Posts tagged ‘networks’
| 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:  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
| 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.
| 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.
| 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.
| 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.
| 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.
| 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:
- 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.
- 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.
- 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.