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49
Drug abuse and HIV prevention research: Expanding paradigms and network contributions to risk reduction
 Connections
, 1995
"... This paper identifies an important paradigm shift in social research on HIV transmission, drug abuse, and risk reduction research. The article describes the key research trends and the institutional support for social network analysis in the HIV and drug risk field for the past decade. Key hypothese ..."
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This paper identifies an important paradigm shift in social research on HIV transmission, drug abuse, and risk reduction research. The article describes the key research trends and the institutional support for social network analysis in the HIV and drug risk field for the past decade. Key hypotheses and recommended areas for future research are identified.
Quantitative methods for studying social context in multilevels and through interpersonal relations
 Review of Research in Education
, 1998
"... The online version of this article can be found at: Published on behalf of ..."
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The online version of this article can be found at: Published on behalf of
Spectral Ranking
, 2009
"... This note tries to attempt a sketch of the history of spectral ranking—a general umbrella name for techniques that apply the theory of linear maps (in particular, eigenvalues and eigenvectors) to matrices that do not represent geometric transformations, but rather some kind of relationship between e ..."
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This note tries to attempt a sketch of the history of spectral ranking—a general umbrella name for techniques that apply the theory of linear maps (in particular, eigenvalues and eigenvectors) to matrices that do not represent geometric transformations, but rather some kind of relationship between entities. Albeit recently made famous by the ample press coverage of Google’s PageRank algorithm, spectral ranking was devised more than fifty years ago, almost exactly in the same terms, and has been studied in psychology and social sciences. I will try to describe it in precise and modern mathematical terms, highlighting along the way the contributions given by previous scholars. Disclaimer This is is a work in progress with no claim of completeness. I have tried to collect evidence of spectral techniques in ranking from a number of sources, providing a unified mathematical framework that should make it possible to understand in a precise way the relationship between contributions. Reports of inaccuracies and missing references are more than welcome. 1
Centrality and AIDS
 Connections
, 1995
"... Techniques is a regular column devoted to techniques of data construction, management, interpretation and analysis. Contributions are appreciated. Centrality measures are commonly described as indices of prestige, prominence, importance, and power — the four Ps. However, this sort of interpretation ..."
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Techniques is a regular column devoted to techniques of data construction, management, interpretation and analysis. Contributions are appreciated. Centrality measures are commonly described as indices of prestige, prominence, importance, and power — the four Ps. However, this sort of interpretation seems inappropriate in the case of sexual networks. In this column, I consider the interpretation of centrality measures in sexual networks, and more generally in the context of any kind of network diffusion. For simplicity, I will assume that the data 1 consist of a discrete symmetric social relation
Visual Reasoning about Social Networks using Centrality Sensitivity
"... In this paper, we study the sensitivity of centrality metrics as a key metric of social networks to support visual reasoning. As centrality represents the prestige or importance of a node in a network, its sensitivity represents the importance of the relationship between this and all other nodes in ..."
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In this paper, we study the sensitivity of centrality metrics as a key metric of social networks to support visual reasoning. As centrality represents the prestige or importance of a node in a network, its sensitivity represents the importance of the relationship between this and all other nodes in the network. We have derived an analytical solution that extracts the sensitivity as the derivative of centrality with respect to degree for two centrality metrics based on feedback and random walks. We show that these sensitivities are good indicators of the distribution of centrality in the network, and how changes are expected to be propagated if we introduce changes to the network. These metrics also help us simplify a complex network in a way that retains the main structural properties and that results in trustworthy, readable diagrams. Sensitivity is also a key concept for uncertainty analysis of social networks, and we show how our approach may help analysts gain insight on the robustness of key network metrics. Through a number of examples, we illustrate the need for measuring sensitivity, and the impact it has on the visualization of and interaction with social and other scalefree networks.
The Influence Of Marketing Journals: A Citation Analysis Of The Discipline And Its SubAreas
 Tilburg University. Available
, 2000
"... An important characteristic of journals is how influential they are in the generation and dissemination of scholarly knowledge in a discipline. We report a citation analysis of 49 marketing and marketingrelated journals to assess their relative influence based on the index of structural influence p ..."
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An important characteristic of journals is how influential they are in the generation and dissemination of scholarly knowledge in a discipline. We report a citation analysis of 49 marketing and marketingrelated journals to assess their relative influence based on the index of structural influence proposed by Salancik (1986). We investigate the level and span of influence of the 49 journals, both in the marketing discipline as a whole and in five specific subareas of marketing. As expected, the Journal of Marketing emerges as the most influential journal in the discipline and as the journal with the broadest span of influence across all subareas of marketing. However, different journals are most influential in each of the subareas, and the Journal of Marketing is particularly influential among the applied marketing journals. We also find that the index of structural influence is significantly correlated with all other measures of influence but least so with the impact factors report...
PageRank: Functional Dependencies
"... PageRank is defined as the stationary state of a Markov chain. The chain is obtained by perturbing the transition matrix induced by a web graph with a damping factor α that spreads uniformly part of the rank. The choice of α is eminently empirical, and in most cases the original suggestion α = 0.85 ..."
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PageRank is defined as the stationary state of a Markov chain. The chain is obtained by perturbing the transition matrix induced by a web graph with a damping factor α that spreads uniformly part of the rank. The choice of α is eminently empirical, and in most cases the original suggestion α = 0.85 by Brin and Page is still used. In this paper, we give a mathematical analysis of PageRank when α changes. In particular, we show that, contrarily to popular belief, for realworld graphs values of α close to 1 do not give a more meaningful ranking. Then, we give closedform formulae for PageRank derivatives of any order, and by proving that the kth iteration of the Power Method gives exactly the value obtained by truncating the PageRank power series at degree k, we show how to obtain an approximation of the derivatives. Finally, we view PageRank as a linear operator acting on the preference vector and show a tight connection between iterated computation and derivation.
Network as a computer: ranking paths to find flows
, 802
"... Abstract. We explore a simple mathematical model of network computation, based on Markov chains. Similar models apply to a broad range of computational phenomena, arising in networks of computers, as well as in genetic, and neural nets, in social networks, and so on. The main problem of interaction ..."
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Abstract. We explore a simple mathematical model of network computation, based on Markov chains. Similar models apply to a broad range of computational phenomena, arising in networks of computers, as well as in genetic, and neural nets, in social networks, and so on. The main problem of interaction with such spontaneously evolving computational systems is that the data are not uniformly structured. An interesting approach is to try to extract the semantical content of the data from their distribution among the nodes. A concept is then identified by finding the community of nodes that share it. The task of data structuring is thus reduced to the task of finding the network communities, as groups of nodes that together perform some nonlocal data processing. Towards this goal, we extend the ranking methods from nodes to paths, which allows us to extract information about the likely flow biases from the available static information about the network. 1
Structure and dynamics of information in networks
 Lecture Notes
, 2011
"... The present notes are derived from a course taught at the University of Southern California. The focus of the course is on the mathematical and algorithmic theory underpinning the connections between networks and information. These connections take two predominant forms: • Network structure itself e ..."
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The present notes are derived from a course taught at the University of Southern California. The focus of the course is on the mathematical and algorithmic theory underpinning the connections between networks and information. These connections take two predominant forms: • Network structure itself encodes a lot of information. For instance, friendships between individuals let us draw inferences about shared interests or other attributes (location, gender, etc.). Similarly, hyperlinks between documents indicate similar topics, but can also be interested as endorsements, and thus hint at quality. The list of scenarios in which network structure helps us interpret information about individual nodes continues beyond this list, and will be explored in more detail throughout these notes. • Networks also play a crucial role in disseminating or gathering information. This applies both to social networks, in which communication between individuals happens naturally, and computer networks, which are designed explicitly to facilitate the exchange of information and distributed computations. We will draw analogies between the two types of networks, and investigate the mathematical underpinnings of the diffusion of information over networks in the later chapters of these notes. These notes are designed to accompany a onesemester graduatelevel course in computer science. We
ANALYSIS OF LAYERED SOCIAL NETWORKS
, 2006
"... contained in this dissertation are those of the author and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ..."
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contained in this dissertation are those of the author and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the