的链接符号预测任务上得到了验证。(3)提出基于低频信息与高频信息融合的网络表示学习算法,实现单个向量表示空间中的节点双重相似性融合。该算法基于网络中相似的节点和不相似的节点在向量表示空间中应分别接近或疏远的基本假设,借助谱图理论和图信号处理技术,提出利用网络中的低频信息和高频信息来表征节点之间的相似性和不相似性,通过设计相应的低频图卷积滤波器和高频图卷积滤波器,提取网络中的低频信息和高频信息,并将所有的图卷积滤波器组合成统一的消息传递框架,实现低频信息和高频信息的有效融合,同时设计了一种自门控机制评估低频信息和高频信息的影响。在真实符号网络的链接符号预测任务上的实验结果验证了该算法的有效性,其明显优于最先进的相关研究工作,实现了显著的性能提升。更多还原

【Abstract】 With the development of information technology and the advent of the era of big data,the scale of data in real life has achieved a dramatic growth,and data are further correlated with each other based on related association rules,which form various types of networks.How to mine potential complex association patterns from network data and build evolutionary prediction model is of great significance to the development of society.With the continuous growth of the network scale,the traditional network analysis methods cannot be flexibly used due to the limitation of time and space computation costs.Network representation learning aims to embed nodes into the low-dimensional vector representation space,and has attracted extensive attention from researchers because of its outstanding network representation ability in the field of network analysis.The real-world networks usually contain multiple information;however,previous network representation learning algorithms mainly focus on single information modeling or suffer from insufficient multiple information mining,which degrades the performance of the network representation learning algorithms.In view of this,this dissertation will conduct research on network representation learning algorithm based on multi-information fusion,aiming to explore more effective multi-information fusion and model construction methods.Comprehensive mining and utilization of multi-information in the network can generate more expressive network representation and significantly improve the quality of node representations.Specifically,the main research contents and contributions of this dissertation are summarized as follows.(1)We propose a novel network representation learning algorithm,which effectively preserves the high-order node proximity and incorporates the hidden community structure into node representations.Based on the assumption that nodes within the same community and nodes with higher proximities should have similar vector representations,we propose to adopt non-negative matrix decomposition technique to embed the high-order node proximity and community structure into a single low-dimensional representation space,and then introduce the low-dimensional community vector representation to bridge the connection between them.As a consequence,a multi-structure information fusion representation model is established,and an efficient alternating optimization strategy is further proposed to solve the non-convex optimization problem in this model.Our proposed algorithm can learn more expressive node representations,whose effectiveness has been effectively verified in network analysis tasks such as node classification,network reconstruction and link prediction.(2)We propose a novel network representation learning algorithm for dual semantic information fusion under node semantic conflict.To be specific,we propose to use two vector representations to represent the different roles and conflicting semantics of nodes,and introduce a novel attention mechanism to estimate the coefficients between nodes under different semantics.Based on the balance theory,we further propose a novel graph attentional neural network framework to effectively capture the complex semantic interaction between nodes and fuse the conflicting semantics of nodes.Besides,a novel objective function is also designed to optimize this framework.The effectiveness of our proposed algorithm is verified in link sign prediction task of real-world signed networks.(3)We propose a novel network representation learning algorithm based on the fusion of low-frequency information and high-frequency information to effectively embed both the similarity and dissimilarity between nodes into a single vector representation space.Based on the assumption that similar and dissimilar connected nodes should be close or distant respectively in the representation space,we propose to use the low-frequency information and high-frequency information in networks to preserve the similarity and dissimilarity between connected nodes with the help of spectral graph theory and graph signal processing.To be specific,we design customized low-frequency graph convolution filters and high-frequency graph convolution filters to extract low-frequency information and high-frequency information,and then combine all the graph convolution filters into a unified message passing framework to effectively fuse low-frequency information and high-frequency information.In addition,we propose a novel self-gating mechanism to estimate the impacts of low-frequency information and high-frequency information during message passing.The experimental results on link sign prediction task of real-world signed networks demonstrate the effectiveness of our proposed algorithm,which significantly outperforms the state-of-the-art methods and achieves significant performance improvement.更多还原