Here I will list my ten favorite papers of the 2010s related to my research. It’s not an ordered list, and it will not be too serious, so don’t hate me if your paper is not on the list. Here we go:
R Bliege Bird, E Ready, EA Power, The social significance of subtle signals, 2018
This is a perspective piece in the border zone between anthropology, economics, and network theory that really changed my thinking about social network modeling. Traditionally, when network methods have been used to quantitatively explain cooperation (and similar fundamental human phenomena), authors have only included interactions that are easy to associate with economic outcomes. This paper argues that cooperation, in the long run, can rather be driven by exchange that primarily serves to maintain friendship. Maybe it even points to an economic modeling of networked societies without the homo-economicus assumption that people always optimize stuff.
N Boers, B Goswami, A Rheinwalt, B Bookhagen, B Hoskins, J Kurths, Complex networks reveal global pattern of extreme-rainfall teleconnections, 2019
People occasionally ask me if I think we should use network science to model everything (implying that network science arrogantly tries to conquer problems out of its capacity). My usual answer would be no. “There are many problems that are naturally spatial, weather for example.” Papers on climatic teleconnections prove otherwise, and maybe network modelers should be modest. Such teleconnections have been known for over a century. Essentially it is not only the case that the weather of nearby locations on Earth predicts each other. This paper is perhaps the best introduction to the topic, showing how networks can predict extreme rainfall events.
H Balian, P Bearman, Pathways to violence: Dynamics for the continuation of large-scale conflict, 2018
This is a thought-provoking paper about the timing of killings in the conflict in Northern Ireland, The Troubles. The order of killings affects the public’s view of a group. Balian and Bearman define temporal network motifs (revenge, categorical revenge, and dominance) involving multiple killing events based on their interpretation among the public and, thus, how they could influence further violence. Combining these motifs into a dynamic model of how violence breeds violence, they explain how endogenous effects can prolong conflicts.
J Overgoor, A Benson, J Ugander, Choosing to grow a graph: Modeling network formation as discrete choice, 2019
Discrete choice models have a long history in the social sciences (economics in particular). Recently, as a part of the data science boom (maybe primarily its commercial side), they have revived. This paper uses it to infer network growth mechanisms and convinces me that they are utterly well-suited for that purpose. It strikes a perfect balance between specificity (like in models of the Barabási–Albert type) and generality (black-box machine learning).
TW Wey, F Jordán, DT Blumstein, Transitivity and structural balance in marmot social networks, 2019
This will be about marmot (cute!) social networks, but first, a rant: Network science* has primarily been about method development. I get the impression that purely applied netsci papers are looked down upon. If science is about understanding the world around us, we just need methods that are good enough but applied to relevant data in the right way. Incremental method development can never be more exciting than a paper advancing the theory using new data. So here is an excellent applied netsci (or SNA if you wish) paper. The authors study a great data set of the social interactions of marmots. They have both positive and negative ties, which probably rings your social-balance bell. Indeed, the authors find a weak pattern of social balance, meaning that marmot friends share enemies. Transitivity (an overabundance of positive triangles) is an even stronger signal. Marmots are thus not only cute, but they are also friendly as well (at least to each other).
* Science by people you regularly come across at conferences like NetSci or Complenet.
A Decelle, F Krzakala, C Moore, L Zdeborová, Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications, 2011
This work builds on a combination of three separate but equally deep connections between physics and computer science: 1) A mapping between the stochastic blockmodel and the Potts model of statistical physics, made possible by the recent developments in inverse problems in statistical physics. 2) The insight that problems in computer science can exhibit phase transitions between regimes of hard and easy instances. 3) The belief-propagation algorithm and its connection to the cavity method of statistical physics.
It turns out that, in stochastic blockmodels, the hard instances of the inference problem (i.e., when using stochastic blockmodels for community detection) are precisely the cases when communities are ill-defined. More interestingly, however, is that the transition from well- to ill-defined communities is not gradual (in the large-N limit) but happens as a phase transition. It is thus meaningful to talk about a detectability limit. The authors calculate this limit precisely for the case of communities of equal average degrees and study it numerically in other cases.
D Krioukov, F Papadopoulos, M Kitsak, A Vahdat, M Boguñá, Hyperbolic geometry of complex networks, 2010
This paper really set the tone for the new decade. It is usually said that the advantage of publishing on a new topic early is that one does not need to be too careful, but this is a paper both with groundbreaking ideas and rigor. Another hallmark of greatness is that it took a long time for me to appreciate its grandeur. For a while, I thought it was just a contrived way of modeling networks. I think I got my epiphany from a talk by Ginestra Bianconi. By now, most of you know the idea of the paper: Embedding a network in space is an excellent way of summarizing the network structure. Everyone that ever plotted a graph by Pajek or Gephi knows that. It is only that Euclidean geometry is not the best for empirical networks, much because of the heterogeneous nature of real-world networks made famous by Barabási & al. a decade earlier.
P Sah, JD Méndez, S Bansal, A multi-species repository of social networks, 2019
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 catalog 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 would come to my mind when teaching network science. (This also brings Harary & Read’s tongue-in-cheek “Is the null-graph a pointless concept?” to mind.)
DJ Watts, Common sense and sociological explanations, 2014
If you liked the book (TED talk, etc.), you’d love the paper! This is kinda the AJS version of Everything is Obvious, but being an academic paper, argued in 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 original 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 a mistake that people coming from formal or natural science (like myself) to study social topics (somewhat ironically) tend to make even more than those with formal training in social science.
D Schoch, U Brandes, Re-conceptualizing centrality in social networks, 2016
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 understanding 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.
2 thoughts on “10 papers of the 10s”
Well, you already picked am article from a special issue I co-edited (and specifically recruited Ulrik to write an article), so I already don’t hate you.
“picked _an_ article”.