Networks are everywhere. From the Internet to networks in economics, networks of disease transmission, etc, the imagery of the network pervades our modern culture. What exactly do we mean by a network? What different kinds of networks are there? And how does their presence affect the way that events happen? In the past few years, a diverse group of scientists and researchers, including mathematicians, physicists, computer scientists, sociologists, and biologists, have been actively pursuing these questions and building in the process the new research field of network theory, or the “science of networks”. The study of networks has had a long history in mathematics and natural sciences. Briefly, in 1736, the great mathematician Leonard Euler became interested in a mathematical riddle called the Königsberg Bridge Problem. The city of Königsberg was built on the banks of the Pregel River in what was then known as Prussia, and on two islands that lie in midstream. A popular brain-teaser of the time asked, “Does there exist any single path that crosses all seven bridges exactly once each?” Legend has it that the people of Königsberg spent many fruitless hours trying to find such a path before Euler proved the impossibility of its existence. In the 1780s, Euler invented network theory and for most of the last two hundred years, network theory remained a form of abstract mathematics. A network is made up of nodes and links and mathematicians assumed the links between the nodes were randomly distributed. If there exist, let’s say, 10 nodes and 50 links, they assumed the distribution would be random and each node would get, on average, five links. For years, mathematicians explored the properties of these random-distribution networks. Nowadays, we see the Internet as a source of networks: of people, of groups, of hashtags at Twitter, of social clusters at Facebook, etc. The Internet was originally designed by the American Military to be randomly distributed with no pattern in order to create a communications network that could survive an attack. In the 1990s, physicists began studying the internet because it was an example of a network in which all the nodes and links could be tracked. Computer scientists soon realized that the Web was not randomly distributed. Maps of the web showed that some nodes had huge numbers of links, while most nodes had only a few links. In biomedicine, the impacts of networks have just recently been tackled. In my article in 2013, on the cover of the journal Drug Discovery Today, I wrote that “Social networks can be seen as a nonlinear superposition of a multitude of complex connections between people where the nodes represent individuals and the links between them capture a variety of different social interactions. In addition, “…the emergence of different types of social networks has fostered connections between individuals, thus facilitating data exchange in a variety of fields.” (see my review article “Social networks, web-based tools and diseases: implications for biomedical research”). Networks of people and how to make sense of it are the hot wave today. A social network is a social structure made up of individuals (or organizations) called “nodes”, which are tied (connected) by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige (for more information check “Social Network Analysis – Theory and Applications”). One can identify a person and the connections a specific person has, how influential he or she is and what social interactions a person has. In addition, the science of networks has been applied for businesses since companies that embed themselves into the social network of an industry by creating lots of contacts (links or nodes of the network) to other companies, suppliers, industry magazines, customers, government, and workers will have a tendency to grow, because the node with the most links will get more links. In life sciences, the science of networks transforms data collection into actionable information that will improve individual and population health, deliver effective therapies and, consequently, reduce the cost of healthcare. These novel tools might also have a direct impact in personalized medicine programs, since the adoption of new products by health care professionals in life sciences and peer-to-peer learning could be improved using social networks (see more at my article “The Impact of Online Networks and Big Data in Life Sciences”). Thus, the science of networks could also help the industry gain insights into how people use and react to pharmaceuticals and medical devices; and how they benefit from them. Such accumulation of information could be applied into the product development process as a “lean” process to test new products. The impact of the science of networks and social networking are immensurable in the scientific and biomedical communities. It is just the beginning for this area of research and I believe that there is a lot more to come. Welcome to the Networking Era!