ten nine eight weeks left of the decade, obviously we need to start making top-lists! The idea here is to list ten favorite papers of the 2010s, related to my research, and add one item per week. It’s not an ordered list and it will not be too serious, don’t hate me if your paper is not on the list. Here we go:
The ‘10s is arguably the decade of data science. Remember when it started “data science” and “big data” were not even in the dictionary. In this era, papers presenting data sets should thus be in the limelight, and this is probably my favorite such. It features a catalogue of animal social networks that is very useful for my small-NetSci line of research. (Some great science of mine based on it coming up in the next decade.) Some networks are actually the smallest possible, i.e. empty, which is a bit profound—if you measure a social network, but there is no interaction, is it useless? Of course not (since it says something about the system), but it’s also the last example that’d come to my mind when I’m teaching network science. (This also brings Harary & Read’s tongue-in-cheek “Is the null-graph a pointless concept?” to mind.)
If you liked the book (TED talk, etc.), you’ll love the paper! This is kinda the AJS version of Everything is Obvious, but being an academic paper argued is a more precise and academic way (thus an even better read).
I think the fallacy of building scientific arguments on common sense is even more general than what this paper discusses. Social and behavioral science is the human endeavor to understand the world of people. Of course the primary ideas in building theories must be allowed to come from one’s own experience. But whether they generalize to a publishable result depends on if they could be supported by data. (I also note that this is mistake people coming from formal or natural science (like myself) to study social topics (somewhat ironically) tend to make even more than those with a formal training in social science.)
If you wish, network science is all about indirect connections. Everything of interest could be seen as a question of how nodes influence other nodes through intermediate nodes. These connections form paths, so it is natural to see paths as the key to understand networks. This is the gist of Schoch and Brandes’s paper that goes on to show that many centrality measures can be unified by so called path algebras. The path algebras can break down centrality ranking into classes such that no vertex in one class is ranked above a vertex in the other class by any of these centrality measures. S & B go on to show that previous axiomatic approaches to centrality measures can be simplified much by path algebras.