Statistics
| Branch: | Revision:

mobicen / documents / soaEXT.md @ master

History | View | Annotate | Download (5.15 KB)

1
SVILUPPI POSSIBILI:
2
- EFFETTI DELLA CORRELAZIONE, vedere come cambia una performance metric di uno streaming/diffusion-process in base a qualche parametro di mobilità
3
- vedere se possiamo sfruttare la correlazione con past-centrality per predirre la next-centr
4
- misurare la k-coreness
5

    
6
Cosa posso plottare?
7

    
8
0) Autocorrelazione delle 3 metriche (al variare di ray OR speed OR density OR mob-model)
9

    
10
1) Correlazione fra le 3 metriche: 
11

    
12
    BC      DEG     KCORE
13
BC
14

    
15
DEG
16

    
17
KCORE
18

    
19
In diversi scenari: (dense vs sparse), (slow vs fast), (short-range vs long-range)  (mobility models)
20

    
21
2) Rank Correlation...
22

    
23
# How Correlated Are Network Centrality Measures?
24
Thomas W. Valente, PhD,
25
University of Southern California, Department of Prevention Research, Los Angeles
26
Kathryn Coronges, MPH,
27
University of Southern California, Department of Prevention Research, Los Angeles
28
Cynthia Lakon, PhD, and
29
University of Southern California, Department of Prevention Research, Los Angeles
30
Elizabeth Costenbader, PhD
31
Research Triangle Institute, Raleigh North Carolina
32

    
33
We correlated the 9 measures for each network and then calculated the average correlation,
34
standard deviation, and range across centrality measures. We also calculated the overall
35
correlation and compared it by study to assess the degree of variation in average correlation
36
between studies. Lastly, we explore the associations between four different sociometric
37
network properties (i.e., density, reciprocity, centralization and number of components) and
38
the centrality correlations. This last analysis seeks to determine whether centrality measures
39
are more highly correlated in *dense or sparse networks*, in reciprocal or non-reciprocal
40
networks, in *centralized or decentralized* networks, and in networks with *few or many
41
components*. Density is the number of ties in the network divided by the total possible number
42
of ties (Nx(N−1)). Reciprocity was measured as the percent of possible ties that are symmetric.
43
Degree centralization was measured using Freeman’s (1979) formula. The number of
44
components in the network was determined by symmetrizing the network and calculating
45
components.
46

    
47

    
48
# Betweenness centrality correlation in social networks
49
K.-I. Goh, E. Oh, B. Kahng, and D. Kim
50
PHYSICAL REVIEW E 67, 017101 2003
51

    
52
PHYSICAL REVIEW E 85, 026107 (2012)
53
# Temporal node centrality in complex networks
54
Hyoungshick Kim and Ross Anderson
55
Computer Laboratory, University of Cambridge, 15 J. J. Thomson Avenue, Cambridge CB3 0FD, United Kingdom
56
(Received 1 November 2011; published 13 February 2012)
57

    
58
--> Buona definizione di Temporal Betweenness, qualche studio di correlazione (ma non si capisce tanto)
59

    
60

    
61
# Centrality prediction in dynamic human contact networks
62
Hyoungshick Kim ⇑ , John Tang, Ross Anderson, Cecilia Mascolo
63
Computer Laboratory, University of Cambridge, United Kingdom
64

    
65
--> un sacco di plot simili ai miei per grafi dinamici molto regolari (contact networks), "gente a conferenze" e "gente in giro per scuola". Fanno i surface-plot che facciamo noi e considerazioni simili sulla correlazioni della BC a vari lag-width.
66
Lo fanno per poi definire dei "predictor" di centralità
67

    
68

    
69
PHYSICAL REVIEW E 84, 016105 (2011)
70
# Path lengths, correlations, and centrality in temporal networks
71
Raj Kumar Pan and Jari Saramäki
72

    
73
--> Abbastanza inutile... la correlazione che studiano è tra "temporal distance" e "static graph distance". Le due metriche dipendono dal tipo di grafo utilizzato per rappresentare i loro dataset (telefonate, voli-aerei). Non studiano tanto la centralità nel tempo. Dicono solo che, rispetto al degree o all'indice k-shell/k-core, la Closeness Centrality è più fortemente correlata rispetto alla temporal closeness centrality. 
74

    
75

    
76
# Time-Dependent Complex Networks: Dynamic Centrality, Dynamic Motifs, and Cycles of Social Interactions
77
Dan Braha and Yaneer Bar-Yam
78

    
79
--> Brutto paper, ma fanno tante misure, inclusa correlazione tra esistenza di archi a diversi istanti di tempo
80

    
81

    
82
# Betweenness Preference: Quantifying Correlations in the Topological Dynamics of Temporal Networks
83
René Pfitzner,* Ingo Scholtes, † Antonios Garas, ‡ Claudio J. Tessone, § and Frank Schweitzer k
84
ETH Zurich, Chair of Systems Design, Weinbergstrasse 56/58, 8092 Zurich, Switzerland
85
(Received 8 August 2012; revised manuscript received 19 March 2013; published 10 May 2013)
86

    
87
--> betweenness preference, i.e., the
88
tendency of nodes to preferentially connect—in a temporal
89
sense—particular pairs of neighbors, (i) is not captured in
90
the time-aggregated network, (ii) is present in empirical
91
temporal network data, (iii) changes the topology of time-
92
respecting paths, and (iv) critically influences dynamical
93
processes evolving on temporal networks.
94

    
95
Studiano la probabilità che un nodo v sia collegato a s al tempo t, e a d al tempo t+1.
96
Così definiscono la BC^v_{s,d} = 1 se c'è un arco s-v al tempo t un arco v-d al tempo t+1, 0 altrimenti.
97
Poi pigliano dati reali di contatti fra gente e dimostrano c'è "betwenness preference" in grafi reali
98
e non ce n'è in grafi temporali generati a caso. Pertanto dicono che questa proprietà di "btw preference"
99
non va mai scordata anzi, ci danno nu generatore per generare grafi che possiedono una buona distribuzione
100
di btw preference.