基于启发式算法的动态社交网络用户对齐

Jiawei He, Li Liu, Zihan Yan, Zhiqian Wang, Min Xiao, Youmin Zhang
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引用次数: 1

摘要

跨社交网络的用户对齐是社交网络分析的一项基本任务,其主要目标是融合不同网络平台上的用户信息。它有利于用户推荐和信息传播等社交网络应用。由于社交网络固有的动态性,动态网络中的用户对齐是实践中的一个关键问题。然而,当网络更新时,大多数对齐模型都会遇到模型再训练,从而导致时间和资源的消耗。为了解决这一问题,提出了一种启发式算法来在动态环境中对齐用户。首先,利用注意力机制获取单个网络中新节点的局部重要度权重;其次,将锚节点作为监督信息,启发式学习新节点在对齐任务驱动下的局部影响;最后,通过保持网络的二阶相似性,该模型可以跨网络对用户进行对齐。在实际数据集上进行的实验结果表明,与几种最先进的算法相比,该模型具有相当的性能,但时间复杂度较低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
User Alignment across Dynamic Social Networks based on Heuristic Algorithm
User alignment across social networks, whose main goal is to fuse user information in different network platforms, is a fundamental task in social network analysis. It can benefit social network applications such as user recommendation and information diffusion. Attributed to the inherent dynamic characteristic of the social networks, aligning users in dynamic networks is a key issue in practice. However, most of the alignment models encounter model retraining when the network is updated, thus result in the consumption of time and resources. To address this problem, a heuristic algorithm is proposed to align users in a dynamic environment. Firstly, the attention mechanism is leveraged to obtain the local importance weight of the new node in a single network. Secondly, the anchor nodes are adopted as supervised information for heuristically learning the alignment task-driven local influence of new nodes. Finally, by preserving the second-order similarity of the network, the model aligns users across networks. Experimental results conducted on realworld datasets prove that the proposed model has a comparable performance but lower time complexity compared with several state-of-the-art algorithms.
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