Titlebook: Statistical Analysis of Network Data; Methods and Models Eric D. Kolaczyk Book 2009 Springer-Verlag New York 2009 Graph.bioinformatics.comp

Indigence

Preliminaries,ples, some of the unique challenges inherent in the statistical analysis of network data. Readers sufficiently familiar with both graph theory and statistical inference may wish to skip this chapter and move directly to Chapter 3, after perhaps a quick detour to glance through the examples of Section 2.3 and the issues raised therein.

Resection

Sampling and Estimation in Network Graphs,s various potential complications. In this chapter we formalize the problem of sampling and estimation in network graphs, describe a handful of common network sampling designs, and develop estimators of a number of quantities of interest.

Bronchial-Tubes

Mapping Networks,he form of a visual image. The process by which such images are produced involves a number of distinct steps, with important decisions to be made along the way, and in fact is not unlike the production of cartographic maps. In this chapter, we address the topic of ‘mapping’ networks.

conformity

Insul岛

GLOOM

Network Topology Inference,etwork topology inference, wherein the graph or some portion thereof is unobserved and we wish to infer it from measurements. There are a number of variations on this problem; we examine three particular forms in some depth.

巡回

推迟

Analysis of Network Flow Data,Flows are at the heart of the form and function of many networks, and understanding their behavior is often a goal of primary interest. Here we consider problems of statistical estimation and prediction arising in connection with various types of measurements relating to network flows.

Lacunar-Stroke

neuron