Social Network Analysis: Methods and Applications

By Stanley Wasserman, Katherine Faust. 1994

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Citations in the corpus

Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose (2013, methodology): “From network algorithmic (Wasserman and Faust 1994) perspective the question is more complicated.”
Clustering in weighted networks (2009, methodology): “…ties are weighted, researchers have typically introduced an arbitrary cut-off level of the weight, and then dichotomized the network by removing ties with weights that are below the cut-off, and then setting the weights of the remaining ties equal to one (Doreian, 1969; Wasserman and Faust, 1994).”
Nonparametric Latent Feature Models for Link Prediction (2009, background): “Statistical analysis of social networks and other relational data has been an active area of research for over seventy years and is becoming an increasingly important problem as the scope and availability of social network datasets increase [1].”
Mathematical models to characterize early epidemic growth: A Review (2016, background): “Clustering & spatial structure Empirical networks do not always satisfy the assumption of random or uncorrelated contacts made by the configuration model, as they are expected to have non-trivial structural properties including clustering and degree correlations [85].”
Inferring friendship network structure by using mobile phone data (2009, background): “As a result, social network analysis has generally been limited to examining small, well-bounded populations, involving a small number of snapshots of interaction patterns (1).”
The stability of centrality measures when networks are sampled (2003, background): “Several centrality measures have been created to measure which individuals in a network possess influential and prestigious roles (Freeman, 1979; Bonacich, 1972, 1987; Scott, 2000; Wasserman and Faust, 1994).”
Bibliometric Analysis using Bibliometrix an R Package (2018, background): “According to Stanley Wasser-mann and Katherine Faust, [16] the existence of relational information among network members is instrumental in defining and shaping of the network structure.”
Computational models of trust and reputation: agents, evolutionary games, and social networks (2002, methodology): “Sociologists have studied these systems using social networks for many decades (Wasserman and Faust, 1994).”
A Critical Review of Centrality Measures in Social Networks (2010, background): “The set VG represents the members of the social network, while the set EG refers to the relationships between them and thus describes social ties and interaction potentials between the actors (Sabidussi 1966; Wassermann and Faust 1994).”
Voice Flows to and around Leaders (2013, methodology): “Consonant with prior network research (e.g., Wasserman and Faust, 1994; Opsahl and Panzarasa, 2009), we first dichotomized the responses such that scores indicating any level of voice (seldom or more frequently) were coded as an existing voice tie, and ‘‘no’’/ ‘‘never’’ responses were coded as lack…”
Bilinear Mixed-Effects Models for Dyadic Data (2005, methodology): “Comparison of various statistics of interest to reference distributions has been a fundamental tool for inference and model evaluation in the social networks literature (Wasserman and Faust 1994, chaps.”
Consensus reaching in social network group decision making: Research paradigms and challenges (2018, background): “importance degree of this decision maker in the social network [71].”
The Negative Effects of Social Capital in Organizations: A Review and Extension (2017, unknown): “Structural SC that leads to cohesiveness and hinders the flow of information emanating from beyond the group (Wasserman and Faust 1994) and relational SC that engenders norms of obligation, commitment, and reciprocity (Nahapiet and Ghoshal 1998; Villena et al.”
Curricular Flows: Trajectories, Turning Points, and Assignment Criteria in High School Math Careers (2006, methodology): “…form trajectories), and course sequences should be identified as structurally equivalent sets of courses in these flows (courses with the same sets of studentflow patterns, such as stages in course sequences and broker-turning point types of courses; see McFarland 1999; Wasserman and Faust 1994).”
Information processing in complex networks: Graph entropy and information functionals (2008, methodology): “By a local property measure, we understand a graph measure that characterizes graph elements (e.g., vertices of a graph) regarding a local structural property, e.g., the centrality of a vertex [1,2,4,19,30,34].”