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Graph Theory and Applications to Scientific Communication

Social networks are a prevalent part of communication in scientific research. Almost 50% of 3000 scientists in a survey conducted by Nature reported that they regularly visit ResearchGate, a social network recently designed especially for researchers, and many others communicate through sites like Facebook and LinkedIn. In particular, as of last June ResearchGate has employed 120 people and has secured over 35 million dollars from investors, lending to the belief that social networks can transform the way research is done. The flow on information across this media and others allows scientists to access reports from the latest research conducted from anywhere in the world faster than ever before.

These networks can be modeled as massive  graphs. One example would be to let the nodes be elements of the set of researchers using a particular social medium, and let an edge connect researcher U and V are aware of each other’s work. In particular, call the edge “strong” if U actively communicates with V about U’s research (whether through actual collaboration or ongoing discussion), and call it “weak” otherwise. Using this example, large social networks like ResearchGate can be modeled as graphs in which edges connect scientists who communicate with each other. This can be a useful model for efficiently capturing the way social networks allow scientists to engage in peer review, discuss, and share data sets. In particular, graphs with many edges indicate that the network represented contains a large amount of communication between many scientists, which is vital to the advancement of their research. Moreover, a network with the property that most if not all nodes satisfy the strong triadic closure property ensures that for researchers A, B, and C, if the node pairs (A,B) and (A,C) are linked by strong edges, then (B,C) is also linked by a strong edge. This would maximize openness about recent work being completed by collaborating scientists. Though simple and lacking in certain information, these graphs can provide a succinct way to assess the level of collaboration a scientific social network facilitates.

Although many have criticized the marketing tactics and use of user information on social media, including ResearchGate, these networks allow for much faster and more efficient communication between scientists than ever before. In fields where collaboration is vital to the advancement of research (so almost every field), such networks may become necessary.





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