TY  - GEN
AU  - Newman, M. E. J.
A2  - 
T1  - A measure of betweenness centrality based on random walks
JO  - 
PB  - 
C1  - 
PY  - 2003/10
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://arxiv.org/abs/cond-mat/0309045
DO  - 
KW  - betweenness
KW  - centrality
KW  - measure
KW  - randomWalk
KW  - sna
L1  - 
N1  - 
N1  - 
AB  - Betweenness is a measure of the centrality of a node in a network, and is normally calculated as the fraction of shortest paths between node pairs that pass through the node of interest. Betweenness is, in some sense, a measure of the influence a node has over the spread of information through the network. By counting only shortest paths, however, the conventional definition implicitly assumes that information spreads only along those shortest paths. Here we propose a betweenness measure that relaxes this assumption, including contributions from essentially all paths between nodes, not just the shortest, although it still gives more weight to short paths. The measure is based on random walks, counting how often a node is traversed by a random walk between two other nodes. We show how our measure can be calculated using matrix methods, and give some examples of its application to particular networks.
ER  -

TY  - JOUR
AU  - Park, Juyong
AU  - Newman, M. E. J.
T1  - The origin of degree correlations in the Internet and other networks
JO  - Physical Review E
PY  - 2003/
VL  - 68
IS  - 
SP  - 
EP  - 
UR  - doi:10.1103/PhysRevE.68.026112
DO  - 
KW  - correlations
KW  - degree
KW  - internet
KW  - measure
KW  - networks
KW  - origin
KW  - social
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 
ER  -

TY  - JOUR
AU  - Pastor-Satorras, R.
AU  - Vázquez, A.
AU  - Vespignani, A.
T1  - Dynamical and correlation properties of the Internet
JO  - Physical Review Letters
PY  - 2001/
VL  - 87
IS  - 25
SP  - 
EP  - 
UR  - http://scholar.google.de/scholar.bib?q=info:KLiz1q2axUQJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0
DO  - 
KW  - analysis
KW  - correlation
KW  - degree
KW  - internet
KW  - measure
KW  - network
L1  - 
SN  - 
N1  - Internet Topologie, sna maße
N1  - 
AB  - 
ER  -

TY  - CONF
AU  - Adda, Gilles
AU  - Mariani, Joseph
AU  - Lecomte, Josette
AU  - Paroubek, Patrick
AU  - Rajman, Martin
A2  - 
T1  - The GRACE French Part-of-Speech Tagging Evaluation Task
T2  - proceedings of the First International Conference on Language Resources and Evaluation (LREC
PB  - 
C1  - 
PY  - 1998/
CY  -  
VL  - 
IS  - 
SP  - 433
EP  - 441
UR  - 
DO  - 
KW  - decision
KW  - measure
KW  - pos
KW  - precision
KW  - recall
KW  - tagging
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 
ER  -

TY  - JOUR
AU  - Watts, Duncan J.
AU  - Strogatz, Steven H.
T1  - Collective dynamics of /`small-world/' networks
JO  - Nature
PY  - 1998/06
VL  - 393
IS  - 6684
SP  - 440
EP  - 442
UR  - http://dx.doi.org/10.1038/30918
DO  - 
KW  - collective
KW  - dynamics
KW  - measure
KW  - networks
KW  - smallworld
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 
ER  -

TY  - CONF
AU  - Jiang, J.J.
AU  - Conrath, D.W.
A2  - 
T1  - Semantic similarity based on corpus statistics and lexical taxonomy
T2  - Proc. of the Int'l. Conf. on Research in Computational Linguistics
PB  - 
C1  - 
PY  - 1997/
CY  -  
VL  - 
IS  - 
SP  - 19
EP  - 33
UR  - http://www.cse.iitb.ac.in/~cs626-449/Papers/WordSimilarity/4.pdf
DO  - 
KW  - 1997
KW  - Conrath
KW  - Jiang
KW  - JiangConrath
KW  - folksonomy
KW  - lexical
KW  - measure
KW  - semantic
KW  - similarity
KW  - taxonomy
L1  - 
SN  - 
N1  - Jiang Conrath Maß
N1  - 
AB  - This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.
ER  -