In short: nuances between Network Science, Social Network Analysis, and Network Analysis

7 min read

Here is another part of my thesis that you may find useful on its own. With academic references.

The two main network-specific fields are social network analysis (SNA) and network science (NS). The scientific literature also mentions network analysis (NA), mostly referring to the practices of analyzing networks, but also, by extension, to their methodological critique (figure below). It is then useful to clarify the nuances between the three notions.

Network science (NS), social network analysis (SNA) and network analysis (NA) are three distinct domains. Although they overlap, each has its own specific knowledge and/or practices; none can be summarized as a combination of the others.

Network analysis (NA) is a set of research practices that progressively stabilized on specific methodological foundations. Although there is a relative consensus on its theoretical ground, its practices are not unified. They include both what Erikson (2013) calls the formalist approach, based on a “structuralist interpretation” (networks are phenomena, e.g. in Georg Simmel’s sociology), and the relationalist approach, that “rejects [the] essentialism” of the network (as an apparatus to know, e.g. in the natural sciences).

Although NA is primarily a practice, we can also see it as a field; a field about a practice. And this practice is much older than its formalization as a field. The overview proposed by Borgatti et al. (2009) places the point of origin of NA within the social sciences (with Moreno’s sociograms, 1934), before it “radiated into a great number of fields, including physics and biology” during the nineties. For these authors, “network analysis” is not a field but a longstanding practice progressively formalized into SNA, and later, NS. However, other authors acknowledge it as an independent field (Brandes and Erlebach, 2005; Chiesi, 2015), with its own methodological knowledge derived from graph theory, and its own theoretical discussions (e.g. Barnes & Harary, 1983; Butts, 2009). Even so, NA is centered on practice. Brandes and Erlebach for instance find “adequate to treat network analysis as a field of its own” (2005). But they add that “[f]rom a computer science point of view, it might well be subsumed under ‘applied graph theory,’ since structural and algorithmic aspects of abstract graphs are the prevalent methodological determinants in many applications, no matter which type of networks are being modeled.” Similarly, for Chiesi NA “can be regarded as a set of techniques with a shared methodological perspective” (2015). NA is part of other fields, including NS and SNA, as a practice. But NA as a field is additionally concerned with the foundations of this practice. It has its own intellectual and cultural space.

Social network analysis (SNA) predates both NS and NA. Indeed, the network is a key idea to multiple schools of thought in the social sciences, from Moreno’s sociograms (1934) to White’s kinship models (1963), Milgram’s “six degrees of separation” (1967), Lévy-Strauss’ structural anthropology (1973) and Granovetter’s “strength of weak ties” (1973). These long and rich considerations on the relational nature of the social coalesced into the field of SNA. In accordance with this thick heritage, the field sustains an in-depth discussion on the empirical nature of the networks it studies, and pays close attention to the various methodological issues tied to the use of its instruments.

Network science (NS) emerged during the late nineties as a “highly interdisciplinary research area” (Börner et al., 2007; see also Barabási, 2016; Hidalgo, 2016) around the object of complex network. Graph theory is generally presented as its point of origin, and more precisely the random graph model (Erdős & Renyi, 1960). As scholars across various disciplines realized that their empirical networks were usefully described by the newly formalized concept of complex network, the theories of NS disseminated as an operational toolkit for analyzing networks. It is worth mentioning that the apparition of the web, and later social media, provided plenty of network data that called for a democratization of network analysis methods. Epistemic clashes in network science features an in-depth inquiry into the epistemic foundations of the field, including a presentation of its key concepts and an analysis of its main controversy.

Network science (NS), social network analysis (SNA), and network analysis (NA) are three distinct domains. Such similar names are unfortunate, because it downplays important differences. I acknowledge the profound intrication of the fields; my attempt to make the distinction is not about enforcing a clear demarcation between them. I rather aim at clarifying the fringe of knowledge and practices that are, in each field, incompatible with the other two. Indeed, despite their important overlap, key specificities subsist, that notably explain that NA resists dissolving into NS and/or SNA.

Differences between NA and SNA. They boil down to the fact that from the perspective of NA, a network is a set of inscriptions. NA is concerned with networks-as-data: it can be used with potentially any data set, as long as it is formatted as a network. Thus it differs from SNA on two notable points: (1) NA is also interested in non-social networks (e.g. protein-gene interactions), and (2) it is not concerned with the gap between a social phenomenon and its reduction as a network. Since SNA is about networks-as-phenomena, it is deeply concerned about the part of reality that is left aside datafied networks; while for NA the datafied network is a given. This is not to say that networks-as-data can dodge the ordeal of empirical validity, but rather that this discussion takes place outside of NA, in the field where empirical data come from (e.g. molecular biology).

Differences between NA and NS. Contrary to NS, NA aims at just describing networks. NS is a broad field with its own subcultures and practices, relatively united around the notion of complex network; it comes with its own research questions such as: Can we find universal laws capable of explaining the pervasiveness of complex networks? NA, by contrast, is quite agnostic in terms of research questions. It focuses instead on how to describe and account for a given network. Analyzing networks is part of what network scientists do, of course, but NA also extends beyond the domain defined by the research questions of NS. An idiographic account of a particular network does not typically meet the publication criteria of NS, contrary to media studies (e.g. Áragon et al., 2013), sociology (e.g. Adamic and Glance, 2005), or digital humanities (e.g. Grandjean, 2016). As an example, the journal Network Science (Brandes et al., 2013) publishes many different types of papers, part of which are case-based, but none of them are just empirical accounts.

The overlap between NA, NS and SNA. While the three fields aim to accomplish different things, they overlap in how they deal with networks. The algorithms and metrics used are largely the same, despite the presence of some specificities in each field. On a practical level, the three fields can easily meet. For instance, within the computational social sciences (Lazer et al., 2009) intersect (1) the figure of the complex network and the practice of modeling, characteristic of NS, (2) the knowledge on the social of SNA, and (3) the empirical practice of NA. The techniques developed by NS disseminated to SNA, but as Freeman (2008) narrates, some methods also travelled the other way around. This important overlap makes it tempting to simplify the situation by assuming that one of the fields subsumes or otherwise contains the other two (pick your favorite!). But doing so would just make us blind to the epistemic trouble caused, at the fringe of each field, by its peculiarities.


Adamic, L. A. and Glance, N. (2005) ‘The political blogosphere and the 2004 U.S. election: divided they blog’, in Proceedings of the 3rd international workshop on Link discovery (LinkKDD ’05). Association for Computing Machinery, New York, NY, USA, 36–43. DOI:

Aragón, P., Kappler, K. E., Kaltenbrunner, A., Laniado, D. and Volkovich, Y. (2013) ‘Communication dynamics in twitter during political campaigns: The case of the 2011 Spanish national election’, Policy & internet, 5(2), 183-206.

Barabási, A. L. (2016) Network science. Cambridge university press.

Barnes, J. A. and Harary, F. (1983) Graph theory in network analysis, Social networks, 5(2), 235-244.

Borgatti, S. P., Mehra, A., Brass, D. J. and Labianca, G. (2009) ‘Network analysis in the social sciences’, Science, 323(5916), 892-895.

Börner, K., Sanyal, S. and Vespignani, A. (2007) ‘Network science’, Annual review of information science and technology, 41(1), 537-607.

Brandes, U. and Erlebach, T. (2005) ‘Introduction’, in: Brandes U., Erlebach T. (eds) Network Analysis. Lecture Notes in Computer Science, vol 3418. Springer, Berlin, Heidelberg.

Brandes, U., Robins, G., McCranie, A. and Wasserman, S. (2013) ‘What is network science?’, Network science, 1(1), 1-15.

Butts, C. T. (2009) ‘Revisiting the foundations of network analysis’, Science, 325(5939), 414-416.

Chiesi, A. M. (2015) ‘Network Analysis’, International Encyclopedia of the Social & Behavioral Sciences, 518–523.

Erdős, P. and Rényi, A. (1960) ‘On the evolution of random graphs’, Publ. Math. Inst. Hung. Acad. Sci, 5(1), 17-60.

Erikson, E. (2013) ‘Formalist and relationalist theory in social network analysis’, Sociological Theory, 31(3), 219–242.

Freeman, L. C. (2008) ‘Going the Wrong Way on a One-Way Street: Centrality in Physics and Biology’, Journal of Social Structure, 9(2), 1–15.

Grandjean, M. (2016). ‘A social network analysis of Twitter: Mapping the digital humanities community’, Cogent Arts & Humanities, 3(1), 1171458.

Granovetter, M. S. (1973) ‘The strength of weak ties’, American journal of sociology, 78(6), pp.1360-1380.

Hidalgo, C. A. (2016) ‘Disconnected, fragmented, or united? a trans-disciplinary review of network science’, Applied Network Science, 1(1), 1–19.

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., … and Jebara, T. (2009) ‘Computational social science’, Science, 323(5915), 721-723.

Lévy-Strauss, C. (1973) Anthropologie structurale deux. Paris: Plon, 33-34.

Milgram, S. (1967) ‘The small world problem’, Psychology today, 2(1), 60-67.

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Hidden structures in hairballs, and how to see them

15 minutes read

At the moment, I am writing my PhD dissertation. As this part of my draft can be useful to those who work with network maps, I reproduce it here right away. I propose an example-based exploration of the Gestalt approach to the semiotics of network maps, and I explain why and how we can see structures in hairball networks. My argument is essentially visual. For an overview of Gestalt theory, see Wagemans et al., 2012. On Gestalt and network visualization, see Bennett et al., 2007; Kobourov et al., 2015.

When it comes to network visualization, the most important Gestalt principle is perceptual grouping. “Historically, the visual phenomenon most closely associated with perceptual organization is grouping: the fact that observers perceive some elements of the visual field as ‘going together’ more strongly than others.” (Wagemans et al., 2012). Multiple factors influence what we perceive as groups, but two of them stand out in our situation: proximity, and closure. In short, you may perceive a set of dots more or less as a textured shape (figure 1), provided that they are distributed homogeneously, that the contour draws a recognizable shape, and that these shapes are well separated. Technically, your perception works the other way around: you associate the dots because they are close, and this interplay of proximity and distance makes you see a contour, then you associate it to a known shape. This process is not perfect, however. The shapes we see have some degree of ambiguity, and different people may perceive different groups.

Figure 1. A homogeneous distribution of dots can be perceived as a group with a given shape. These shapes may be ambiguous, and get perceived differently by different persons.

The topological structure is mainly mediated by the node placement, to the point that some authors propose to not display the edges at all (Noack, 2007). Here I will simplify the problem by considering that nodes are represented by dots of the same size and color. Size and color are known visual variables (in the sense of Bertin, 1967) but also influence the perception of groups (Wagemans et al., 2012) and should be accounted for in a complete perceptive model of network maps. Here, however, I only touch on this subject. My main concern is our perception of node groups, that we intuitively interpret as topological clusters. Gestalt theory provides tools to discuss this intuition.

As a starting point, let me emphasize the importance of gaps in our perception of groups. Gaps are places where the continuum of proximities breaks. We see groups when (i.e. because) there are gaps between them. We need gaps to separate clusters visually (figure 2). If the gaps are too small or inexistent, we do not perceive different groups. Different persons will agree more easily on the groups with big gaps than small gaps. Big gaps make visual groups less ambiguous and easier to see. This is, Gestalt says, how our cognitive system works.

Figure 2. A same set of three groups of dots may be perceived as one group if they are close enough.

Unfortunately, computations do not follow the same principles as human vision. Although a force-directed node placement algorithm makes, in some sense, groups, it does not care about the same gaps. Intuitively, the eye looks at the gaps border-to-border, while the algorithm cares about the distance between middle points, between statistical averages (figure 3). When there are large gaps, the eye and the algorithm agree. But when the gaps are small or inexistent, it is possible that the algorithm “sees” a gap where the human eye does not.

Figure 3. A visual intuition of the disagreement between the algorithm and the eye. Human vision looks at borders, while the algorithm cares about barycentres.

As an experimental illustration of this phenomenon using networks, I will use a planted partition model, also called stochastic block model. Here I build a network with two groups of nodes, and create links between the nodes by following a statistical rule. For two nodes in the same group, I will create a link with a probability Pin. For nodes in different groups, I will use a probability Pout. As long as Pin is bigger than Pout, each group is promised to be a cluster in a topological sense (e.g. modularity). I choose Pin and Pout so that Pin is smaller, and the sum equals 100%. When Pin is large, the clusters are well-defined. When Pin is 50%, the community structure has entirely disappeared and we just have a random network (indeed Pin = Pout = 50%).

I generate a series of networks with a decreasing probability Pin (figure 4). As expected, the most separated groups topologically (Pin high) are the most separated visually. As Pin gets closer to 50%, the two groups start to merge. At 70%, the general contour starts to look like a circle, but there is still a gap. At 60%, there is no more gap. Yet there is still a topological structure. Indeed, the layout correctly positions the nodes in the right group; but the groups are stuck to each other. We still see the groups because we have colors, but from just the node positions, we would not perceive a cluster. Gestalt theory says why: we need gaps and contours to perceive groups.

Figure 4. Planted partition networks: nodes in the same group have Pin chances to be connected, nodes in different groups, Pout. Each group has a distinct color. The most separated groups topologically are also the most separated visually. But when Pin is low, the node placement does not display a gap. Layout: Force Atlas 2, default settings.

You may think that at a Pin of 60%, the two groups are too entangled to be considered distinct. There is some sense to this point, yet it does not change that there is some topological structure, and more importantly, that the force-directed layout is able to display it. Even though we do not see it. It displays it in the sense that nodes of the same group get placed next to each other. The layout algorithm is so consistently successful at retrieving these groups (figure 5) that we cannot deny their existence. But we do not see it because there is no gap. Here I only give a visual argument, but we could quantify it.

Figure 5. Planted partition networks with Pin= 60%. Layout: Force Atlas 2, default settings.

You may also think that we actually see the two groups, even without the colors. Take your chance at making the distinction between a planted partition of Pin=60% and a random network in figure 6, using solely the node placement. I doubt you see any gaps, and without them, the groups do not appear. But it does not mean that there is no locality principle: close nodes may still be, on average, more connected.

Figure 6. Six of these networks are planted partitions with Pin = 60%, and six are random networks with a connection probability of 50%. Layout: Force Atlas 2, default settings.
Answer: the random networks are, in reading order, 3 to 5 and 7 to 9.

Hidden structures in hairballs

Many authors have blamed network visualization, and notably force-directed networks, for producing hairballs (e.g. Correa and Ma, 2011; Van Den Elzen and Van Wijk, 2014). Hairballs are typically networks such as those of figures 5 and 6, with a “significant node occlusion and link crossings that can almost completely fill the inter-node space” (Edge et al., 2018). Nocaj et al. (2015) and Edge et al. (2018) propose a sparsification approach based on reduction to subgraphs to tackle this specific problem, and Dianati (2016) a pruning approach. I do not deny the practical problem of hairballs (one does not see the structure), and I certainly think that sparsification methods have applications. Yet the hairball is most often a straw man.

The hairball rhetoric is easy to track in academic literature, since it precisely employs the term “hairball”. The argument is always to blame the layout for failing at representing the structure. But this statement is never grounded on a model of how we perceive network maps, and relies instead on a series of noteworthy assumptions. It assumes that network maps are, to some extent, self-evident. It assumes that the (community) structure is translated by the layout as visual groups. And finally, it assumes that the absence of visible groups is a failure. These assumptions are, in fact, wrong. The layout does not exactly translate the structure as visual groups, but as a locality principle. The difference is the presence of visual gaps: the layout may place same-cluster nodes together in a way that produces no visual gaps, which makes these groups invisible to the human eye. From an ethnographic perspective, this rhetoric is etic, it assesses the algorithm from the outside, using criteria alien to its own functioning. But it does not allow predicting the behavior of the algorithm, because it misses the way it assembles a visual structure (even though we do not see it). I claim that there are structures in hairballs, and that we may see them if we learn the way the algorithm communicates them. By mobilizing Gestalt theory, I hope to sketch an emic approach to hairballs.

Firstly, not all networks produce hairballs. Here, I assume that the layout algorithm has been properly parametrized (a high “gravity” setting in Force Atlas 2 tends to produce a hairball regardless of the network’s structure, and that is not a true hairball). So, if a network is properly visualized as a hairball, this does tell us something about its structure: it tells that it is pretty dense. You may find the result banal, it nevertheless discriminates between different structures, so it mediates the topology. Secondly, network layouts are bad at manifesting visual gaps, because in some sense, it does not matter to them. But they are good at placing connected nodes next to each other (on average). It is possible that nodes placed together in the pictures, i.e. local areas, have a meaning. It is possible for clusters to be present even in the absence of any gaps. You may think of it as a bunch of clay balls smashed together (figure 7): the local structures are still present, but they touch each other. Now, to be clear, not all hairballs hide such structures. But since we know they may exist, we can check for them. We may for instance reveal them by coloring clusters: if the hairball has a structure, the colors will not be mixed but gathered in coherent localities. Those clusters may be obtained from the data set (categorical node attributes) or from a community detection algorithm, for instance modularity clustering. For instance, Edge et al. (2018) claim that hairballs lack “clear separation and grouping” but in their example (figure 8 on the left), the hairball has a clear community structure, if we look at colors, despite the lack of visual gaps.

Figure 7. A metaphor for hairball networks: there may be clusters, but they are smashed together like plastic clay balls.
Figure 8. From left to right, the pruning of hairballs, using the layout Force Atlas 2, as illustrated in Edge et al. (2018, figure 2). “How skeletal community structure emerges from an initial ‘hairball’ graph.”

In practice, the contour of the hairball may tell about the internal clustering. In figure 9 the classic network C. Elegans (Watts and Strogatz, 1998) is spatialized by the LinLog energy model. As there are no clear gaps, we do not perceive distinct groups in the node placement (9-a) unless we add additional information, such as color (9-b). The relatively homogeneous distribution of nodes is perceived as a weirdly shaped stain (9-c). The shape of this stain mediates the topology, even in the absence of clear groups. Intuitively, some denser groups of nodes may pull the network in different directions, under the action of the algorithm. This stretches the contour in certain directions, and may create bumps and headlands in specific directions. Intuitively, those are partial clusters that create distinct localities despite being interlinked (hence the absence of visual gaps). I do not offer any proof of this statement here, but you can check, in the example below, that the clusters found by modularity maximization (colors in 9-b) match the elongation and protrusions in the contour of the network (9-c).

Figure 9. The neural network of C. Elegans (Watts and Strogatz, 1998) spatialized by the LinLog energy model. (a) No color: no clear gaps allow to separate groups visually: it can be considered a hairball. (b) Nodes colored by modularity clustering (modularity = 0.346). Three homogeneous areas appear, showing a locality principle. (c) Poles and elongation highlighted. Intuitively, the modularity clusters correspond to the ends of the shape that pull it apart.

I propose to call these pseudo-clusters “poles”, as they tend to appear on the sides (figure 10). The lack of visual separation is meaningful: poles are not only linked, but also weakly separated. A number of nodes lie in-between the two poles, creating an ambiguous area with no clear divide. The poles themselves, however, may be sufficiently dense to be considered topological landmarks. From a clustering perspective, one may say that each pole strongly defines a weakly delineated cluster. The existence of the cluster is robust, but its limits are ambiguous. The pole itself, however, acts as a local anchor. In figure 11 the same community detection algorithm (Blondel et al., 2008) was applied to the same network (same as figure 9). I used the Gephi implementation, which is non-deterministic (Lambiotte et al., 2008). You can observe that the poles always end up in different clusters, but that the boundaries between clusters are not stable. The existence of clusters around poles is stable, but their boundaries are not. In other terms, each denser zone on the side is non-ambiguously local, but most nodes in the middle are ambiguously connected to the different poles.

Figure 10. Poles are weakly separated clusters that one can detect by looking at denser areas on the sides of a network spatialized by a force-driven algorithm.
Figure 11. Six renditions of the same community detection algorithm. The poles consistently end up in different clusters, but the boundaries are not stable. Note: one rendition found 4 clusters, contrary to the others. Network: C. Elegans spatialized by LinLog.

As we argue in What do we see when we look at networks, the ambiguity of this middle space is a feature of the data. The absence of visual gap reflects the absence of a clear boundary in the topology. The community detection algorithms are tasked to find a boundary anyways, and react to the lack of natural gaps in the topology with a high variance in where they put the limit. From an interpretative standpoint, these boundaries do not deserve much trust, since they are poorly reproducible. Therefore, we argue, the layout is a better reduction of the topology. Indeed, it faithfully reflects the inherent ambiguity of clustering. This assumes that we know how to interpret the layout, of course. And it does not undermine the usefulness of clear-cut categories in various situations where ambiguity is a problem. Yet it is essential to realize that clusters do not exist as separated things, but as a continuous and ambiguous landscape of link density.

Now that we are equipped to understand clustering as, more generally, a matter of locality, we can start to find structures in hairballs. Although the lack of visual gaps prevents us from seeing clusters right away, we can rely instead on the subtleties of the contour and the denser zones on the borders. Hairballs may have non-obvious poles. As a test, we can run a modularity clustering a few times to check whether, and where, it is consistent. This approach reveals a community structure in some networks (figures 12 and 13) but not all (figure 14). Important note: in this visual experiment, I used the same settings for the community detection algorithm, and I used consistent colors. However, the number of clusters depends on the settings I used.

Figure 12. The hairball network from Venturini et al. (2018), spatialized by Force Atlas 2, with different renditions of a community detection algorithm. The same clusters appear consistently but their boundaries are not consistent.
Figure 13. The hairball network from Cardon et al. (2019), spatialized with the LinLog energy model, with different renditions of a community detection algorithm. The same clusters appear consistently but their boundaries are not consistent.
Figure 14. A random network (500 nodes, 5% chances of connection) spatialized by Force Atlas 2, with different renditions of a community detection algorithm. Found clusters do not map specific areas, and do not characterize the same nodes consistently.


  • We need visual gaps to see groups (Gestalt law of proximity).
  • Force-directed layouts do not care about visual gaps, and may compress a community structure into a hairball. Then we do not see the structure, even though it is there.
  • Force-driven layout creates localities, but it does not always make clear-cut clusters.
  • Localities may be more true to the data than clear-cut clusters.
  • Weakly connected clusters form poles.
  • Poles can be detected by looking at elongated contours and denser areas on the sides.


Bennett, C., Ryall, J., Spalteholz, L. and Gooch, A. (2007) ‘The aesthetics of graph visualization’, Proceedings of the 2007 Computational Aesthetics in Graphics, Visualization, and Imaging, pp. 57–64. doi: 10.2312/COMPAESTH/COMPAESTH07/057-064.

Bertin, J. (1967) Sémiologie Graphique. Les diagrammes, les réseaux, les cartes, Paris, La Haye, Mouton, Gauthier-Villars. 2e édition : 1973, 3e édition : 1999, EHESS, Paris.

Blondel, V. D., Guillaume, J.-L., Lambiotte, R. and Lefebvre, E. (2008) ‘Fast unfolding of communities in large networks’, Journal of Statistical Mechanics: Theory and Experiment, 2008(10). doi: 10.1088/1742-5468/2008/10/P10008.

Cardon, D., Cointet, J.P., Ooghe, B. and Plique, G. (2019) Unfolding the multi-layered structure of the French mediascape.

Correa, C. D. and Ma, K.-L. (2011) ‘Visualizing Social Networks’, in Social Network Data Analytics. Boston, MA: Springer US, pp. 307–326. doi: 10.1007/978-1-4419-8462-3_11.

Dianati, N. (2016) Unwinding the hairball graph: pruning algorithms for weighted complex networks. Physical Review E, 93(1), p.012304.

Edge, D., Larson, J., Mobius, M. and White, C. (2018) December. Trimming the hairball: Edge cutting strategies for making dense graphs usable. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 3951-3958). IEEE.

Kobourov, S. G., McHedlidze, T. and Vonessen, L. (2015) ‘Gestalt Principles in Graph Drawing’, in International Symposium on Graph Drawing and Network Visualization. Los Angeles: Springer, Cham, p. 13. doi: 10.1007/978-3-319-27261-0_50.

Lambiotte, R., Delvenne, J.C. and Barahona, M. (2008) Laplacian dynamics and multiscale modular structure in networks. arXiv preprint arXiv:0812.1770.

Noack, A. (2007) ‘Energy Models for Graph Clustering’, Journal of Graph Algorithms and Applications JGAA, 11(112), pp. 453–480.

Nocaj, A., Ortmann, M. and Brandes, U. (2015) ‘Untangling the Hairballs of Multi-Centered, Small-World Online Social Media Networks’, Journal of Graph Algorithms and Applications, 19(2), pp. 595–618. doi: 10.7155/jgaa.00370.

Van Den Elzen, S. and Van Wijk, J. J. (2014) ‘Multivariate network exploration and presentation: From detail to overview via selections and aggregations’, IEEE Transactions on Visualization and Computer Graphics, 20(12), pp. 2310–2319. doi: 10.1109/TVCG.2014.2346441.

Venturini, T., Jacomy, M., Bounegru, L. and Gray, J. (2018) ‘Visual Network Exploration for Data Journalists’, in Franklin, S. E. I. and B. (ed.) The Routledge Handbook to Developments in Digital Journalism Studies. Abingdon: Routledge.

Wagemans, J., Elder, J.H., Kubovy, M., Palmer, S.E., Peterson, M.A., Singh, M. and von der Heydt, R. (2012) A century of Gestalt psychology in visual perception: I. Perceptual grouping and figure–ground organization. Psychological bulletin, 138(6), p.1172.

Watts, D. and Strogatz, S. (1998) Collective dynamics of “smallworld” networks. Nature 393: 440–442.

Epistemic clashes in network science

5 min readcross-posted on Big Data & Society’s blog.

This post presents the paper Epistemic clashes in network science: Mapping the tensions between idiographic and nomothetic subcultures (45 min read).

My interest for networks was passed to me by one of my teachers, Franck Ghitalla, who had just read Albert-László Barabási’s best seller book Linked. Like many others, I was intrigued by the discovery of the scale-free network, a new and exotic structure that scientists started to find in every aspect of the world, from genetics to economy, from the power grid to terrorism, and to love. Or at least, that is what Barabási claimed.

The scale-free network is special because a few nodes get most of the links, while the rest is poorly connected. The number of links follows a power law, a distribution already known to be intriguingly pervasive in physics. From there, Barabási and other researchers went on a quest to theorize a universal law of complex networks, using the scale-free model as a foundation. But as the emergent field of network science consolidated, the apparent simplicity of the situation faded away. More accurate measurements challenged the pervasiveness of the power law. Models required more sophistication. Power laws were found in non-scale-free contexts. Scale-freeness became more difficult to assess in empirical situations. Yet the pervasiveness of more or less scale-free networks remained: Network scientists had found something, but what? The field adopted flexible umbrella terms such as “complex network” and “heavy-tailed distribution” to account for the diversity of empirical cases. Network science persisted as a field, but the prospect of theorizing a universal law had lost momentum. The scale-free model was productive despite constant criticism, but now, two decades later, some want to let it go to the benefit of a more experiment-driven approach. They meet a fierce resistance.

Epistemic clashes in network science narrates and reflects on the two main disputes on scale-freeness. The first dispute started in 2005 with a pre-print contesting the characterization of the power law, discussed in blog posts first, and later in academic publications. A compromise position gradually emerged, giving the impression that a consensus had formed. Network scientists believed that the problem originated in a disciplinary divide between statisticians and physicists, and that a better mutual understanding had solved it. However, a second dispute started again in 2018, once again as a pre-print contesting the pervasiveness of scale-free networks, discussed on social media then in academic publications. It came as a surprise to many network scientists, who considered the case closed.

This repeated failure of network scientists to agree on the facts established by their own field motivated me to analyze their exchanges as a controversy, drawing a methodological inspiration from Bruno Latour. My material consists of a set of 40 academic and non-academic publications that I selected, coded and analyzed. I synthetize, document and illustrate the dynamic of the controversy by focusing on a reduced set of actors and claims. I then propose my own interpretation about the persistence of a disagreement, arguing against the commonly accepted idea that it roots in a disciplinary divide.

Publications analyzed in the study, and their authors. 

To social science scholars, I offer a guided tour of network science, a field that is probably less well known than social network analysis. My account should make clear that the field is not as unified as it may seem from a distance. I put a particular effort into providing a nuanced perspective on the epistemological commitments of physicists such as Barabási. Their nomothetic approach to knowledge postulates the existence of universal laws of nature – finding them being the purpose of science. Evelyn Fox Keller criticized Barabási’s “faith” in “the traditional holy grail of universal ‘laws,’” but I argue that researcher’s beliefs are of no relevance here. The postulated existence of laws acts as an epistemic device, driving methods and shaping scientific claims; laws, here, are not a knowledge, but a way to know. Barabási’s papers, contrary to his general public books, do not claim the existence of universal laws. Their scientific validity does not depend on their existence, because to postulate is not to believe; that is why the approach is scientifically effective – robust. Social science scholars may find instructive to follow the resistance of network science’s nomothetic claims to their constant criticism. Although the nomothetic approach might be ultimately losing its dominance on the field.

To network scientists, beyond a recap of a controversy they already know about, I offer a better explanation of why the controversy reopened recently. The commonly accepted idea that it is rooted in a disciplinary divide is not satisfying. Indeed, bridging the disciplinary gap did not solve the problem; on the contrary, Aaron Clauset, a researcher who actively worked at it, co-authored the pre-print that reopened the controversy. I borrow Peter Galison’s concept of trading zones to develop a better diagnosis. I argue that since the inception of network science in the late nineties, theorists have been trading their models for the results of experimentalists. Theorists offered the scale-free model, postulating the pervasiveness of the power law; they needed experimental evidence of this pervasiveness. Experimentalists needed theoretical material to design their experiments; they employed the scale-free model and produced enough empirical evidence to ground the claim to pervasiveness of scale-free networks. This trade was beneficial to both sides, but it was not symmetrical. I argue that the controversy arose when experimentalists (e.g. Clauset) pushed for their own program to characterizing power laws independently of the scale-free model. This program is problematic because it breaks the exchange. Indeed, without the scale-free model, the experimental results do not benefit theorists such as Barabási; worse, they might challenge existing models and lead to new theories. I suggest that the situation is controversial because the balance of power is shifting in network science: from a dynamic where theory leads to experiments (a theory-driven program), the field is moving to experiments calling for new theories (experiment-driven program).

Read the paper online:

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It fucks the world to make techno-monsters

7 min read.

This is a “billet d’humeur.” An opinion piece, if you want, but this expression lacks the nuance of mood and humour that the French version suggests. I want to share my feelings. Because I am a little angry.

This morning, I was trying to compromise between two things. Write a passage on Donna Haraway’s notion of situated knowledges for my PhD dissertation (I’m late), or follow an online workshop, in a project I participate to, where Danish social science and humanities scholars are offered help in digital methods and data science. I decided to follow the workshop. It offered a promising talk by a Danish physicist who had studied social interactions by analyzing GPS data with network science techniques. It did not make me so happy.

I am not going to say more about that guy, because he is a great guy and my rant is not personal. And I must say, his work was really nice. But I have a problem with his narrative. He made a number of points that I did not expect from someone standing on the bridge between the natural and the social sciences. His words caught me by surprise. Here is a selection of his clichés, commonplace ideas from a natural scientist selling the big data rhetoric. I presume he was not realizing it. Note: I do not have a record, so all of my quotes are from memory. I would do better if I could, it would be worth it.

Self-evidence. This physicist shows a video of moving dots over the map of a building. We clearly see different rooms, and dots assembling in them over time. He says: “Those are real people, with feelings and goals, and we see them moving around, living their everyday life.” By saying so, he conflates the visualization with the phenomenon. Yet those dots are not actual people: those are, in this case, students in a rich Western country, and they do not represent all human beings. That kind of bias is a major concern in contemporary social science. Their phones are also not always on, and not always close to their body. Yes, we do see relevant patterns. But the picture is far from self-evident.

End of theory. He says: “The amazing thing with this technique is that you do not need any assumption, any theory. You get knowledge directly by analyzing the data, it does not come with the flaws of other social science methods.” This is so 2008. We have seen that this work, of course, comes with assumptions. More importantly, developing a statistical processing and a model, as he showcases, piles up even more assumptions. People do not equate their body. To meet is not to be close, and to be close is not to meet. etc. For instance, I was more with that physicist, listening to him live on Zoom, than with the co-worker in front of me, doing her own thing. And each of these piled-up assumptions comes with its own flaws. At the end of the day, the findings are narrow and their reach very limited. And if you want to generalize to “human beings,” then you have to face the same need for assumptions as anyone else (i.e. social scientists). That is not quite the end of theory.

Superiority of the natural sciences. He says: “I can help social scientists with more modern methods.” At this point I was starting to get angry. I don’t think I need to explain the problem, but my emotional reaction deserves a comment. I know such misconceptions are commonplace, and that it is a cultural issue. If all you have ever been doing is maths and physics, then assuming the existence of universal laws is a given, since it has always been unreasonably rewarding to you and your peers. There is genuinely no reason, for you, to suppose it works differently for the social world. Yet it does. You have a personal bias, and by definition you ignore it until you face a different reality. But I was disappointed to see these points from someone in close contact with the social sciences. I believe that if you just read seriously a few sociology papers, your figure it out by yourself. But there might be an explanation.

Two narratives

That physicist had not one, but two narratives. The first narrative is the pitch, the story intended to sell his work. The clichés come within that story. The second narrative is the work itself, e.g. the paper. On that level, there is no need for grand concepts such as the “human being.” It is about data points, statistical analysis, and modeling. The validity conditions are clear, and the claims narrow.

I suggest that these two narratives work like two legs. When one gets weakened, you move your weight onto the other one. This way you can argue both sides of the academic border. When criticized for the limited relevance of your findings, you can draw from the legendary reach of physics, and its quantitatively proven universal laws. You could not do that in a paper, because your results would not be universal at all. But you do not have to, as long as you can sell the dream informally, elsewhere. When criticized for not acknowledging social science’s norms (preexisting knowledge and specific epistemological commitments), you can let the quantitative nature of your papers speak for itself. Your method is legit and your findings robust, even if your paper’s way of warranting knowledge is different from a large body of sociological work, with which it is poorly compatible. The informal gospel provides the reach, while the paper’s narrative provides the scientific robustness. You cannot argue both at the same time, but defending them separately works well enough.

I observed something similar with Barabási, a famous scientist and pioneer of network science. What he writes in his general public books (e.g. Linked, The Formula) is different from his papers. He is criticized for his grand claims or (alleged) beliefs in the existence of universal laws of the social. He is explicit about this vision in his books. Yet such disputed claims are absent from his papers, and his work is much more solid than what his critiques seem to think. His papers have narrow findings with clear validity conditions. He might suggest interpretations, that is all. The inflated interpretation as findings of universal reach is made separately, outside of peer review, in other spaces. And to be fair, his vision has the benefit of giving a meaning to the otherwise dry findings of network science. His vision has to be challenged on a scientific ground, but regardless of the outcome, it has the benefit of existing.

Casually hiding methodological commitments

As researchers, beyond our cultural differences, we have a responsibility not to spread problematic ideas such as the self-evidence of visualization, or the absence of methodological commitments to data science. Visualization is never self-evident, because there is always a method. We must not hide it, because we must account for its flaws. Data is never raw, it is always obtained, and it comes with its own biases. We know that. We know that we know that. Yet we let ourselves go down this slippery slope in informal spaces. We must stop selling this rhetoric, especially when teaching. It is toxic, and unnecessary. It does not empower anyone worth empowering.

The funny and sad conclusion is that I went through this instead of writing on Donna Haraway’s situated knowledges. She was precisely making this point. At the time she wrote her famous paper, I was only 8 years old. I wish we had since learned her lesson. She calls this enactment of self-evidence and absence of epistemological commitments “the god trick of seeing everything from nowhere.” I leave you with her, a challenging but good company. Enjoy the verve.

The instruments of visualization in multinationalist, postmodernist culture have compounded these meanings of disembodiment. The visualizing technologies are without apparent limit. The eye of any ordinary primate like us can be endlessly enhanced by sonography systems, magnetic reasonance imaging, artificial intelligence-linked graphic manipulation systems, scanning electron microscopes, computed tomography scanners, color-enhancement techniques, satellite surveillance systems, home and office video display terminals, cameras for every purpose from filming the mucous membrane lining the gut cavity of a marine worm living in the vent gases on a fault between continental plates to mapping a planetary hemisphere elsewhere in the solar system. Vision in this technological feast becomes unregulated gluttony; all seems not just mythically about the god trick of seeing everything from nowhere, but to have put the myth into ordinary practice. And like the god trick, this eye fucks the world to make techno-monsters.


3 min read

Not all dreams are oniric. As I was half-awakening today, I saw myself in a work situation where I was helping a policy maker and his team design the information system they needed. Their initial inclination was to pile up features, mimicking what they thought other information systems looked like, and hoping that it would, by some magic, solve their problems. But they did not know how, and they had doubts about the whole endeavor; that is why I was advising them.

In my dream, I told them that they should focus on some key, quality content, and how to present it. My message was that even if you have a good database and a nice front-end, your system is only worth the data it contains, assuming that one can make sense of it. I encouraged them to stop thinking of technical features as a list, and instead pick a practical case as a benchmark. Assuming that you know the data and its interpretation already, does the system help you tell the right story? Storytelling is all about context. Your system is not just about data, but also practices around them, everything you need to interpret them. This thought exercise helps shifting the design discussion away from data-centric features to the profit of user-centric features. For instance, annotation, metadata, data care (cleaning, formatting) and quality control.

In my dream, the team was very receptive. They knew it already, and they needed a confirmation. Now their next step was clear: pick data on a topic they know well, process it through the current prototype, and check which features are useful to tell the right story. They were satisfied.

Then I added: storytelling is only half the picture. Because most of the time, your system will contain half-baked, dirty data that you don’t know how to interpret. While you focus on telling stories with good data, the system allows bad data to talk by themselves. It frames them a certain way, it changes how you think of them. If you do not pay attention, you will circulate these unintentional stories. So the other half of the picture is to ask yourself: what story am I telling with the system when I am not trying to tell a story?

I thought: this should be called storyletting. And I woke up. The brain is a mystery.


Storyletting is the act of publishing data or a data visualization while pretending that it tells no story, or framing it as something that is external to the story currently told. It is the act of letting certain inscriptions tell freely their own stories.

The concept assumes that all inscriptions tell stories: that is its purpose. It aims at making you aware of the narrative side effects of elements you do not see as parts of the story you tell. We know that visualizations can be manipulated. But we tend to overlook that the same effects can arise unintentionally, in our own practices, because we wrongly assume that certain inscriptions do not have their own voice. For instance the unprocessed (“raw” data, “source” code) or things we frame as side content (screenshot of a tool, appendix). It is good to be able to name their narrative effect.