Posts tagged ‘networks’

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.

dylan

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.

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February 6, 2018 at 12:24 pm 1 comment

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

April 3, 2015 at 9:16 am

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.

April 2, 2013 at 7:53 am

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.

September 19, 2012 at 6:00 am

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.

December 22, 2011 at 11:07 am 16 comments

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.

(more…)

October 18, 2011 at 2:30 pm

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.

September 27, 2011 at 4:29 am 4 comments

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