论文标题
进行网络功能学习抽样
Sampling for network function learning
论文作者
论文摘要
给定的图形，其中图形的节点和边缘都与一个或几个值相关联，必须根据图中的节点及其连接的节点来定义给定节点的任何网络函数。通常，将相同的定义应用于整个图形或任何给定的子图将导致系统上不同的网络函数。在本文中，我们考虑了图形采样方法对网络功能学习的可行性，以及基于样本图的相应学习方法。当边缘从一开始，或图形太大（或动态）以至于无法完全处理时，这可能是有用的。
Given a valued graph, where both the nodes and the edges of the graph are associated with one or several values, any network function for a given node must be defined in terms of that node and its connected nodes in the graph. Generally, applying the same definition to the whole graph or any given subgraph of it would result in systematically different network functions. In this paper we consider the feasibility of graph sampling approach to network function learning, as well as the corresponding learning methods based on the sample graphs. This can be useful either when the edges are unknown to start with or the graph is too large (or dynamic) to be processed entirely.
