Latent geometry of bipartite networks
Latent geometry of bipartite networks
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DOI：
10.1103/physreve.95.032309
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发表时间：
2017-03-08
影响因子：
2.4
通讯作者：
Krioukov, Dmitri
中科院分区：
文献类型：
--
作者：
Kitsak, Maksim;Papadopoulos, Fragkiskos;Krioukov, Dmitri
尽管有大量的二分网络系统，但与单分网络相比，对它们的组织原理的研究较少。二分网络通常在将它们投影到两组节点之一后进行分析。作为投影的结果，如果相同集合的节点在二分网络中至少有一个共同的邻居，则它们被链接在一起。尽管这些投影允许人们使用为单部网络开发的工具来研究二部网络，但单模式投影会导致信息的大量丢失以及具有完全连接子图的投影网络的人为膨胀。在这里，我们追求一种不同的方法来分析二分系统，该方法基于这样的系统具有潜在度量结构的观察：网络节点是潜在度量空间中的点，而连接更有可能在距离较短的节点之间形成。这种方法是为单部网络开发的，而对它对二部系统的适用性知之甚少。在这里，我们充分分析了一个简单的潜在几何模型的二部网络，并表明，该模型解释了许多真实的二部系统的特殊结构特性，包括分布的共同邻居和二部集群。我们还分析了该模型中单模式投影的几何信息损失，并提出了一种有效的方法来推断节点之间的潜在成对距离。揭示潜在的几何基础真实的二分网络可以在不同的领域中找到应用，从构建有效的推荐系统到理解细胞代谢。
Despite the abundance of bipartite networked systems, their organizing principles are less studied compared to unipartite networks. Bipartite networks are often analyzed after projecting them onto one of the two sets of nodes. As a result of the projection, nodes of the same set are linked together if they have at least one neighbor in common in the bipartite network. Even though these projections allow one to study bipartite networks using tools developed for unipartite networks, one-mode projections lead to significant loss of information and artificial inflation of the projected network with fully connected subgraphs. Here we pursue a different approach for analyzing bipartite systems that is based on the observation that such systems have a latent metric structure: network nodes are points in a latent metric space, while connections are more likely to form between nodes separated by shorter distances. This approach has been developed for unipartite networks, and relatively little is known about its applicability to bipartite systems. Here, we fully analyze a simple latent-geometric model of bipartite networks and show that this model explains the peculiar structural properties of many real bipartite systems, including the distributions of common neighbors and bipartite clustering. We also analyze the geometric information loss in one-mode projections in this model and propose an efficientmethod to infer the latent pairwise distances between nodes. Uncovering the latent geometry underlying real bipartite networks can find applications in diverse domains, ranging from constructing efficient recommender systems to understanding cell metabolism.