Multi-view Network Embedding via Graph Factorization Clustering and Co-regularized Multi-view Agreement
Multi-view Network Embedding via Graph Factorization Clustering and Co-regularized Multi-view Agreement
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DOI：
10.1109/icdmw.2018.00145
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发表时间：
2018-11
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通讯作者：
Yiwei Sun;N. Bui;Tsung-Yu Hsieh;Vasant G Honavar
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作者：
Yiwei Sun;N. Bui;Tsung-Yu Hsieh;Vasant G Honavar
现实世界的社交网络和数字平台由通过多种类型的关系（链接）链接到其他个体或实体的个体（节点）组成。基于每种类型的链路的这种网络的子网络对应于底层网络的不同视图。在实际应用中，每个节点通常只链接到其他节点的一个小子集。因此，解决诸如节点标记等问题的实际方法必须科普由此产生的稀疏网络。虽然低维网络嵌入为解决这个问题提供了一种很有前途的方法，但目前大多数网络嵌入方法主要集中在单视图网络上。我们介绍了一种新的多视图网络嵌入（MVNE）算法，用于从多视图网络构建低维节点嵌入。MVNE采用并扩展了一种方法，以单视图节点嵌入使用图分解聚类（GFC）的多视图设置使用的目标函数，最大限度地提高了基于本地和全局结构的基础上的多视图图之间的协议。我们的实验与几个基准的真实世界的单视图网络表明，SVNE产生的网络嵌入的竞争力或上级那些由国家的最先进的单视图网络嵌入方法时，嵌入用于标记网络中的未标记节点。我们的实验与几个多视图网络表明，MVNE大大优于单视图方法的综合视图和国家的最先进的多视图方法。我们进一步表明，即使当目标是预测标签的节点在一个单一的目标视图，MVNE优于它的单视图对应建议的MVNE是能够提取的信息，是有用的标签节点在目标视图中的所有视图。
Real-world social networks and digital platforms are comprised of individuals (nodes) that are linked to other individuals or entities through multiple types of relationships (links). Sub-networks of such a network based on each type of link correspond to distinct views of the underlying network. In real-world applications each node is typically linked to only a small subset of other nodes. Hence, practical approaches to problems such as node labeling have to cope with the resulting sparse networks. While low-dimensional network embeddings offer a promising approach to this problem, most of the current network embedding methods focus primarily on single view networks. We introduce a novel multi-view network embedding (MVNE) algorithm for constructing low-dimensional node embeddings from multi-view networks. MVNE adapts and extends an approach to single view node embedding using graph factorization clustering (GFC) to the multi-view setting using an objective function that maximizes the agreement between views based on both the local and global structure of the underlying multi-view graph. Our experiments with several benchmark real-world single view networks show that SVNE yields network embeddings that are competitive with or superior to those produced by the state-of-the-art single view network embedding methods when the embeddings are used for labeling unlabeled nodes in the networks. Our experiments with several multi-view networks show that MVNE substantially outperforms the single view methods on integrated view and the state-of-the-art multi-view methods. We further show that even when the goal is to predict labels of nodes within a single target view, MVNE outperforms its single-view counterpart suggesting that the MVNE is able to extract the information that is useful for labeling nodes in the target view from the all of the views.