DNA: Dynamic Social Network Alignment
DNA: Dynamic Social Network Alignment
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DOI：
10.1109/bigdata47090.2019.9006430
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Li Sun;Zhongbao Zhang;Pengxin Ji;Jian Wen;Sen Su;Philip S. Yu
中科院分区：
文献类型：
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作者：
Li Sun;Zhongbao Zhang;Pengxin Ji;Jian Wen;Sen Su;Philip S. Yu
社交网络对齐，即根据其共同用户调整不同的社交网络，正在受到学术界和工业界的极大关注。所有现有的研究都认为社交网络是静态的，而忽略了其内在的动态性。事实上，社交网络的动态包含个人的歧视模式，可以利用它来促进社交网络的调整。因此，我们首次提出研究调整动态社交网络的问题。为此，我们提出了一种新颖的动态社交网络对齐（DNA）框架，这是一种针对深层神经架构的统一优化方法，以展开富有成效的动态来执行对齐。然而，它在建模和优化方面都面临着巨大的挑战：（1）为了对网络内动态进行建模，我们探索了好友演化中潜在模式的局部动态以及与邻居的表示相似性的全局一致性。我们设计了一种新颖的深度神经架构来获得双重嵌入，捕获每个用户的局部动态和全局一致性。 (2) 为了对网络间对齐进行建模，我们从每个动态社交网络的双重嵌入中利用个体的潜在身份。我们设计了一种统一的优化方法，与所提出的深度神经架构相互作用，以构建身份嵌入的公共子空间。 (3)为了解决这个优化问题，我们设计了一种具有坚实理论保证的有效交替算法。我们对现实世界的数据集进行了广泛的实验，结果表明所提出的 DNA 框架大大优于最先进的方法。
Social network alignment, aligning different social networks on their common users, is receiving dramatic attention from both academic and industry. All existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic social Network Alignment (DNA) framework, a unified optimization approach over deep neural architectures, to unfold the fruitful dynamics to perform alignment. However, it faces tremendous challenges in both modeling and optimization: (1) To model the intra-network dynamics, we explore the local dynamics of the latent pattern in friending evolvement and the global consistency of the representation similarity with neighbors. We design a novel deep neural architecture to obtain the dual embedding capturing local dynamics and global consistency for each user. (2) To model the inter-network alignment, we exploit the underlying identity of an individual from the dual embedding in each dynamic social network. We design a unified optimization approach interplaying proposed deep neural architectures to construct a common subspace of identity embeddings. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed DNA framework substantially outperforms the state-of-the-art methods.