TABLE C-2 Real-World Networks Appearing in Courses


Type of Network

Infrastructure and communications networks

Power grid


Public switched telephone network

Information and content distribution networks

World Wide Web




Social networks



Social filtering and recommendations



Computing networks

Neural nets

Petri nets

Cellular automata

Interacting intelligent agents

Engineering systems

Control networks

Integrated circuits

Queuing networks

Process networks

Transportation networks

Supply chains and manufacturing

Research networks

Scientific grid


Blogs and online journals

Military networks

Terrorist networks

Intelligence networks

Logistics networks

Biological networks


Gene and protein interactions


Regulatory and control networks

Ecological networks and food webs

Viruses and epidemics

finally, the link strength or weight, which characterizes the nature of the interactions between different nodes.

Based on these measures, real networks may be classified in perhaps two or three major classes. First, there is a class known as regular networks, or graphs, in which the degree of all the nodes assumes the same value or only a few discrete values and the underlying network has a regular, repetitive structure. Such regular graphs approximate the structure of most crystals, as well as a number of other objects, engineered and natural, from the retina of the eye to the roads of some large cities (like New York). Much attention, however, has focused on random networks, systems in which the nodes are randomly connected to each other. In such networks the degrees follows a Poisson distribution. Despite their important role in network theory, we do not know of major real networks that would be fully random. Finally, the availability of large-scale network maps has led to the discovery that many real networks are neither regular nor fully random but, rather, scale-free. They have a heavily tailed degree distribution—that is, there are significant (order of magnitude) differences in the degree of different nodes. Scale-free networks describe the cell, the Web, the Internet, and many collaboration and social and economic networks. While many real networks are intermediate between these three classes, this classification captures some of the basic primitives used in many courses on networks and most networks are characterized in terms of the three classes.

An important question surfacing in many network-science-related courses is the following: What processes and mechanisms give rise to the network characteristics discussed in the preceding section? A closely related question is this: How do we generate networks with structural characteristics that mimic the properties of selected real networks? Network models, introduced to answer these two questions, are an important part of most network science courses (see Table C-4). These models have two main functions. First, some models aim to mimic, in a simplified form, the emergence and evolution of real networks, helping us to understand the mechanism responsible for the formation of real networks. Second, to test the impact of selected network characteristics on the network’s behavior, we need to gener-

TABLE C-3 Content of a Typical Network Science Course



Core concepts

Real-world networks

Characterization and classifying networks and their components

Network modeling

Network interpretation and processes

Flow and routing

Aggregation and growth

Communication and coordination

Behavior: networks as dynamic entities

Performance and scaling


Routing and congestion


Engineering methods in network science

Network design

Network analysis

Applications of network science

Information and communication network

Biological networks

Social networks

Control and mechanical systems

Industrial applications

Military applications

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