具有竞争隐属性结构的网络模型
A Network Model characterized by a Latent Attribute Structure with Competition
摘要：
寻求一种能够解释、描述、分析和模拟现实世界复杂网络的模型具有极大的实际意义和理论意义。在本文中，我们介绍和研究的网络模型，是基于一个潜在的属性结构：每个节点的特点是由一些功能和两个节点之间的边缘的存在的概率取决于他们共享的功能。根据Indian-Buffet类型的过程选择特征，但是附加有附加到每个节点的附加随机“适应性”参数，该参数确定其将其自身特征传输到其他节点的能力。因此，节点的连通性不仅仅取决于其年龄，因此“年轻”节点也能够竞争并成功获取链接。我们的模型的潜在的二分“节点属性”网络的优点之一是，它依赖于一个简单的解释的几个参数。我们提供了一些理论，以及实验，结果的幂律行为的模型和参数的估计。通过实验数据，我们还展示了所提出的属性结构模型如何自然地捕获大多数局部和全局属性（例如，度分布、连通性和距离分布）。关键词：复杂网络，社会网络，属性矩阵，Indian Buffet过程
英文摘要：
The quest for a model that is able to explain, describe, analyze and simulate real-world complex networks is of uttermost practical as well as theoretical interest. In this paper we introduce and study a network model that is based on a latent attribute structure: each node is characterized by a number of features and the probability of the existence of an edge between two nodes depends on the features they share. Features are chosen according to a process of Indian-Buffet type but with an additional random "fitness" parameter attached to each node, that determines its ability to transmit its own features to other nodes. As a consequence, a node's connectivity does not depend on its age alone, so also "young" nodes are able to compete and succeed in acquiring links. One of the advantages of our model for the latent bipartite "node-attribute" network is that it depends on few parameters with a straightforward interpretation. We provide some theoretical, as well experimental, results regarding the power-law behaviour of the model and the estimation of the parameters. By experimental data, we also show how the proposed model for the attribute structure naturally captures most local and global properties (e.g., degree distributions, connectivity and distance distributions) real networks exhibit. keyword: Complex network, social network, attribute matrix, Indian Buffet process
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
