基于关系强度理论与反馈机制的信息传播动态网络表示

doi:10.19678/j.issn.1000-3428.0063656

摘要/Abstract

摘要： 现有多数网络表示学习方法不能很好地贴合真实世界的信息传播网络，且无法对信息传播动态网络的时间特性与动力学演化特征进行有效建模。提出一种新的信息传播动态网络表示模型，基于关系强度将信息传播动态网络划分为关系网络与传播网络，并分别计算变化节点对的概率密度和邻接矩阵。通过更新节点注意力强度矩阵，聚合节点邻域变化信息，并融合节点邻域变化信息、自身历史信息以及外部影响因素，对信息传播动态网络进行归纳式表示学习。引入反馈机制，将最新的节点表示反馈到邻居节点，解决网络表示不及时的问题，提升网络表示性能。实验结果表明，与Know-Evolve、DyRep、LDG等模型相比，该模型的命中率和平均排名提升显著，与LDG模型相比，其时间效率在Social Evolution数据集和Github数据集上分别提升了91.8%、87.2%。

关键词: 信息传播, 动态网络表示, 关系强度, 反馈机制, 链接预测

Abstract: Most of the existing Network Representation Learning (NRL) methods can not fit well with the real world information dissemination network, and can not effectively model the time characteristics and dynamic evolution characteristics of the information dissemination dynamic network.A new Dynamic Network Representation(DNRep) model of information dissemination is proposed.First, the information transmission dynamic network is divided into relationship and communication networks based on the relationship strength.Second, the probability density and adjacency matrix of variable node pairs are calculated.Then, the attention intensity matrix of the nodes was updated to aggregate the node neighborhood change information.Finally, the inductive representation learning of the information transmission dynamic network is performed by integrating the change information, historical information of the nodes, and external influencing factors.Meanwhile, a feedback mechanism is introduced to feed back the latest node representation information to the neighbor node to solve the delay in network presentation and improve the network representation effect.The experimental results indicate that the hit rate (HITS@10) and Mean Average Rank (MAR) of the proposed model are higher than that of the Know-Evolve, DyRep, LDG, and other models.Compared with the LDG model, the time efficiency of the proposed model based on the social evolution and Github datasets increased by 91.8% and 87.2%, respectively.

Key words: information diffusion, Dynamic Network Representation(DNRep), relationship strength, feedback mechanism, link prediction

中图分类号:

TP391

潘乐, 李弼程, 万旺, 曾荣燊. 基于关系强度理论与反馈机制的信息传播动态网络表示[J]. 计算机工程, 2023, 49(2): 246-253.

PAN Le, LI Bicheng, WAN Wang, ZENG Rongshen. Dynamic Network Representation of Information Diffusion Based on Relationship Strength Theory and Feedback Mechanism[J]. Computer Engineering, 2023, 49(2): 246-253.

http://www.ecice06.com/CN/Y2023/V49/I2/246

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