Exploring Evolution of Dynamic Networks via Diachronic Node Embeddings

Exploring Evolution of Dynamic Networks via Diachronic Node Embeddings
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通过历时节点嵌入探索动态网络的演化

DOI:
10.1109/tvcg.2018.2887230
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发表时间:
2020-07
影响因子:
5.2
通讯作者:
Hai Lin
Hai Lin
中科院分区:
计算机科学1区
文献类型:
--
作者:
Jin Xu;Yubo Tao;Yuyu Yan;Hai Lin

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动态网络随着其结构的变化而发展。如何有效地从结构和时间两方面探索动态网络的演化仍然是一个具有挑战性的问题。在本文中,我们提出了一种可视化分析方法来交互式地探索动态网络在其结构背景下的时间演变。提出了一种新的历时节点嵌入方法来学习向量空间中节点的结构特征和时间特征的潜在表示。然后使用历时节点嵌入来发现具有相似结构接近度和时间演化模式的群落。可视化分析系统的目的是使用户能够可视化地探索节点、社区和网络作为一个整体在其结构和时间属性方面的演变。我们使用人工和真实世界的动态网络来评估我们方法的有效性,并与以前的方法进行比较。
Dynamic networks evolve with their structures changing over time. It is still a challenging problem to efficiently explore the evolution of dynamic networks in terms of both their structural and temporal properties. In this paper, we propose a visual analytics methodology to interactively explore the temporal evolution of dynamic networks in the context of their structure. A novel diachronic node embedding method is first proposed to learn latent representations of the structural and temporal features of nodes in a vector space. Diachronic node embeddings are then used to discover communities with similar structural proximity and temporal evolution patterns. A visual analytics system is designed to enable users to visually explore the evolutions of nodes, communities, and the network as a whole in terms of their structural and temporal properties. We evaluate the effectiveness of our method using artificial and real-world dynamic networks and comparisons with previous methods.
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