Networks Reading List

| Gabriel |

In response to my review of Ferguson’s Square and the Tower, several people have asked me what to read to get a good introduction to social networks. First of all, Part I of Ferguson’s book is actually pretty good. I meant it when I said in the review that it’s a pretty good intro to social networks and in my first draft I went through and enumerated all the concepts he covers besides betweenness and hierarchy being just a tree network. Here’s the list: degree, sociometry, citation networks, homophily, triadic closure, clustering coefficients, mean path length, small worlds, weak ties as bridges, structural holes, network externalities, social influence, opinion leadership, the Matthew Effect, scale free networks, random graph networks, and lattices. While I would also cover Bonacich centrality / dependence and alpha centrality / status, that’s a very good list of topics and Ferguson does it well. I listed all my issues with the book (basically 1) he’s not good on history/anthropology prior to the early modern era and 2) there’s a lot of conceptual slippage between civil society and social networks as a sort of complement (in the set theory sense) to the state and other hierarchies. However it’s a very well written book that covers a lot of history, including some great historical network studies and the theory section of the book is a good intro to SNA for the non-specialist.

Anyway, so what else would I recommend as the best things to get started with for understanding networks, especially for the non-sociologist.

Well obviously, I wrote the best short and fun introduction.

My analysis of combat events in the Iliad is how I teach undergraduates in economic sociology and they like it. (Gated Contexts version with great typesetting and art, ungated SocArxiv version with the raw data and code). This very short and informal paper introduces basic concepts like visualization and nodes vs edges as well as showing the difference between degree centrality (raw connections), betweenness centrality (connections that hold the whole system together), and alpha centrality (top of the pecking order).

Social networks is as much a method as it is a body of theory so it can be really helpful to play with some virtual tinker toys to get a tactile sense of how it works, speed it up, slow it down, etc. For this there’s nothing better than playing around in NetLogo. There’s a model library including several network models like “giant component” (Erdos-Renyi random graph), preferential attachment, “small world” (Watts and Strogatz ring lattice with random graph elements), and team assembly. Each model in the library has three tabs. The first shows a visualization that you can slow down or speed up and tweak in parameter space. This is an incredibly user-friendly and intuitive way to grok what parameters are doing and how the algorithm under each model thinks. A second tab provides a well-written summary of the model, along with citations to the primary literature. The third tab provides the raw code, which as you’d expect is a dialect of the Logo language that anyone born in the late 1970s learned in elementary school. I found this language immediately intuitive to read and it only took me two days to write useful code in it, but your mileage may vary. Serious work should probably be done in R (specifically igraph and statnet), but NetLogo is much better for conveying the intuition behind models.

Since this post was inspired by Square and the Tower and my main gripe about that is slippage between civil society and social networks, I should mention that the main way to take a social networks approach to civil society in the literature is to follow Putnam in distinguishing between bridging (links between groups) and bonding (links within groups) social capital. TL;DR is don’t ask the monkey’s paw for your society to have social capital without specifying that you want it to have both kinds.

If you want to get much beyond that, there are some books. For a long time Wasserman and Faust was canonical but it’s now pretty out of date. There are a few newer books that do a good job of it.

The main textbook these days is Matthew O. Jackson’s Social and Economic Networks. It’s kind of ironic that the main textbook is written by an economist, but if Saul of Tsarsus could write a plurality of the New Testament, then I guess an economist can write a canonical textbook on social network analysis. It covers a lot of topics, including very technical ones.

I am a big fan of The Oxford Handbook of Analytical Sociology. Analytical sociology isn’t quite the same thing as social networks or complex systems, but there’s a lot of overlap. Sections I (Foundations) and III (Social Dynamics) cover a lot in social networks and related topics like threshold models. (One of my pet peeves is assuming networks are the only kind of bottom-up social process so I like that OHoAS includes stuff on models with less restrictive assumptions about structure, which is not just a simplifying assumption but sometimes more accurate).

I’m a big fan of John Levi Martin’s Social Structures. The book divides fairly neatly into a first half that deals with somewhat old school social networks approaches to small group social networks (e.g., kinship moieties) and a second half that emphasizes how patronage is a scalable social structure that eventually gets you to the early modern state.

Aside from that, there’s just a whole lot of really interesting journal articles. Bearman, Moody, and Stovel 2004 maps the sexual network of high school students and discover an implicit taboo on dating your ex’s partner’s ex. Smith and Papachristos 2016 look at Al Capone’s network and show that you can’t conflate different types of ties, but neither can you ignore some types, only by taking seriously multiple types of ties as distinct can you understand Prohibition era organized crime. Hedström, Sandell, and Stern 2000 show that the Swedish social democratic party spread much faster than you’d expect because it didn’t just go from county to county, but jumped across the country with traveling activists, which is effectively an empirical demonstration of a theoretical model from Watts and Strogatz 1998.

Picking sides

| Gabriel |

Today the Economist posted a graph showing the patrons of factions in various civil wars in the Middle East. The point of the graph is that the alliances don’t neatly follow balance theory, since it is in fact sometimes the case that the friend of my enemy is my friend, which is a classic balance theory fail. As such, I thought it would be fun to run a Spinglass model on the graph. Note that I could only do edges, not arcs, so I only included positive ties, not hostility ties. One implication of this is ISIS drops out as it (currently) lacks state patronage.

Here’s the output. The second column is community and the third is betweenness.

> s
Graph community structure calculated with the spinglass algorithm
Number of communities: 4 
Modularity: 0.4936224 
Membership vector:
 [1] 4 4 3 2 2 2 4 3 4 3 1 4 1 3 3 4 2 4 2
> output
 b 
 [1,] "bahrain_etc" "4" "0" 
 [2,] "egypt_gov" "4" "9.16666666666667" 
 [3,] "egypt_mb" "3" "1.06666666666667" 
 [4,] "iran" "2" "47.5" 
 [5,] "iraq_gov" "2" "26" 
 [6,] "iraq_kurd" "2" "26" 
 [7,] "jordan" "4" "6.73333333333333" 
 [8,] "libya_dawn" "3" "1.06666666666667" 
 [9,] "libya_dignity" "4" "0.333333333333333"
[10,] "qatar" "3" "27.5333333333333" 
[11,] "russia" "1" "0" 
[12,] "saudi" "4" "4" 
[13,] "syria_gov" "1" "17" 
[14,] "syria_misc" "3" "31.0333333333333" 
[15,] "turkey" "3" "6.83333333333333" 
[16,] "uae" "4" "4" 
[17,] "usa" "2" "74.4" 
[18,] "yemen_gov" "4" "74.3333333333333" 
[19,] "yemen_houthi" "2" "0"   

So it looks like we’re in community 2, which is basically Iran and its clients, though in fairness we also have high betweenness as we connect community 2 (Greater Iran), community 3 (the pro Muslim Brotherhood Sunni states), and community 4 (the pro Egyptian government Sunni states). This is consistent with the “offshore balancing” model of Obama era MENA policy.

Here’s the code:

library("igraph")
setwd('~/Documents/codeandculture')
mena <- read.graph('mena.net',format="pajek")
la = layout.fruchterman.reingold(mena)
V(mena)$label <- V(mena)$id #attaches labels
plot.igraph(mena, layout=la, vertex.size=1, vertex.label.cex=0.5, vertex.label.color="darkred", vertex.label.font=2, vertex.color="white", vertex.frame.color="NA", edge.color="gray70", edge.arrow.size=0.5, margin=0)
s <- spinglass.community(mena)
b <- betweenness(mena, directed=FALSE)
output <- cbind(V(mena)$id,s$membership,b)
s
output

And here’s the data:

*Vertices 19
1 "bahrain_etc"
2 "egypt_gov"
3 "egypt_mb"
4 "iran"
5 "iraq_gov"
6 "iraq_kurd"
7 "jordan"
8 "libya_dawn"
9 "libya_dignity"
10 "qatar"
11 "russia"
12 "saudi"
13 "syria_gov"
14 "syria_misc"
15 "turkey"
16 "uae"
17 "usa"
18 "yemen_gov"
19 "yemen_houthi"
*Arcs
1 18
2 9
2 18
4 5
4 6
4 13
4 19
7 2
7 14
7 18
10 3
10 8
10 14
10 18
11 13
12 2
12 9
12 18
15 3
15 8
15 14
16 2
16 9
16 18
17 5
17 6
17 14
17 18

Choosing the null set

| Gabriel |

A few months ago I listened to an interview with a historian who had studied “bride shows” in Czarist Russia. If you’re familiar with the Book of Esther (or its holiday, Purim) you’re familiar with the idea — a monarch holds a beauty contest to find a wife. This seems like a fairly obvious thing to do, but if you’ve studied history (or watched Game of Thrones) you know that typically royalty marry in order to cement political alliances. So why would the czar (or the shah) choose a commoner to marry? The answer is not that the king is actually trying to find the biggest hottie in the kingdom, but a political logic, in that the monarch does not wish to form an alliance with any of the domestic or foreign noble houses. If you’re at the apex of a power structure, forming an edge mostly serves to bring the other party up to your level and this could undermine efforts to horde power for yourself (or more likely, for your clan or faction). This seems to be a common practice where the polity is relatively isolated from neighboring polities (e.g., Russia, Egypt, Hawaii) and so marriage would in effect involve elevating a client rather than allying with a rival. In such situations the strategic choice is to choose “none of the above.”

It seems like there are really three ways to go about this:

1. Do not form a tie at all. That is, celibacy. This was the strategy exercised by Queen Elizabeth I.

2. Loops. That is, royal incest. This was the strategy practiced by most Egyptian dynasties right through the Ptolemies.

3. Form a tie with a socially irrelevant person. Here we have the bride show strategy. You form a tie, but do so with someone of low enough status that obviously they’re not a player.

Note that some apparent instances of strategies 1 and 2 might actually be strategy 3. On page 95 of Social Structures, JLM describes how strategy 1 was actually strategy 3 in Renaissance Florence:

But given that Florentine sons had to marry up, those of the most distinguished lineages were hard pressed to marry— there was no one good enough for the sons of the elite to marry. In this case, there was no elegant structural solution, but rather a cheat: the elite, argue Padgett and Ansell, snuck away to other neighborhoods to find women as opposed to effectively announcing to their neighbors that there was a family of higher status than themselves.

Likewise, powerful “celibate” clergy from Alexander VI to Marcial Maciel have formed ties to socially irrelevant people but framed it as celibacy by having children with mistresses. I’m not aware of explicit references to this, but I like to imagine that some royal incest marriages were sexless and the heir was actually produced by a concubine, which would be socially irrelevant marriage framed as a loop. You can even find cases where celibacy is framed as a socially irrelevant marriage, as with women who are married off to a god or inanimate object.

Also note that sometimes “strategies” could be imposed on people, as with celibacy imposed on rival succession claimants (eg, the mythological Greek princess Danae and her Roman doublet Rhea Silvia or the dozen or so very historical deposed Byzantine emperors forced into monastic orders).

You also see this sort of thing in non-marital contexts. Most famously, during the principate senators resented the emperors because the emperors relied heavily on freedmen and knights to staff the Roman imperial bureaucracy, such relatively lowly people being less likely than senators to use such positions to build rival power bases (or to extract usurious rents). We see a similar practice more recently with the kings of Ethiopia, who for centuries would request a bishop be sent down from Alexandria, the purpose of which was not so much to cement ties to Egypt as to refrain from investing ecclesiastical power in any of the local notables, a foreigner bishop being the next best thing to no bishop at all, politically speaking.

Climbing the Charts, ch 4

| Gabriel |

A few months ago Stanford’s sociology department was nice enough to invite me up to give a talk on chapter four of Climbing the Charts. This chapter argues that the opinion leadership hypothesis cannot be supported in radio and in the talk I show a simulation of why we should be skeptical of this hypothesis in general. There’s no video, but here’s an enhanced audio file with slideshow. Also a separate PDF of the slides in case you have problems with the integrated version. (A caveat, I knew I was speaking to a technically sophisticated audience so I let the jargon flow freely, the chapter itself is much easier to follow for people without a networks background).

Also in shameless plugging news, Fabio’s review at OrgTheory.

Is Facebook “Naturally Occurring”?

| Gabriel |

Lewis, Gonzalez, and Kaufman have a forthcoming paper in PNAS on “Social selection and peer influence in an online social network.” The project uses Facebook data from the entire college experience of a single cohort of undergrads at one school in order to pick at the perennial homophily/influence question. (Also see earlier papers from this project).

Overall it’s an excellent study. The data collection and modeling efforts are extremely impressive. Moreover I’m very sympathetic to (and plan to regularly cite) the conclusion that contagious diffusion is over-rated and we need to consider the micro-motives and mechanisms underlying contagion. I especially liked how they synthesize the Bourdieu tradition with diffusion to argue that diffusion is most likely for taste markers that are distinctive in both sense of the term. As is often the case with PNAS or Science, the really good stuff is in the appendix and in this case it gets downright comical as they apply some very heavy analytical firepower to trying to understand why hipsters are such pretentious assholes before giving up and delegating the issue to ethnography.

The thing that really got me thinking though was a claim they make in the methods section:

Because data on Facebook are naturally occurring, we avoided interviewer effects, recall limitations, and other sources of measurement error endemic to survey-based network research

That is, the authors are reifying Facebook as “natural.” If all they mean is that they’re taking a fly on the wall observational approach, without even the intervention of survey interviews, then yes, this is naturally occurring data. However I don’t think that observational necessarily means natural. If researchers themselves imposed reciprocity, used a triadic closure algorithm to prime recall, and discouraged the deletion of old ties; we’d recognize this as a measurement issue. It’s debatable whether it’s any more natural if Mark Zuckerberg is the one making these operational measurement decisions instead of Kevin Lewis.

Another way to put this is to ask where does social reality end and observation of it begin? In asking the question I’m not saying that there’s a clean answer. On one end of the spectrum we might have your basic random-digit dialing opinion survey that asks people to answer ambiguously-worded Likert-scale questions about issues they don’t otherwise think about. On the other end of the spectrum we might have well-executed ethnography. Sure, scraping Facebook isn’t as unnatural as the survey but neither is it as natural as the ethnography. Of course, as the information regimes literature suggests to us, you can’t really say that polls aren’t natural either insofar as their unnatural results leak out of the ivory tower and become a part of society themselves. (This is most obviously true for things like the unemployment rate and presidential approval ratings).

At a certain point something goes from figure to ground and it becomes practical, and perhaps even ontologically valid, to treat it as natural. You can make a very good argument that market exchange is a social construction that was either entirely unknown or only marginally important for most of human history. However at the present the market so thoroughly structures and saturates our lives that it’s practical to more or less take it for granted when understanding modern societies and only invoke the market’s contingent nature as a scope condition to avoid excessive generalization of economics beyond modern life and into the past, across cultures, and the deep grammar of human nature.

We are, God help us, rapidly approaching a situation where online social networks structure and constitute interaction. Once we do, the biases built into these systems are no longer measurement issues but will be constitutive of social structure. During the transitional period we find ourselves in though, let’s recognize that these networks are human artifices that are in the process of being incorporated into social life. We need a middle ground between “worthless” and “natural” for understanding social media data.

Chain of Litigation

| Gabriel |

I was intrigued by the FT infographic on cell phone patent suits and decided to reformat it with F-R layout to get a big picture. A few things leap out. First, there is some pretty serious reciprocity (aka, counter-suits) going on, especially with Apple. On the other hand, Microsoft seems to be pretty good at attacking people who aren’t in a position to fight back. (*cough*trolls*cough*). Second, Google is at the periphery of the network which is pretty strange since many of these suits are actually about Android. This highlights the litigation strategy of picking off the weak members of the Android herd rather than taking the fight directly to Google itself. Furthermore, it suggests a data issue that there are omitted ties from the network, specifically positive ties between firms in the form of alliances (especially the Open Handset Alliance), and that reading the graph without these positive ties is misleading.

Anyway, here’s the graph. Click on it for a scalable PDF. Click here for the data in “.net” format. Code to produce the graph is below the fold.

The Passively Monitored Self and the Death of a “Backstage”

| Gabriel |

Practical advice will follow, but first a rant.

I have previously complained about “social” features that automate how you share information, especially when such features are opt-out rather than opt-in. For instance, I was not enthusiastic about Skype “mood messages” giving your friends and colleagues a play-by-play of what music you listen to, nor was I enamored of a product that would share your browser history.

It’s not as if I’m an introverted recluse either. I have a blog and I correspond pretty actively by e-mail, but the difference is that in these media I actively and deliberately control the flow of information rather than having the prestigious, shameful, and indifferent aspects of my personality and behavior all indiscriminately broadcast to my alters.

I have a fantasy in which Mark Zuckerberg is weeping in his garden when he overhears some neighbor children saying “take and read.” He looks up and notices an old copy of The Presentation of Self in Everyday Life sitting on the table. Tolle lege Mr. Zuckerberg, tolle lege.

Barring such an epiphany, I wouldn’t be surprised if next year’s Facebook Developer’s Conference includes announcements that American Standard is going social to automatically let your friends know when you use the toilet. Or perhaps Vivid will automatically tell all your second cousins and old friends from high school what pornography you’ve purchased. Or Gap brands could let all your friends know what size pants you wear. Visa could post a status update giving the vendor, address, and dollar value every time you buy anything. Because, really, everything’s better when it’s social regardless of whether it’s humiliating or just pointless information overload. It’s a brave new world of web 2.0 social media integration!

Anyway, I was most recently aggravated by Spotify which (like most things nowadays) defaults to over-sharing. Spotify describes this to NPR as “Freeing people from the hassle of actively sharing songs they like [which] will help keep people engaged in their friends’ listening habits without effort.” Some of us prefer to have this “hassle” because the alternative is an uncensored view of our listening habits. As I wrote when Apple added its “Ping” social feature to iTunes:

As a cultural sociologist who has published research on music as cultural capital, I understand how my successful presentation of self depends on me making y’all believe that I only listen to George Gershwin, John Adams, Hank Williams, the Raveonettes, and Sleater-Kinney, as compared to what I actually listen to 90% of the time, which is none of your fucking business.

Anyway, the worst thing about Spotify freeing you from privacyhassle is it does so by default and it’s difficult to opt-out. You can edit your profile to suppress playlists, but by default they are all revealed and even if you suppress them, new ones created thereafter are revealed. Worse, editing your profile provides no way to suppress “Top Tracks” and “Top Artists” (at least in the Mac client version 0.6.1). After a fair amount of searching (and coming very close to deleting my account entirely), I discovered that it’s fairly easy to totally suppress all of this through the client’s preferences. Just go to the “Spotify” menu and choose “Preferences . . .” then scroll down and uncheck these boxes:

You may now return to the dignity of crafting a public personae that is only loosely coupled to your backstage behavior. Enjoy.

This Malawian Life

| Gabriel |

Just a quick tip to check out the current episode of This American Life, which is based on the work of my CCPR colleague Susan Watkin on HIV-related gossip in Malawi. Even if you’re not interested in health or development, it’s very interesting for what it says about social networks, diffusion, statistical discrimination, and concealed stigma. The main issue is that people constantly talk about HIV in attempts to figure out who has HIV and thus makes an undesirable sex partner but I also had a few somewhat idiosyncratic interests:

1. Information does not just diffuse through social networks in the usual sense of things that would show up in your edge list or sociomatrix but also through space (I’m at the clinic next door to the HIV clinic when you pick up your meds) and through ad hoc collections of people temporarily bounded together (a bunch of people on a bus all start speculating about the HIV status of a pedestrian). I consider this more evidence for my belief that network contagion as a mechanism for information flow is over-rated.
2. A lot of public health programs emphasize the coals to Newcastle policy of “encouraging discussion” and “raising awareness.” These policies were driven by cosmopolitan elites, international NGOs, etc. That is, it’s John Meyer “world society” kind of stuff run amuck.
3. About a year ago our mutual grad student, Tom Hannan, started a new project that synthesizes Susan’s concerns in #2 with some of my recent theoretical/methodological interests.

Social Structures

| Gabriel |

Shortly before ASA, I finished John Levi Martin’s Social Structures and I loved it, loved it, loved it. (Also see thoughts from Paul DiMaggio, Omar Lizardo, Neil Gross, Fabio Rojas, and Science). I find myself hoping I have to prep contemporary theory just so I can inflict it on unsuspecting undergrads. The book is all about emergence and how fairly minor changes in the nature of social mechanisms can create quite different macro social structures.* It’s just crying out for someone to write a companion suite in NetLogo, chapter by chapter. In addition, JLM knows an enormous amount of history, anthropology, and even animal behavior and uses it all very well to both illustrate his points and show how they work when the friction of reality enters. For instance, he notes that balance theory breaks down to the extent that people have some agency in defining the nature of ties and/or keeping some relations “neutral” rather than the ally versus enemy dichotomy.**

An interesting contrast is Francis Fukuyama’s Origins of Political Order, which I also liked. The two books are broadly similar in scope, giving a sweeping comparative overview of history that starts with animals and attempts to work up to the early modern era. (There are also some similarities in detail, such as their very similar understandings of the “big man” system and that domination is more likely in bounded populations). There is an obvious difference of style in that Fukuyama is easier to read and goes into more extended historical discussions but the more important differences are thematic and theoretical. One such difference is that Fukuyama follows Polybius in seeing the three major socio-political classes as the people, the aristocracy, and the monarch, with the people and the monarch often combining against the aristocracy (as seen in the Roman Revolution and in early modern absolute monarchies). In contrast, JLM’s model tends to see the monarch as just the top aristocrat, though his emphasis on the development of transitivity in command effectively accomplishes some of the same work as the Fukuyama/Polybius model.

The most important difference comes in that  Fukuyama is inspired by Weber whereas JLM uses Simmel, a distinction that becomes especially distinct as they move from small tribal bands to early modern societies. Fukuyama’s book is fundamentally about the tension between kinship and law as the fundamental organizing principle of society. In Fukuyama’s account both have very old roots and modernity represents the triumph of law. In contrast, JLM sees kinship (and analogous structures like patronage) as the fundamental logics of society with modernity being similar in kind but grander in scale. In the last chapter and a half JLM discusses the early modern era and here he sounds a bit more like Fukuyama, but he’s clearly more interested in, for instance, the origins of political parties than in their transformation into modern ideological actors.

In part this is because, as Duncan Watts observed at the “author meets critics” at ASA, JLM is mostly interested in that which can be derived from micro-macro emergence and tends to downplay issues that do not fit into this framework.*** This is seen most clearly in the fact that the book winds down around the year 1800 after noting that (a) institutionalization can partially decouple mature structures from their micro origins and (b) ideology can in effect form a sort of bipartite network structure through which otherwise disconnected factions and patronage structures can be united (usually in order to provide a heuristic through which elites can practice balance theory), as with the formation of America’s original party system of Federalists and Democrats which JLM discusses in detail. Of course as I said in the “critics” Q&A, at the present most politically active Americans have a primarily ideological attachment to their party without things like ward bosses and perhaps more interestingly, a role for ideology as a bridge is not an issue restricted to the transition from early modern to modern. As is known to any reader of Gibbon, there was a similar pattern in late antiquity in how esoteric theological disputes over adoptionist Christology and reconciliation of sinners provided rallying points for core vs periphery political struggles in the late Roman empire. Since this is largely a dispute over emphasis, it’s not surprising that JLM was sympathetic to this but he noted that there are limits to what ideological affinity can accomplish and when it comes to costly action you really need micro structures. (He is of course entirely right about this as seen most clearly in the military importance of unit cohesion, but it’s still interesting that ideology has waxed and patronage waned in party systems of advanced democracies).

There are a few places in the book where JLM seemed to be arguing from end states back to micro-mechanisms and I couldn’t tell whether he meant that the micro-mechanisms necessarily exist (i.e., functionalism) or that such demanding specifications of micro-mechanisms implied that the end state was inherently unstable (i.e., emergence). For instance, in chapter three he discusses exchange of women between patrilineal lineages and notes that if there is not simple reciprocity (usually through cross-cousin marriage) then there must be either be some form of generalized reciprocity or else the bottom-ranked male lineages will go extinct. On reading this I was reminded of this classic exchange:

That is, I think it is entirely possible that powerful male lineages could have asymmetric marital exchange with less powerful male lineages and if the latter are eventually driven into extinction then that sucks for them. (The reason this wouldn’t lead to just a single male lineage clan is because, as Fukuyama notes, large clans can fissure and tracing descent back past the 5th or 6th generation is usually more political than genealogical). This is the sort of thing that can actually be answered empirically by contrasting Y chromosomes with mitochondrial DNA. For instance, a recent much publicized study showed that pretty much all ethnically English men carry the Germanic “Frisian Y” chromosome. The authors’ interpretation of this is that a Saxon mass migration displaced the indigenous Gallo-Roman population but I don’t see how this is at all inconsistent with the older elite transfer model of the Saxon invasion if we assume that the transplanted foreign elite hoarded women, including indigenous women. A testable implication of the elite transfer model is that the English would have the same Y as the Danes and Germans but similar mitochondria as the Irish and Welsh. Similarly, a 2003 study showed that 8% of men in East and Central Asia show descent on the male line from Ghengis Khan but nobody has suggested that this reflects a mass migration. Rather in the 12th and 13th centuries the Mongols used rape and polygamy to impregnate women of many Asian nations and they didn’t really give a damn if this meant extinction of the indigenous male lineages.

A very minor point but one that is important to me as a diffusion guy is that chapter five uses the technical jargon of diffusion in non-standard ways, or to be more neutral about it, he and I use terms differently. That said it’s a good chapter, it just needs to be read carefully to avoid semantic confusion.

This post may read like I’m critical of the book but that’s only because I prefer to react to and puzzle out the book rather than summarize it. What reservations I have are fairly minor and unconfident. My overall assessment is that this is a tremendously important book that should be read carefully by anyone interested in social networks, political sociology, social psychology, or economic sociology. For instance, I wish it had been published before my paper with Esparza and Bonacich as using the chapter on pecking orders would have allowed us to develop more depth to the finding about credit ranking networks. (That and it would have given us a pretext to compare Hollywood celebrities to poultry and small children). Despite the book’s foundation in graph theory, this interest should span qualitative/quantitative — at ASA Randy Collins praised the book enthusiastically and gave a very thoughtful reading and from personal conversation I know that Alice Goffman was also very impressed. I think this is because JLM’s relentless focus on interaction between people is a much thinner but nonetheless similar approach to the kinds of issues that qualitative researchers tend to engage with. Indeed, at a deep level Social Structures has more in common with ethnography than with anything that uses regression to try to describe society as a series of slope-intercept equations.

————-

* Technically, it’s about weak emergence, not strong emergence. At “author meets critics” JLM was very clear that he rejects the idea of sui generis social facts with an independent ontological status rather than just a summary or aggregation of micro structure.

** One of the small delights in the early parts of the book is that he notes how our understanding of network structure is driven in part by the ways we measure and record it. So networks based on observation of proximity are necessarily symmetric whereas networks based on sociometric surveys highlight the contingent nature of reciprocity, networks based on balance theory tend to be positive/negative whereas matrices emphasize presence/absence and are often sparse, etc. I might add to his observations in this line that the extremely common practice of projecting bipartite networks into unipartite space (as with studies of Hollywood, Broadway, corporate boards, and technical consortia) has its own sets of biases, most obviously exaggerating the importance and scalability of cliques. Also, I’ve previously remarked on a similar issue in Saller’s Personal Patronage as to how we need to be careful about directed ties being euphemistically  described as symmetric ties in some of our data.

*** Watts also observed that JLM’s approach is very much a sort of 1960s sociometry and doesn’t use the recent advances in social network analysis driven by the availability of big data about computer-mediated communication (such as Watts’ current work on Twitter). JLM responded with what was essentially a performativity critique of naive reliance on web 2.0 data, noting for instance that Facebook encourages triadic closure, enforces reciprocity, and discourages deletion of old ties.

TV Party Tonight!

| Gabriel |

A month or so ago bloggingheads had Alyssa Rosenberg and Peter Suderman (mp3 only), my two favorite politically-informed-but-not-hacks culture bloggers. In the course of their conversation they talked about “recapping” culture, which is where a blogger reacts in about 1000 words to each episode of a tv show, usually the day after it airs. I’m sure there were earlier precedents on Usenet forums, but I associate the development of this genre of criticism with Television Without Pity. TWOP recaps are almost Talmudic exegesis that take as long to read as the show itself takes to watch. There are currently many other recaps sites, most notably The Onion’s tv club, and other bloggers do just one or two shows, as Alyssa is currently doing with Breaking Bad and True Blood. It’s a very interesting genre of writing and helps illuminate some theoretical issues with the superstar effect and the demand structure for entertainment.

The superstar effect is of course Sherwin Rosen’s observation that cultural products and cultural workers have a truly ridiculous level of inequality. Rosen first noted that a scope condition is technology for infinite reproducibility and this has held up. However his theoretical mechanism was ordinal selection that was hyper-sensitive to infinitesimmal quality differences and later research has pretty definitively discarded that mechanism. Rather, most everybody now agrees that the superstar effect reflects some kind of cumulative advantage mechanism and the only question is exactly how it works. We know for a fact from Salganik’s music lab work that information cascades are a part of this, but that doesn’t mean that there aren’t also other cumulative advantage mechanisms at work.

Probably the first article to propose a cumulative advantage mechanism for the superstar effect was Moshe Adler’s “Stardom and Talent.” Adler is often cited as synthesizing network externalities and the superstar effect, that is, people read him as articulating a model of “watercooler entertainment” where entertainment is a mixed coordination game (aka, “battle of the sexes“) consumed mostly or entirely for its utility in providing topics of conversation. When you see people citing Adler they are usually arguing that cultural consumption is a means to an end of socializing. For example, imagine that (like any sane human being) you find watching golf on tv to be incredibly tedious but you force yourself to watch it so that you have something to chit chat about with your boss, who is a big golfer.

This is a compelling model, but it’s not actually the model Adler proposed, in part because he’s coming from a theoretical background that emphasizes demand (i.e., micro-economics) rather than a tradition that emphasizes homophily (i.e., sociology). What Adler actually wrote is that chit chat is a means to cultivating taste in entertainment addiction goods. Adler starts from the premise that many art forms function as addiction goods (aka, acquired tastes). However it is often difficult to consume enough of the art to get you into a place where the addiction good has positive expected value and so we use discourse about the art in order to heighten the addiction and thereby increase the utility of arts consumption. That is, I discuss a tv show with you because it helps me develop my relationship with the tv show, not because it helps me develop my relationship with you. We can see this in a formal setting when people take “[wine / opera / painting] appreciation” classes, where (in price theory terms) the class increases your addiction to the good even more so than simply consuming the good.

Adler’s model seems a bit on the aspy side and, like I said, people often get it backwards when they cite it, perhaps because they are forgetting how weird it is and one’s memory’s reconstructs the article’s argument to be more intuitive. Nonetheless, I think that Adler’s original model is also pretty compelling. Notably, there’s no reason why the causation has to go one way. It could be endogenous or it might even be contingent, with “watercooler” for some types of art and “addiction good” for others.

These are subtly different models and provide theoretical implications that are in theory distinguishable (though may be hard to disentangle in practice). In particular, I’m thinking that we can use Omar Lizardo’s argument about the different types of network ties supported by high culture versus pop culture. Omar argues that since pop culture forms a more universal social lubricant it should be (and in fact is) associated more with weak ties whereas high culture is tricky enough that it requires more strong ties.

If we extrapolate this out, we can interpret it as meaning that the “watercooler” network externality effect (ie, the common misreading of Adler) is a mechanism that supports cumulative advantage for shows that are very accessible and not terribly nuanced. That is, you might watch American Idol in order to have a bunch of 2 minute long conversations with acquaintances and strangers whom you normally come into contact with anyway. An important corollary is that you wouldn’t normally seek out fellow fans of crap but just make sure that you’re sufficiently familiar with crap to hold your own in a conversation with random people.

In contrast, we can use the “addiction goods” model (ie, Adler’s actual argument) to explain consumption of less accessible cultural objects of the sort that might sustain an entire dinner’s worth of conversation. The objects might even be so inscrutable that they are difficult to consume without having an interlocutor to help you make sense of them and so you might either seek out strangers who already consume the object or try to convince a close friend to consume the object as well so you can discuss it together. For instance if you read the first paragraph of this post and said “I don’t know or care about this Alyssa person but I’m going to click the link because I’m hoping somebody can help me understand what’s the deal with Hank’s mineral collection” then that would be an illustration of the addiction good model at work. Now if it’s just people who already consume a show finding each other that’s not cumulative advantage but homophily. However there is cumulative advantage if you start watching a show because your favorite blogger is recapping it or if you read a book to participate in a book club or if you buy your best friend a box set of the first season of Battlestar Galactica so you have someone with whom to discuss the downward spiral of Gaius Baltar. In this sense recapping is a complement to the increasing narrative complexity of popular entertainment and one way to see this is that people tend to recap shows with a serial rather than episodic structure.