1) The document discusses analyzing social networks using mobile phone call and SMS data. It finds communities have core strong ties within and weak ties between, and weak ties help spread information.
2) Dynamics show bursty communication patterns. Bursts remain even after removing weekly patterns, suggesting multiple reasons for bursts.
3) Social interactions are contextual. Events happen in overlapping communities and bursty patterns differ by communication context. Understanding both topology and dynamics provides a "Social Connectome" of comprehensive social interaction mapping.
Artificial Intelligence In Microbiology by Dr. Prince C P
Social Connectome: Unified Frame for Communities and Bursts
1. Social Connectome
Hang-Hyun Jo
Dept. of Physics, Pohang University of Science and Technology, Republic of Korea
Dept. of Computer Science, Aalto University School of Science, Finland
2. Outline
• Mobile phone data for temporal social networks
• Community structure and bursty dynamics
• Interaction is contextual!
• Overlapping communities and contextual bursts
• Demographic and geographic analysis
• Towards Social Connectome
4. Q1: What does the social network
look like?
Q2: What drives the evolution of
the social network?
(from a physicist’s viewpoint…)
5. A physicist’s viewpoint
• More interested in the universal patterns than in
the details (But, the devil is in the detail…)
• Apply and extend physics to solve the problems
derived from social phenomena
6. Why mobile phone data?
• Mobile phones carried by people almost always
• Almost 100% of coverage in many countries
• Good proxy of the real social networks
7. Mobile phone data
• Source: A European operator
• Time-resolved call/SMS events among several
millions of mobile phone users
• Topology = communication network
• Dynamics = temporal patterns of communication
9. Community structure
Onnela et al., PNAS, NJP (2007)
A
1
100
10
strong ties in
community
weak ties between
communities
10. c~~~~~~~~~~~~~~~~
D AH G
D/
C~ A B X
E
(b)
FIG. 2.-Local bridges. a, Degree 3; b, Degree 13. = strong ti
weak tie.
Strength of weak ties
Granovetter, AJS (1973)
strong tie
weak tie
11. Modeling communities
Kumpula et al., PRL (2007)
global
attachment
local
attachment
preferential
reinforcement
global
attachment
reset
node
13. Bursty communication
outgoing calls →
incoming calls →
burst
inter-event time τ
Karsai et al., PRE (2011)
line) Spreading dynamics in the Reality Mining
orks (right), for the original event sequence (᭺)
W ( ) and DCWB (᭛). In the email network,
is directed. The maximum prevalence is limited
the SCC and the OUT component (∼85%).
tor 2. Similarly for the 100% prevalence
2 (∼342 d), showing that the effects of
sistent for the duration of the whole process
uns. As for the effect of the random initial
all error of mean values in Table I show
s (Fig. 1) characterize the overall behavior
nitial conditions are demonstrated in Fig. 1,
ons are clearly separable at full prevalence.
Reality Mining mobile call network and for
hown in Fig. 2, with the DCW and DCWB
tcome is qualitatively similar with that of
ere are certain differences. In the small and
, successive calls to many people within a
y a hub give rise to a steep prevalence rise.
one-off event and the effect is destroyed
In the email network, very high-degree
uent emails give rise to rapid spreading
hed. This effect is conserved in the null
y pattern, i.e., variation in overall commu-
by the hour, is retained in every null model
andomizing the original event sequence.
gested that natural periodicities, such as
responsible for the fat-tailed waiting time
er to evaluate the impact of the daily pattern
peed, we carried out simulations where
N was used as the network. Events were
ks by two Poisson processes that conserve
mogeneous Poisson process, and a process
s rate follows the daily pattern as calculated
cs on hourly basis (see inset in Fig. 3). The
h cases are shown in Fig. 3. The difference
urves is negligible, demonstrating that the
ly a minor impact on the spreading speed.
FIG. 3. (Color online) Spreading dynamics as obtained from a
Poissonian event-generating model on the aggregated MCN, with
daily pattern ( ) and without (᭞). Link weights were taken into
account and the curve with the daily pattern is comparable with the
DCW null model. Inset: the average daily pattern as observed for the
MCN event sequence with binning by the hour. The continuous line
is to guide the eye.
exclude the possibility that the fat tail in the interevent time
distribution is only due to the broad weight distribution as
suggested in [21], we calculated the distributions for binned
weights and obtained satisfactory scaling with the average
interevent time, same as [17]. We find that the distribution can
be fitted by a power law with exponent 0.7 over 3.5 decades,
followed by a fast decay. The scaling breaks down for small
interevent times, where a peak in the distribution at ∼20 s is
found, which is due to event correlations between links. The
power law indicates non-Poissonian bursty character of the
events. Both the characteristics vanish for the time-shuffled
null model and the interevent time is well described by an
FIG. 4. (Color online) Scaled interevent time distributions for
P(⌧) ⇠ ⌧ ↵
16. Cyclic Poisson process
Malmgren et al., PNAS (2008)
time-varying rate with weekly cycle for e-mail usage
heavy tail of inter-event time distribution
Question: Are weekly cycles the ONLY reason for bursts?
17. De-seasoning cycles?
Jo et al., NJP (2012)
mobile call sequence of one user
: weekly cycle (T=7 days)⇢(t)
: no cyclic patterns⇢⇤
(t⇤
) = 1
de-seasoned by weekly cycle
B7 = 0.146
: burstiness parameter
Goh & Barabási, EPL (2008)
B0 = 0.224
B =
m
+ m
18. Bursts are robust!
Burstiness remains finite after
de-seasoning weekly cycles.
burstiness
de-seasoning period (days)
different activity group
19. strong link weak link
Temporal networks
time
event
inter-eventtime
25. Figure 2. TI-OR model. A. The cumulative weight distribution Pc(w). B. The average number of next nearest neighbors knn(k). C. The aver
overlap O(w). D. The local clustering coefficient c(k). E. The inter-event time distribution P(t). F. The average strength s(k). Results are averaged o
50 realizations for networks with N~5|104
and pML
~10{3
. We obtain SkT&10:1 and ScT&0:08 for pLA
~0:013 and pGA
~0:1. The cases w
pLA
~0:1 and/or with pGA
~0:07 are also plotted for comparison.
doi:10.1371/journal.pone.0022687.g002
Bursts and Communities in Evolving Networks
Figure 3. TI-AND model. A. The cumulative weight distribution Pc(w). B. The av
overlap O(w). D. The local clustering coefficient c(k). E. The inter-event time distribu
50 realizations for networks with N~5|104
and pML ~10{3
. We obtain SkT&9:6 an
and/or with pGA
~0:04 are also plotted for comparison.
doi:10.1371/journal.pone.0022687.g003
Figure 5. Link percolation analysis. A, B. TI-OR model. C, D. TI-AND model. E, F.
and the overlap (right panel). For each panel, we calculate the fraction of giant compo
as a function of the fraction of removed links, f . Results are averaged over 50 realiz
doi:10.1371/journal.pone.0022687.g005
OR model AND model
26. Summary
• Topology of communication
• Granovetter: Strength of weak ties
• Kumpula’s model with global/local attachments
• Dynamics of communication
• Bursts of events
29. Time-ordering
Figure 11 Time-ordering behavior between services. (a) Distributions of time interval
consecutive events of different services s and s′. (b) Diagram for time-ordering behavior bet
based on the distributions of time interval.
Table 1 k-means clustering results for weekly patterns of service usages
Service q = 0 Ns
web 74 9 7 6 5 3 3 2 1 1 111
app 50 32 10 7 6 6 5 4 3 1 124
2012, 1:10 Page 13 of 18
nce.com/content/1/1/10
inter-event time between
different services/contexts
communication
services
non-communication
services
31. Overlapping community
Family Work
Alice
Bob
Family
Alice
Bob
Link communities
Work
Alice
Bob
Node communities
Spouses Alice and Bob also work togethera b
The Alice-Bob link was placed in family but both
home and work relationships are identified
Ahn et al., Nature (2010)
The geo-
nly break
Fig. 4d, we
munity in
nity along
structures
idence for
l scale. To
ntitatively
103
200
s
H+
Threshold, t = 0.20
t = 0.27
0.4
D
0.6 0.8 1
d Largest community
Largest
subcommunity
Remaining
hierarchy
t
e
c
Word associationMetabolic
0.8
1
Phone
Largest
community
Second
largest
Third largest
mobile phone data
32. Multilayer social networks
Murase, Jo et al., PRE (2014)
AYER WEIGHTED SOCIAL NETWORK MODEL PHYSICAL REVIEW E 90, 052810 (2014)
(a) L=1
asc.
desc.
(b) L=2
asc.
desc.
0.2 0.4 0.6 0.8 1
f
0 0.2 0.4 0.6 0.8 1
f
1. (Color online) Link percolation analysis for L = 1 (left)
2 (right). The upper figures show the relative size of the
onnected component, RLCC, as a function of the fraction of
ved links f . The lower figures show the susceptibility χ.
(green dashed) lines correspond to the case when links are
(b) p=0.01
(c) p=0.1 (d) p=1
(a) p=0
FIG. 2. (Color) Snapshots of the copy-and-shuffle model with
different p shuffling parameter values and N = 300. Red (blue) links
are in the first (second) layer, and green links are in both layers.
UC
MN
matic illustration of three kinds of correlated multiplex networks, maximally-positive (MP), uncorrelated (UC), and maximally-
f the networks has different types of links, indicated by solid and dashed links, respectively.
th permission from Ref. [48].
hysical Society.
mple of all possible multilinks in a multiplex network with M = 2 layers and N = 5 nodes. Nodes i and j are linked by one
m Ref. [113].
rade layers. Even the two ‘‘negative’’ layers of enmity and attack have significant overlap of the links.
Boccaletti et al., Phys. Rep. (2014)
41. Other issues
• General framework for stylized facts in social
networks [Jo, Murase, Torok, Kaski, Kertesz]
• Correlated bursts [Karsai et al., Sci. Rep. (2012); Jo et al., Phys.
Rev. E (2015)]
• Dynamics on networks: spreading [Jo et al., Phys. Rev. X
(2014)]
• Perception-based network formation [Jo et al., submitted]
42. References
• Onnela et al., Proc. Nat. Acad. Sci. USA 104, 7332 (2007); New J. Phys. 9, 179 (2007)
• Granovetter, Am. J. Sociol. 78, 1360 (1973)
• Kumpula et al., Phys. Rev. Lett. 99, 228701 (2007)
• Karsai et al., Phys. Rev. E 83, 025102 (2011)
• Barabasi, Nature 435, 207 (2005)
• Malmgren et al., Proc. Nat. Acad. Sci. USA 105, 18153 (2008)
• Goh & Barabasi, EPL 81, 48002 (2008)
• Jo et al., New J. Phys. 14, 013055 (2012)
• Jo et al., PLOS ONE 6, e22687 (2011)
• Jo et al., EPJ Data Science 1, 10 (2012)
• Ahn et al., Nature 466, 761 (2010)
• Murase, Jo et al., Phys. Rev. E 90, 052810 (2014)
• Jo et al., Phys. Rev. E 87, 062131 (2013)
• Jo et al., Phys. Rev. X 4, 011041 (2014)