Networks, Crowds and Markets: Reasoning about a Highly Connected World

By M. Aalabaf-Sabaghi. 2012

1,466 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

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Citations in the corpus

Graphite: Iterative Generative Modeling of Graphs (2018, background): “Graphs arise in a wide variety of domains in physical sciences, information sciences, and social sciences, and consequently algorithms for unsupervised representation learning of such data have several downstream applications, including node classification, link prediction, and community detection [”
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec (2017, background): “conventional paradigm of mining and learning with networks usually starts from the explicit exploration of their structural properties [12, 30].”
We know what @you #tag: does the dual role affect hashtag adoption? (2012, background): “The factors of joining communities are inspired from the literature of social network analysis, in which this is modeled as a cascading behavior [1, 10], or the adoption of innovation [32, 5].”
Bi-cross-validation for factor analysis (2015, background): “Strong factors resemble the giant components commonly found in networks [16].”
On the Behaviour of Deviant Communities in Online Social Networks (2016, positive): “…revealing an intertwinement between community boundaries and cascade reach that is particularly tight in communities built upon a common theme shared by all of their members (Easley and Kleinberg 2010; Romero, Tan, and Ugander 2013; Barbi-eri, Bonchi, and Manco 2013; Martin-Borregon et al. 2014).”
Sociology in the Era of Big Data: The Ascent of Forensic Social Science (2015, positive): “Network science is a field that has merged together the network analytic efforts of computer scientists, physical scientists, and methodologically oriented social scientists who study social networks (Brandes et al. 2013; Easley and Kleinberg 2010; Newman 2009).”
Melting Ice Caps and the Economic Impact of Opening the Northern Sea Route (2018, background): “…stage regression we include the variables also present in the gravity equation (except for tariffs) as well as lagged trade network embeddedness (Easley and Kleinberg, 2010; De Benedictis and Tajoli, 2011; Zhou, 2011) and a variable for the economic mass of the two trading partners together,…”
Graph-based term weighting for text categorization (2015, methodology): “, degree centrality, in-degree/out-degree centrality in directed networks, weighted degree in weighted graphs, clustering coefficient) [9], or more global information, in the sense that the importance of a node is based on the properties of the node globally in the graph (e.”
Social contagion theory: examining dynamic social networks and human behavior (2011, background): “Network statistics is a fast-growing field (for useful reviews of the topic, see: [29-36]), and it is clear that perfect methods, free of any limitations or assumptions, do not exist for every sort of question one might want to ask with observational (or even experimental) data.”
Identifying Intention Posts in Discussion Forums (2013, background): “Researchers have studied many problems in social media, e.g., sentiment analysis (Pang & Lee, 2008; Liu, 2010) and social network analysis (Easley & Kleinberg, 2010).”
A tutorial on modeling and analysis of dynamic social networks. Part I (2017, background): “The development of SNA has inspired many important concepts of modern network theory [13–15] such as e.g. cliques and communities, centrality measures, small-world network, graph’s density and clustering coeﬃcient.”
Random Graphs and Complex Networks (2016, background): “See also [104] for a more algorithmic perspective to networks.”
The Routledge handbook of English language teaching (2018, background): “The underlying principle was recognising that human beings act upon and interact with the world using cultural artefacts and means, chief among them language.”
Digital Business Strategy: Toward a Next Generation of Insights (2013, background): “The pace of network formation is rapidly accelerating in every firm, industry sector, and industry (e.g., Easley and Kleinberg 2010; Venkatraman and Lee 2004; Viswanathan 2005).”
The Anatomy of the Facebook Social Graph (2011, background): “Moreover, social networks have been observed to display a broad range of unifying structural properties, including homophily, clustering, the small-world eﬀect, heterogeneous distributions of friends, and community structure [5,6].”