Similarity breeds connection. This principle—the homophily principle—structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship. The result is that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics. Homophily limits people's social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Homophily in race and ethnicity creates the strongest divides in our personal environments, with age, religion, education, occupation, and gender following in roughly that order. Geographic propinquity, families, organizations, and isomorphic positions in social systems all create contexts in which homophilous relations form. Ties between nonsimilar individuals also dissolve at a higher rate, which sets the stage for the formation of niches (localize...

/pdf/birds-of-a-feather-homophily-in-social-networks-3kjmeyr23w.pdf

Birds of a Feather: Homophily in Social Networks

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

A population is “hidden” when no sampling frame exists and public acknowledgment of membership in the population is potentially threatening. Accessing such populations is difficult because standard probability sampling methods produce low response rates and responses that lack candor. Existing procedures for sampling these populations, including snowball and other chain-referral samples, the key-informant approach, and targeted sampling, introduce well-documented biases into their samples. This paper introduces a new variant of chain-referral sampling, respondent-driven sampling, that employs a dual system of structured incentives to overcome some of the deficiencies of such samples. A theoretic analysis, drawing on both Markov-chain theory and the theory of biased networks, shows that this procedure can reduce the biases generally associated with chain-referral methods. The analysis includes a proof showing that even though sampling begins with an arbitrarily chosen set of initial subjects, as do most chain-referral samples, the composition of the ultimate sample is wholly independent of those initial subjects. The analysis also includes a theoretic specification of the conditions under which the procedure yields unbiased samples. Empirical results, based on surveys of 277 active drug injectors in Connecticut, support these conclusions. Finally, the conclusion discusses how respondent- driven sampling can improve both network sampling and ethnographic 44 investigation.

Respondent-driven sampling : A new approach to the study of hidden populations

Social Network Analysis

https://toreopsahl.files.wordpress.com/2010/04/node_centrality_in_weighted_networks1.pdf

Node centrality in weighted networks: Generalizing degree and shortest paths.

Social and economic exchanges often occur between strangers who cannot rely on past behavior or the prospect of future interactions to establish mutual trust. Game theorists formalize this problem as a one-shot prisoner's dilemma and predict mutual noncooperation. Recent studies, however, challenge this conclusion. If the game provides an option to exit (or to refuse to play), strategies based on projection (of a player's intentions) and detection (of the intentions of a stranger) can confer a cooperator's advantage. Yet previous research has not found a way for these strategies to evolve from a random start or to recover from invasion by aggressive strategies that feign trustworthiness. The authors use computer simulation to show how trust and cooperation between strangers can evolve without formal or informal social controls. The outcome decisively depends, however, on two structural conditions : the payoff for refusing to play, and the embeddedness of interaction. Effective norms for trusting strangers emerge locally, in exchanges between neighbors, and then diffuse through weak ties to outsiders

The evolution of trust and cooperation between strangers: A computational model.

We evaluate four theories that predict the distribution of power in exchange networks. All four theories-core theory, equidependence theory, exchange-resistance theory, and expected value theory-assume actors rationally pursue self-interests. Three of the theories add social psychological assumptions that place the pursuit of self interest in an interactive context. Predictions of exchange earnings by the four theories are evaluated against data from eight experimental networks, including types of networks not previously studied. These networks vary conditions that affect the chances that a position can be excluded from exchange. We find that when the theories base predictions on a network position's structural potential for exclusion, exchange-resistance theory provides the bestfit, but when predictions are based on actual experiences of exclusion, expected value theory fits best. Our discussion focuses on the distinction between the a priori potential for exclusion versus experienced exclusion as factors in the genesis of power T he problem of power distribution in exchange networks has captured the attention of a variety of theorists. The appeal of the problem derives from the combination of the formal representation of social structure as network (Wellman and Berkowitz 1992) and sociology's perennial concern with power. A growing body of experimental studies now permits researchers to test various theoretical formulations. How does location in a network confer advantages on a person or a corporate body in their dealings with others? Consider the promotion prospects of two senior accountants, Andy and Bob. Because Andy's work involves accounts at various regional offices, his coworkers typically do not know each other. Bob, on the other hand, deals with corporate accounts, so his coworkers typically associate with each other. Thus, Andy and Bob are surrounded by two very different networks and it is not obvious that Andy's network favors him for promotion (Burt 1992). In a second ex

/pdf/exclusion-and-power-a-test-of-four-theories-of-power-in-11rctz6e5x.pdf

Exclusion and power: a test of four theories of power in exchange networks*

We describe and illustrate methodology for comparing networks from diverse settings. Our empirical base consists of 42 networks from four kinds of species (humans, nonhuman primates, nonprimate mammals, and birds) and covering distinct types of relations such as influence, grooming, and agonistic encounters. The general problem is to determine whether networks are similarly structured despite their surface differences. The methodology we propose is generally applicable to the characterization and comparison of network–level social structures across multiple settings, such as different organizations, communities, or social groups, and to the examination of sources of variability in network structure. We first fit a p* model (Wasserman and Pattison 1996) to each network to obtain estimates for effects of six structural properties on the probability of the graph. We then calculate predicted tie probabilities for each network, using both its own parameter estimates and the estimates from every other network in the collection. Comparison is based on the similarity between sets of predicted tie probabilities. We then use correspondence analysis to represent the similarities among all 42 networks and interpret the resulting configuration using information about the species and relations involved. Results show that similarities among the networks are due more to the kind of relation than to the kind of animal.

/pdf/comparing-networks-across-space-and-time-size-and-species-3720ms08uq.pdf

Comparing Networks Across Space and Time, Size and Species

We extend network exchange theory (Markovsky, Willer, and Patton 1988) to accommodate a new class of power phenomena. Previous theory and research have shown that structural configurations in some networks promote or inhibit exchange opportunities, leading to robust power and resource differentials. The extension identifies a structural basis for subtler forms of differentiation. Using computer simulations and laboratory experiments, we show that the degree to which this "weak power" is manifested in resource accumulations is conditioned by local and global network patterns, and by the experience and strategies of actors in the network. Experimental tests corroborate the predicted weak power effects and the consequences of variations in actors' negotiating experiences.

John Skvoretz

Papers

Node centrality in weighted networks: Generalizing degree and shortest paths.

The evolution of trust and cooperation between strangers: A computational model.

Exclusion and power: a test of four theories of power in exchange networks*

Comparing Networks Across Space and Time, Size and Species

The Seeds of Weak Power: an Extension of Network Exchange Theory