Giorgio Gosti Project 2012 -- https://www.researchgate.net/profile/Giorgio_Gosti
- Giorgio, Maria, Joele, Nico Buon natale a tutti. Now Giorgio, Maria, Joele, Nico
- 1 "Evolutionary Dynamics" Compared to "Classical Dynamics"
- 2 Authors and Words Bipartite Network
- 3 Abstract Example
- 4 Abstracts to Authors
- 5 Abstracts to Words
- 6 Authors to Words
- 7 Using Galois Lattices to Rebuild Taxonomies of Knowledge Communities
- 8 Topic Model or Latent Semantic Analysis (LSA)
- 9 Causality and Authors-Words Network Structure
- 10 SCCS maps
"Evolutionary Dynamics" Compared to "Classical Dynamics"
"Questa evoluzione non può più essere considerata come risultato di un obbedienza al principio autocratico che nulla accade che non sia prescritto. Essa è meglio rappresentata dal principio democratico che può accadere tutto ciò che non sia proibito. Fuori di metafora le leggi fisiche sempre più ci appaiono essere l’espressione di vincoli, la formulazione di proibizioni, o la richiesta di compatibilità, piuttosto che la manifestazione di istruzioni coercitive che impongono comportamenti prevedibili in ogni dettaglio." M. Cini, Il caso nelle scienze del ’900. Caso, Necessità, Libertà, serie di seminari del Cirms - dell’università la Sapienza di Roma - AA 1997-1998, pag. 13-29, Napoli, 1998. CUEN.
Such an evolution can not be considered the outcome of the obedience at a autocratic principle, in which nothing that is not prescribed can happen. It is better represented by a democratic principle, in which anything that is not prohibited can happen. Metaphors aside, the laws of physics appear more and more to be the expressions of constraints, the expression of prohibitions, or the requirement of compatibility, rather then the manifestation of coercive instructions that impose predictable behaviors in full detail.
Authors and Words Bipartite Network
We present here the "NSF Research Award Abstracts 1990-2003 Data Set" that can be found at UCI Machine Learning Repository. (Frank, A. & Asuncion, A. (2010)).
A sample abstract is shown in the next section.
A list of abstract-word tuples representing the words in an abstract was automatically processed with a text analyzer called NSFAbst, built using VisualText. While most fields of the output are very accurate, the authors were not extracted from the Investigator: field with 100% accuracy, due to wide variability in that field.
The word list came from a separate process, and may not include all the words of interest in the abstracts.
================================================================== Title : CAREER: Markov Chain Monte Carlo Methods Type: Award NSF Org : CCR Latest Amendment Date : May 5, 2003 File : a0237834 Award Number: 0237834 Award Instr.: Continuing grant Prgm Manager: Ding-Zhu Du CCR DIV OF COMPUTER-COMMUNICATIONS RESEARCH CSE DIRECT FOR COMPUTER & INFO SCIE & ENGINR Start Date : August 1, 2003 Expires : May 31, 2008 (Estimated) Expected Total Amt. : $400000 (Estimated) Investigator: Eric Vigoda firstname.lastname@example.org (Principal Investigator current) Sponsor : University of Chicago 5801 South Ellis Avenue Chicago, IL 606371404 773/702-8602 NSF Program : 2860 THEORY OF COMPUTING Fld Applictn: Program Ref : 1045,1187,9216,HPCC, Abstract : Markov chain Monte Carlo (MCMC) methods are an important algorithmic device in a variety of fields. This project studies techniques for rigorous analysis of the convergence properties of Markov chains. The emphasis is on refining probabilistic, analytic and combinatorial tools (such as coupling, log-Sobolev, and canonical paths) to improve existing algorithms and develop efficient algorithms for important open problems. Problems arising in computer science, discrete mathematics, and physics are of particular interest, e.g., generating random colorings and independent sets of bounded-degree graphs, approximating the permanent, estimating the volume of a convex body, and sampling contingency tables. The project also studies inherent connections between phase transitions in statistical physics models and convergence properties of associated Markov chains. The investigator is developing a new graduate course on MCMC methods. ==================================================================
Abstracts to Authors
I used the file docauths.txt which had two rows of data one containing the abstract id (docid), and the other containing the relative author of the abstract (Author_string), e.g. 7 Brian Fiedler.
Red is author and green is abstract.
Abstracts to Words
I used the file docwords.txt which had three rows of data one containing the abstract id (docid), the second containing the relative word id of the words in the abstract (wordid), and the third column contains the words frequency (freq), e.g., 1 9792 1.
Blue is Abstract and green is word.
Authors to Words
I transposed the abstracts to authors network and got the authors to abstract network. Then, I multiplied the authors to abstract network with the abstract to words network. In this way I obtained the authors to words network.
This network assigns a subset of words to each authors. These words represent the words that the agent uses to describe its research.
Red is author and blue is abstract.
Words in degree for authors to words network.
Using Galois Lattices to Rebuild Taxonomies of Knowledge Communities
In this Section, I advance the hypothesis that the authors-word network can be used to reconstruct not only the Social Network of the research fields and sub-fields, but also the knowledge communities.
This type of datais particularly fit for this task, because we can assume a strong agreement on the meaning and use of the words. In fact, this words are scientific terms that we expect to be well defined, unambiguous. Used by scientist which are experts in their field.
This image is from the paper: Roth, Camille. (2005). Co-evolution in Epistemic Networks -- Reconstructing Social Complex Systems. Structure and Dynamics, 1(3). Retrieved from: http://escholarship.org/uc/item/63z855xj
Topic Model or Latent Semantic Analysis (LSA)
It is feasible to use topic modelling or LSA to improve our taxonomy lattice?
Causality and Authors-Words Network Structure
This data represent only the first period (1990-1994) of three data sets each representing a following period (1995-1999 and 2000-2005).
For future work, it would be also very interesting to understand, if and how centrality measure or generally structural measure can predict the emergence of new popular words.