Birds of a Feather: Homophily in Social Networks
01 Aug 2001-Review of Sociology (Annual Reviews 4139 El Camino Way, P.O. Box 10139, Palo Alto, CA 94303-0139, USA)-Vol. 27, Iss: 1, pp 415-444
TL;DR: The homophily principle as mentioned in this paper states that similarity breeds connection, and that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics.
Abstract: 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...
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26 Apr 2010
TL;DR: In this paper, the authors have crawled the entire Twittersphere and found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks.
Abstract: Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing.We have crawled the entire Twitter site and obtained 41.7 million user profiles, 1.47 billion social relations, 4,262 trending topics, and 106 million tweets. In its follower-following topology analysis we have found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks [28]. In order to identify influentials on Twitter, we have ranked users by the number of followers and by PageRank and found two rankings to be similar. Ranking by retweets differs from the previous two rankings, indicating a gap in influence inferred from the number of followers and that from the popularity of one's tweets. We have analyzed the tweets of top trending topics and reported on their temporal behavior and user participation. We have classified the trending topics based on the active period and the tweets and show that the majority (over 85%) of topics are headline news or persistent news in nature. A closer look at retweets reveals that any retweeted tweet is to reach an average of 1,000 users no matter what the number of followers is of the original tweet. Once retweeted, a tweet gets retweeted almost instantly on next hops, signifying fast diffusion of information after the 1st retweet.To the best of our knowledge this work is the first quantitative study on the entire Twittersphere and information diffusion on it.
6,108 citations
TL;DR: Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties, which has implications for clinical and public health interventions.
Abstract: Background The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic. Methods We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The bodymass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors. Results Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], ≥30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person’s chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network. Conclusions Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions.
4,783 citations
TL;DR: The authors found that in ethnically diverse neighbourhoods residents of all races tend to "hunker down" and trust (even of one's own race) is lower, altruism and community cooperation rarer, friends fewer.
Abstract: Ethnic diversity is increasing in most advanced countries, driven mostly by sharp increases in immigration. In the long run immigration and diversity are likely to have important cultural, economic, fiscal, and developmental benefits. In the short run, however, immigration and ethnic diversity tend to reduce social solidarity and social capital. New evidence from the US suggests that in ethnically diverse neighbourhoods residents of all races tend to ‘hunker down’. Trust (even of one's own race) is lower, altruism and community cooperation rarer, friends fewer. In the long run, however, successful immigrant societies have overcome such fragmentation by creating new, cross-cutting forms of social solidarity and more encompassing identities. Illustrations of becoming comfortable with diversity are drawn from the US military, religious institutions, and earlier waves of American immigration.
3,466 citations
TL;DR: This article introduces four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.
Abstract: Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.
2,937 citations
Cites background or methods from "Birds of a Feather: Homophily in So..."
...Such interconnections occur naturally in data from a variety of applications such as bibliographic data [10, 16], email networks [7] and social networks [41]....
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...When we are adding a link, we choose the source node randomly but we choose the destination node using the dh parameter (which varies homophily [41] by specifying what percentage, on average, of a node’s neighbor is of the same type) as well as the degree of the candidates (preferential attachment [3])....
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...However, in many applications that produce data with correlations between labels of interconnected objects (a phenomenon sometimes referred to as relational autocorrelation [41]) labels of the objects in the neighborhood are often unknown as well....
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References
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"Birds of a Feather: Homophily in So..." refers background in this paper
...Evidence about Homophily: Salient Dimensions Lazarsfeld & Merton (1954) distinguished two types of homophily:status homophily, in which similarity is based on informal, formal, or ascribed status, and value homophily, which is based on values, attitudes, and beliefs....
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TL;DR: Ibarra et al. as mentioned in this paper found that men were more likely to form homophilous ties across multiple networks and to have stronger homophily ties, while women evidenced a differentiated network pattern in which they obtained social support and friendship from women and instrumental access through network ties to men.
Abstract: Herminia Ibarra Harvard University This paper argues that two network mechanisms operate to create and reinforce gender inequalities in the organizational distribution of power: sex differences in homophily (i.e., tendency to form same-sex network relationships) and in the ability to convert individual attributes and positional resources into network advantages. These arguments were tested in a network analytic study of men's and women's interaction patterns in an advertising firm. Men were more likely to form homophilous ties across multiple networks and to have stronger homophilous ties, while women evidenced a differentiated network pattern in which they obtained social support and friendship from women and instrumental access through network ties to men. Although centrality in organization-wide networks did not vary by sex once controls were instituted, relative to women, men appeared to reap greater network returns from similar individual and positional resources, as well as from homophilous relationships.'
1,978 citations
Additional excerpts
...Iannaccone (1988) reviewed literature differentiating churches and sects, indicating that sects (which tend to be more conservative, evangelical, and fundamentalist) are a more total social environment for their members, spawning a larger proportion of their friendships and social support networks…...
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