CiteULike: bigbossman's network

Generalized h-index for Disclosing Latent Facts in Citation Networks

Antonis Sidiropoulos — 2006-09-16T10:33:02-00:00

(13 Jul 2006)

What is the value of a scientist and its impact upon the scientific thinking? How can we measure the prestige of a journal or of a conference? The evaluation of the scientific work of a scientist and the estimation of the quality of a journal or conference has long attracted significant interest, due to the benefits from obtaining an unbiased and fair criterion. Although it appears to be simple, defining a quality metric is not an easy task. To overcome the disadvantages of the present metrics used for ranking scientists and journals, J.E. Hirsch proposed a pioneering metric, the now famous h-index. In this article, we demonstrate several inefficiencies of this index and develop a pair of generalizations and effective variants of it to deal with scientist ranking and with publication forum ranking. The new citation indices are able to disclose trendsetters in scientific research, as well as researchers that constantly shape their field with their influential work, no matter how old they are. We exhibit the effectiveness and the benefits of the new indices to unfold the full potential of the h-index, with extensive experimental results obtained from DBLP, a widely known on-line digital library.

Visual analysis of dynamic group membership in temporal social networks

Hyunmo Kang — 2008-02-25T22:23:28-00:00

SIGKDD Explor. Newsl., Vol. 9, No. 2. (December 2007), pp. 13-21.

The Spread of Obesity in a Large Social Network over 32 Years.

Nicholas A Christakis — 2007-07-27T13:28:27-00:00

N Engl J Med, Vol. 357, No. 4. (26 July 2007), pp. 370-379.

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 body-mass 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. Copyright 2007 Massachusetts Medical Society.

Marvel Universe looks almost like a real social network

R Alberich — 2008-01-28T21:20:19-00:00

(11 Feb 2002)

We investigate the structure of the Marvel Universe collaboration network, where two Marvel characters are considered linked if they jointly appear in the same Marvel comic book. We show that this network is clearly not a random network, and that it has most, but not all, characteristics of "real-life" collaboration networks, such as movie actors or scientific collaboration networks. The study of this artificial universe that tries to look like a real one, helps to understand that there are underlying principles that make real-life networks have definite characteristics.

Community Structure in the United States House of Representatives

Mason Porter — 2006-10-21T03:59:31-00:00

(4 Feb 2006)

We investigate the networks of committee and subcommittee assignments in the United States House of Representatives from the 101st--108th Congresses, with committees connected according to “interlocks” or common membership. We examine the House's community structure using several methods, which reveal strong links between different committees as well as the intrinsic hierarchical structure within the House as a whole. We identify structural changes, including additional hierarchical levels and higher modularity, resulting from the 1994 elections, in which the Republican party earned majority status in the House for the first time in more than forty years. We also combine our network approach with analysis of roll call votes using singular value decomposition to uncover correlations between the political and organizational structure of House committees.

Dynamic Exploration of Networks: from general principles to the traceroute process

Luca Dall'asta — 2007-06-28T12:00:33-00:00

(26 Jun 2007)

Dynamical processes taking place on real networks define on them evolving subnetworks whose topology is not necessarily the same of the underlying one. We investigate the problem of determining the emerging degree distribution, focusing on a class of tree-like processes, such as those used to explore the Internet's topology. A general theory based on mean-field arguments is proposed, both for single-source and multiple-source cases, and applied to the specific example of the traceroute exploration of networks. Our results provide a qualitative improvement in the understanding of dynamical sampling and of the interplay between dynamics and topology in large networks like the Internet.

Hierarchical Organization of Modularity in Metabolic Networks

E Ravasz — 2005-08-24T02:24:50-00:00

Science, Vol. 297, No. 5586. (30 August 2002), pp. 1551-1555.

Social Network Analysis : Methods and Applications (Structural Analysis in the Social Sciences)

Stanley Wasserman — 2005-04-21T01:34:49-00:00

(25 November 1994)

Efficient Detection of Network Motifs

Sebastian Wernicke — 2007-01-05T02:31:40-00:00

IEEE/ACM Trans. Comput. Biol. Bioinformatics, Vol. 3, No. 4. (October 2006), pp. 347-359.

Using structure indices for efficient approximation of network properties

Matthew Rattigan — 2006-11-30T23:24:24-00:00

(2006), pp. 357-366.

Scaling and percolation in the small-world network model

MEJ Newman — 2006-11-02T08:48:38-00:00

(6 May 1999)

In this paper we study the small-world network model of Watts and Strogatz, which mimics some aspects of the structure of networks of social interactions. We argue that there is one non-trivial length-scale in the model, analogous to the correlation length in other systems, which is well-defined in the limit of infinite system size and which diverges continuously as the randomness in the network tends to zero, giving a normal critical point in this limit. This length-scale governs the cross-over from large- to small-world behavior in the model, as well as the number of vertices in a neighborhood of given radius on the network. We derive the value of the single critical exponent controlling behavior in the critical region and the finite size scaling form for the average vertex-vertex distance on the network, and, using series expansion and Pade approximants, find an approximate analytic form for the scaling function. We calculate the effective dimension of small-world graphs and show that this dimension varies as a function of the length-scale on which it is measured, in a manner reminiscent of multifractals. We also study the problem of site percolation on small-world networks as a simple model of disease propagation, and derive an approximate expression for the percolation probability at which a giant component of connected vertices first forms (in epidemiological terms, the point at which an epidemic occurs). The typical cluster radius satisfies the expected finite size scaling form with a cluster size exponent close to that for a random graph. All our analytic results are confirmed by extensive numerical simulations of the model.

Network Sampling: Some First Steps

Mark Granovetter — 2006-11-29T18:21:45-00:00

The American Journal of Sociology, Vol. 81, No. 6. (1976), pp. 1287-1303.

Social network research has been confined to small groups because large networks are intractable, and no systematic theory of network sampling exists. This paper describes a practical method for sampling average acquaintance volume (the average number of people known by each person) from large populations and derives confidence limits on the resulting estimates. It is shown that this average figure also yields an estimate of what has been called "network density." Applications of the procedure to community studies, hierarchical structures, and interorganizational networks are proposed. Problems in developing a general theory of network sampling are discussed.

A Survey of Models of Network Formation: Stability and Efficiency

M Jackson — 2006-09-15T22:00:50-00:00

(2003)

I survey the recent literature on the formation of networks. I provide definitions of network games, a number of examples of models from the literature, and discuss some of what is known about the (in)compatibility of overall societal welfare with individual incentives to form and sever links.

Weak pairwise correlations imply strongly correlated network states in a neural population

Elad Schneidman — 2006-04-09T19:27:39-00:00

Nature (09 April 2006)