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Networked
user engagement
user engagement optimization workshop - CIKM 2013
In	
  collabora*on	
  with:	
  
Mounia	
  Lalmas,	
  	
  
Ricardo	
  Baeza-­‐Yates,	
  	
  
Elad	
  Yom-­‐Tov	
  
Jane%e	
  Lehmann	
  
outline
1.  Mo%va%on	
  
How	
  to	
  measure	
  user	
  engagement?	
  
	
  
	
  
2.  Network	
  metrics	
  
From	
  site	
  to	
  network	
  engagement.	
  
	
  
	
  
3.  Measuring	
  networked	
  user	
  engagement	
  
Case	
  study	
  based	
  on	
  the	
  network	
  of	
  Yahoo	
  sites.	
  
Lights	
  on	
  by	
  JC*+A!	
  
182-­‐365+1	
  by	
  meaganmakes	
  
How	
  to	
  measure	
  
User	
  
Engagement?	
  
Measuring user engagement
4	
  JaneHe	
  Lehmann	
   MoJvaJon	
  
User	
  engagement	
  on	
  Yahoo!	
  
Web	
  traffic	
  reports:	
  Google	
  Analy%cs,	
  Alexa.com,	
  …	
  
Popularity 	
  (#Users)	
  
Ac%vity 	
   	
  (DwellTime)	
  
Loyalty 	
   	
  (AcJveDays)	
  
Measuring user engagement
5	
  JaneHe	
  Lehmann	
   MoJvaJon	
  
However,	
  Yahoo!	
  has	
  many	
  sites:	
  	
  
maps,	
  flickr,	
  weather,	
  news,	
  shine,	
  etc.	
  
	
  
How	
  to	
  measure	
  engagement	
  within	
  	
  
a	
  network	
  of	
  sites?	
  
NETWORK OF SITES
6	
  JaneHe	
  Lehmann	
   MoJvaJon	
  
frontpage	
  
tv	
   sports	
  
shopping	
  
autos	
  
search	
  
daJng	
  
jobs	
  
news	
  
shine	
  
groups	
  
maps	
  
local	
  
health	
  
answer	
  
weather	
  
games	
  
mail	
  
omg	
  
homes	
  
travel	
  
flickr	
  
finance	
  
Large	
  online	
  providers	
  	
  
(AOL,	
  Google,	
  Yahoo!,	
  etc.)	
  	
  
offer	
  not	
  one	
  site,	
  	
  
but	
  a	
  network	
  of	
  sites	
  
NETWORK OF SITES
7	
  JaneHe	
  Lehmann	
   MoJvaJon	
  
frontpage	
  
tv	
   sports	
  
shopping	
  
autos	
  
search	
  
daJng	
  
jobs	
  
news	
  
shine	
  
groups	
  
maps	
  
local	
  
health	
  
answer	
  
weather	
  
games	
  
mail	
  
omg	
  
homes	
  
travel	
  
flickr	
  
finance	
  
Each	
  site	
  is	
  usually	
  	
  
op%mized	
  individually,	
  	
  
with	
  some	
  effort	
  to	
  direct	
  	
  
users	
  between	
  them	
  
NETWORK OF SITES
8	
  JaneHe	
  Lehmann	
   MoJvaJon	
  
frontpage	
  
tv	
   sports	
  
shopping	
  
autos	
  
search	
  
daJng	
  
jobs	
  
news	
  
shine	
  
groups	
  
maps	
  
local	
  
health	
  
answer	
  
weather	
  
games	
  
mail	
  
omg	
  
homes	
  
travel	
  
flickr	
  
finance	
  
Online	
  mul%tasking	
  
Users	
  switch	
  between	
  sites	
  
within	
  one	
  online	
  session	
  	
  
(e.g.	
  emailing,	
  reading	
  news,	
  	
  
social	
  networking,	
  
search)	
  
NETWORK OF SITES
9	
  JaneHe	
  Lehmann	
   MoJvaJon	
  
frontpage	
  
tv	
   sports	
  
shopping	
  
autos	
  
search	
  
daJng	
  
jobs	
  
news	
  
shine	
  
groups	
  
maps	
  
local	
  
health	
  
answer	
  
weather	
  
games	
  
mail	
  
omg	
  
homes	
  
travel	
  
flickr	
  
finance	
  
Measuring	
  user	
  	
  
engagement	
  should	
  	
  
account	
  for	
  	
  
user	
  traffic	
  between	
  	
  
sites...	
  
NETWORK OF SITES
10	
  JaneHe	
  Lehmann	
   MoJvaJon	
  
frontpage	
  
tv	
   sports	
  
shopping	
  
autos	
  
search	
  
daJng	
  
jobs	
  
news	
  
shine	
  
groups	
  
maps	
  
local	
  
health	
  
answer	
  
weather	
  
games	
  
mail	
  
omg	
  
homes	
  
travel	
  
flickr	
  
finance	
  
…	
  to	
  detect	
  
sites	
  that	
  are	
  	
  
used	
  together	
  
NETWORK OF SITES
11	
  JaneHe	
  Lehmann	
   MoJvaJon	
  
frontpage	
  
tv	
   sports	
  
shopping	
  
autos	
  
search	
  
daJng	
  
jobs	
  
news	
  
shine	
  
groups	
  
maps	
  
local	
  
health	
  
answer	
  
weather	
  
games	
  
mail	
  
omg	
  
homes	
  
travel	
  
flickr	
  
finance	
  
…	
  to	
  detect	
  
isolated	
  
sites	
  
NETWORK OF SITES
12	
  JaneHe	
  Lehmann	
   MoJvaJon	
  
frontpage	
  
tv	
   sports	
  
shopping	
  
autos	
  
search	
  
daJng	
  
jobs	
  
news	
  
shine	
  
groups	
  
maps	
  
local	
  
health	
  
answer	
  
weather	
  
games	
  
mail	
  
omg	
  
homes	
  
travel	
  
flickr	
  
finance	
  
…	
  to	
  detect	
  
important	
  
sites	
  
Danboard	
  by	
  sⓘndy°	
  
From	
  site	
  to	
  network	
  
engagement	
  	
  
Looking	
  beyond	
  one	
  	
  	
  	
  	
  
own	
  nose!	
  
ENGAGEMENT NETWORKS
14	
  Networked	
  User	
  Engagement	
  JaneHe	
  Lehmann	
  
State	
  of	
  the	
  art	
   Our	
  approach	
  
Non-
provider
service
Provider
service
Provider
network
Traffic
Provider
service
under
study
G=(V,	
  E,	
  λ)	
  
V:	
   n<i>	
  -­‐	
  Sites,	
  services,	
  funcJonaliJes,	
  etc.	
   n<e>	
  -­‐	
  External	
  sites	
  
E:	
   e<i>	
  -­‐	
  Traffic	
  between	
  internal	
  nodes	
  
λ(e):	
   Traffic	
  volume	
  (#Users)	
  
Network	
  metrics	
  
	
  
	
   	
   	
   	
  TrafficVolume	
  
	
   	
   	
   	
  Def.:	
  Sum	
  of	
  edge	
  weights	
  
	
   	
   	
   	
  A	
  high	
  value	
  is	
  a	
  sign	
  of	
  a	
  high	
  networked	
  user	
  engagement;	
  	
  
	
   	
   	
   	
  users	
  navigate	
  oHen	
  between	
  the	
  sites	
  of	
  the	
  network.	
  
	
  
	
   	
   	
   	
  Density	
  
	
   	
   	
   	
  Def.:	
  ProporJon	
  of	
  edges	
  to	
  maximum	
  possible	
  
	
   	
   	
   	
  A	
  high	
  value	
  is	
  a	
  sign	
  of	
  a	
  high	
  networked	
  user	
  engagement;	
  	
  
	
   	
   	
   	
  users	
  navigate	
  between	
  many	
  different	
  sites.	
  
	
  
	
   	
   	
   	
  Reciprocity	
  
	
   	
   	
   	
  Def.:	
  RaJo	
  of	
  number	
  of	
  edges	
  in	
  both	
  direcJons	
  
	
   	
   	
   	
  A	
  high	
  value	
  is	
  a	
  sign	
  of	
  a	
  high	
  networked	
  user	
  engagement;	
  
	
   	
   	
   	
  users	
  do	
  not	
  only	
  navigate	
  from	
  one	
  site	
  to	
  another,	
  they	
  also	
  tend	
  to	
  return	
  	
  
	
   	
   	
   	
  to	
  previously	
  visited	
  sites.	
  
Network-LEVEL METRICS
15	
  Networked	
  User	
  Engagement	
  JaneHe	
  Lehmann	
  
30 78
22 15 45
9
90
ConnecJvity	
  Size	
  
Network	
  metrics	
  
	
  
	
   	
   	
   	
  Modularity	
  [subNWmod]	
  
	
   	
   	
   	
  Def.:	
  Captures	
  the	
  existence	
  of	
  modules	
  based	
  on	
  a	
  random	
  walk	
  approach	
  
	
   	
   	
   	
  A	
  high	
  modularity	
  indicates	
  that	
  users	
  visit	
  many	
  sites	
  of	
  one	
  subnetwork,	
  	
  
	
   	
   	
   	
  but	
  hardly	
  navigate	
  to	
  other	
  subnetworks.	
  
	
  
	
   	
   	
   	
  Global	
  Clustering	
  Coefficient	
  [GCC]	
  	
  
	
   	
   	
   	
  Def.:	
  Captures	
  the	
  existence	
  of	
  Jghtly	
  connected	
  groups	
  of	
  nodes	
  
	
   	
   	
   	
  A	
  high	
  value	
  is	
  a	
  sign	
  of	
  a	
  high	
  networked	
  user	
  engagement;	
  
	
   	
   	
   	
  users	
  access	
  sites	
  directly,	
  instead	
  of	
  using	
  front	
  pages.	
  
	
  
	
   	
   	
   	
  	
  
Network-LEVEL METRICS
16	
  Networked	
  User	
  Engagement	
  JaneHe	
  Lehmann	
  
Low GCC High GCC
Modularity	
  
ENGAGEMENT NETWORKS
Modularity	
  [subNWmod]	
  
Yahoo!	
  Network	
  with	
  3	
  countries	
  
17	
  Networked	
  User	
  Engagement	
  JaneHe	
  Lehmann	
  
low	
  modularity	
  è	
  low	
  engagement	
   high	
  modularity	
  è	
  high	
  engagement	
  
ENGAGEMENT NETWORKS
Modularity	
  [subNWmod]	
  
Yahoo!	
  Network	
  with	
  3	
  countries	
  
18	
  Networked	
  User	
  Engagement	
  JaneHe	
  Lehmann	
  
low	
  modularity	
  è	
  low	
  engagement	
   high	
  modularity	
  è	
  high	
  engagement	
  
low	
  modularity	
  	
  
è	
  high	
  engagement	
  
Country	
  
lem	
  by	
  	
  [	
  embr	
  ]	
  	
  
Case	
  Study	
  	
  
Measuring	
  	
  
Networked	
  user	
  
engagement	
  
ENGAGEMENT NETWORKS
Interac%on	
  data	
  
•  July	
  2011	
  
•  2M	
  user,	
  25M	
  sessions	
  
•  728	
  Yahoo!	
  sites	
  (news,	
  mails,	
  search,	
  etc.)	
  
	
  
Networks	
  
•  12	
  weighted	
  directed	
  networks,	
  filtered	
  by	
  
»  Edge	
  types	
   	
  (internal,	
  internal	
  +	
  returning)	
  
»  Time 	
   	
  (Wednesday,	
  Sunday)	
  
»  User	
  loyalty	
   	
  (causal,	
  acJve,	
  VIP)	
  
»  Country	
   	
  (5	
  EU	
  countries)	
  
	
  
•  Applying	
  metrics	
  on	
  networks,	
  results	
  are	
  
normalized	
  using	
  the	
  z-­‐score	
  
20	
  Networked	
  User	
  Engagement	
  JaneHe	
  Lehmann	
  
external	
  
returning	
  edge	
  
z-­‐score	
  
trafficVolume
Country1
Country3
Country4
Country2
Country5
average
above
average
below
average
Network-LEVEL METRICS
Edge-­‐based	
  networks	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
Leaving	
  the	
  network	
  does	
  not	
  necessarily	
  entail	
  less	
  engagement	
  
•  Traffic	
  increases	
  significant	
  when	
  accounJng	
  for	
  returning	
  traffic	
  [18.26%]	
  
•  ConnecJvity	
  increases	
  à	
  User	
  use	
  new	
  navigaJon	
  paths	
  
•  Modularity	
  increases	
  à	
  Users	
  stay	
  in	
  the	
  same	
  part	
  of	
  the	
  network	
  
	
  
21	
  Networked	
  User	
  Engagement	
  JaneHe	
  Lehmann	
  
trafficVolume density reciprocity subNWmod
GCC
Size Connectivity Modularity
Edgeint
Edgeint+ext
Timewed
Timesun
Usercasual
Usernormal
UserVIP
Network-LEVEL METRICS
Time-­‐based	
  networks	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
Naviga%on	
  is	
  more	
  goal-­‐oriented	
  during	
  the	
  week	
  
•  TrafficVolume	
  and	
  density	
  are	
  lower	
  on	
  Sunday	
  à	
  Less	
  acJvity	
  
•  Reciprocity,	
  subNW,	
  and	
  GCC	
  are	
  higher	
  on	
  Sunday	
  à	
  User	
  return	
  more	
  omen	
  to	
  already	
  
visited	
  sites	
  and	
  visit	
  more	
  sites	
  than	
  usual	
  (browsing	
  with	
  less	
  specific	
  goals)	
  
	
  
22	
  Networked	
  User	
  Engagement	
  JaneHe	
  Lehmann	
  
trafficVolume density reciprocity subNWmod
GCC
Size Connectivity Modularity
Edgeint
Edgeint+ext
Timewed
Timesun
Usercasual
Usernormal
UserVIP
Network-LEVEL METRICS
User-­‐based	
  networks	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
Loyal	
  users	
  have	
  also	
  a	
  higher	
  networked	
  user	
  engagement	
  
•  TrafficVolume	
  is	
  higher	
  for	
  VIP	
  user	
  à	
  More	
  acJvity	
  
•  ConnecJvity	
  and	
  modularity	
  is	
  higher	
  for	
  VIP	
  user	
  à	
  Users	
  are	
  interested	
  in	
  more	
  sites	
  
•  GCC	
  is	
  lower	
  for	
  causal	
  users	
  à	
  Users	
  access	
  sites	
  using	
  the	
  front	
  pages	
  
	
   23	
  Networked	
  User	
  Engagement	
  JaneHe	
  Lehmann	
  
trafficVolume density reciprocity subNWmod
GCC
Size Connectivity Modularity
Edgeint
Edgeint+ext
Timewed
Timesun
Usercasual
Usernormal
UserVIP
trafficVolume density reciprocity subNWmod
GCC
Size Connectivity Modularity
Country1
Country3
Country4
Country2
Country5
Network-LEVEL METRICS
Country-­‐based	
  networks	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
Networked	
  user	
  engagement	
  differs	
  between	
  countries	
  
•  Country1:	
  Highest	
  trafficVolume	
  +	
  GCC,	
  high	
  density	
  +	
  reciprocity,	
  lowest	
  subNWmod	
  à	
  high	
  
engagement	
  
•  Country5:	
  Low	
  trafficVolume,	
  but	
  high	
  density	
  	
  +	
  GCC,	
  low	
  subNWmod	
  à	
  less	
  popular,	
  but	
  high	
  
engagement	
  
24	
  Networked	
  User	
  Engagement	
  JaneHe	
  Lehmann	
  
CONCLUSION AND FUTURE WORK
•  Network	
  analysis	
  enhances	
  the	
  understanding	
  of	
  user	
  engagement.	
  
It	
  allows:	
  
•  To	
  analyze	
  users	
  browsing	
  behavior	
  on	
  a	
  global	
  scale	
  
•  To	
  compare	
  networks	
  of	
  sites	
  (e.g.	
  of	
  different	
  countries)	
  
•  To	
  observe	
  differences	
  over	
  Jme	
  
•  Leaving	
  the	
  network	
  does	
  not	
  entail	
  less	
  engagement	
  
•  Some	
  network	
  metrics	
  are	
  more	
  useful	
  than	
  others	
  
Future	
  work:	
  
•  DefiniJon	
  of	
  metrics	
  that	
  combine	
  network	
  traffic	
  and	
  engagement	
  
•  Comparing	
  traffic-­‐	
  and	
  hyperlink-­‐networks	
  
•  Studying	
  the	
  effect	
  of	
  changes	
  in	
  the	
  network	
  
25	
  Networked	
  User	
  Engagement	
  JaneHe	
  Lehmann	
  
Janebe	
  Lehmann	
  
Universitat	
  Pompeu	
  Fabra,	
  Spain	
  
lehmannj@acm.org	
  
	
  
Mounia	
  Lalmas	
  
Yahoo	
  Labs	
  London	
  
mounia@acm.org	
  
	
  
Ricardo	
  Baeza-­‐Yates	
  
Yahoo	
  Labs	
  Barcelona	
  
rbaeza@acm.org	
  
	
  
Elad	
  Yom-­‐Tov	
  
Microsof	
  Research,	
  Herzliya,	
  Israel	
  
elad@ieee.org	
  
	
  

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Networked user engagement

  • 1. Networked user engagement user engagement optimization workshop - CIKM 2013 In  collabora*on  with:   Mounia  Lalmas,     Ricardo  Baeza-­‐Yates,     Elad  Yom-­‐Tov   Jane%e  Lehmann  
  • 2. outline 1.  Mo%va%on   How  to  measure  user  engagement?       2.  Network  metrics   From  site  to  network  engagement.       3.  Measuring  networked  user  engagement   Case  study  based  on  the  network  of  Yahoo  sites.   Lights  on  by  JC*+A!  
  • 3. 182-­‐365+1  by  meaganmakes   How  to  measure   User   Engagement?  
  • 4. Measuring user engagement 4  JaneHe  Lehmann   MoJvaJon   User  engagement  on  Yahoo!   Web  traffic  reports:  Google  Analy%cs,  Alexa.com,  …   Popularity  (#Users)   Ac%vity    (DwellTime)   Loyalty    (AcJveDays)  
  • 5. Measuring user engagement 5  JaneHe  Lehmann   MoJvaJon   However,  Yahoo!  has  many  sites:     maps,  flickr,  weather,  news,  shine,  etc.     How  to  measure  engagement  within     a  network  of  sites?  
  • 6. NETWORK OF SITES 6  JaneHe  Lehmann   MoJvaJon   frontpage   tv   sports   shopping   autos   search   daJng   jobs   news   shine   groups   maps   local   health   answer   weather   games   mail   omg   homes   travel   flickr   finance   Large  online  providers     (AOL,  Google,  Yahoo!,  etc.)     offer  not  one  site,     but  a  network  of  sites  
  • 7. NETWORK OF SITES 7  JaneHe  Lehmann   MoJvaJon   frontpage   tv   sports   shopping   autos   search   daJng   jobs   news   shine   groups   maps   local   health   answer   weather   games   mail   omg   homes   travel   flickr   finance   Each  site  is  usually     op%mized  individually,     with  some  effort  to  direct     users  between  them  
  • 8. NETWORK OF SITES 8  JaneHe  Lehmann   MoJvaJon   frontpage   tv   sports   shopping   autos   search   daJng   jobs   news   shine   groups   maps   local   health   answer   weather   games   mail   omg   homes   travel   flickr   finance   Online  mul%tasking   Users  switch  between  sites   within  one  online  session     (e.g.  emailing,  reading  news,     social  networking,   search)  
  • 9. NETWORK OF SITES 9  JaneHe  Lehmann   MoJvaJon   frontpage   tv   sports   shopping   autos   search   daJng   jobs   news   shine   groups   maps   local   health   answer   weather   games   mail   omg   homes   travel   flickr   finance   Measuring  user     engagement  should     account  for     user  traffic  between     sites...  
  • 10. NETWORK OF SITES 10  JaneHe  Lehmann   MoJvaJon   frontpage   tv   sports   shopping   autos   search   daJng   jobs   news   shine   groups   maps   local   health   answer   weather   games   mail   omg   homes   travel   flickr   finance   …  to  detect   sites  that  are     used  together  
  • 11. NETWORK OF SITES 11  JaneHe  Lehmann   MoJvaJon   frontpage   tv   sports   shopping   autos   search   daJng   jobs   news   shine   groups   maps   local   health   answer   weather   games   mail   omg   homes   travel   flickr   finance   …  to  detect   isolated   sites  
  • 12. NETWORK OF SITES 12  JaneHe  Lehmann   MoJvaJon   frontpage   tv   sports   shopping   autos   search   daJng   jobs   news   shine   groups   maps   local   health   answer   weather   games   mail   omg   homes   travel   flickr   finance   …  to  detect   important   sites  
  • 13. Danboard  by  sⓘndy°   From  site  to  network   engagement     Looking  beyond  one           own  nose!  
  • 14. ENGAGEMENT NETWORKS 14  Networked  User  Engagement  JaneHe  Lehmann   State  of  the  art   Our  approach   Non- provider service Provider service Provider network Traffic Provider service under study G=(V,  E,  λ)   V:   n<i>  -­‐  Sites,  services,  funcJonaliJes,  etc.   n<e>  -­‐  External  sites   E:   e<i>  -­‐  Traffic  between  internal  nodes   λ(e):   Traffic  volume  (#Users)  
  • 15. Network  metrics            TrafficVolume          Def.:  Sum  of  edge  weights          A  high  value  is  a  sign  of  a  high  networked  user  engagement;            users  navigate  oHen  between  the  sites  of  the  network.            Density          Def.:  ProporJon  of  edges  to  maximum  possible          A  high  value  is  a  sign  of  a  high  networked  user  engagement;            users  navigate  between  many  different  sites.            Reciprocity          Def.:  RaJo  of  number  of  edges  in  both  direcJons          A  high  value  is  a  sign  of  a  high  networked  user  engagement;          users  do  not  only  navigate  from  one  site  to  another,  they  also  tend  to  return            to  previously  visited  sites.   Network-LEVEL METRICS 15  Networked  User  Engagement  JaneHe  Lehmann   30 78 22 15 45 9 90 ConnecJvity  Size  
  • 16. Network  metrics            Modularity  [subNWmod]          Def.:  Captures  the  existence  of  modules  based  on  a  random  walk  approach          A  high  modularity  indicates  that  users  visit  many  sites  of  one  subnetwork,            but  hardly  navigate  to  other  subnetworks.            Global  Clustering  Coefficient  [GCC]            Def.:  Captures  the  existence  of  Jghtly  connected  groups  of  nodes          A  high  value  is  a  sign  of  a  high  networked  user  engagement;          users  access  sites  directly,  instead  of  using  front  pages.               Network-LEVEL METRICS 16  Networked  User  Engagement  JaneHe  Lehmann   Low GCC High GCC Modularity  
  • 17. ENGAGEMENT NETWORKS Modularity  [subNWmod]   Yahoo!  Network  with  3  countries   17  Networked  User  Engagement  JaneHe  Lehmann   low  modularity  è  low  engagement   high  modularity  è  high  engagement  
  • 18. ENGAGEMENT NETWORKS Modularity  [subNWmod]   Yahoo!  Network  with  3  countries   18  Networked  User  Engagement  JaneHe  Lehmann   low  modularity  è  low  engagement   high  modularity  è  high  engagement   low  modularity     è  high  engagement   Country  
  • 19. lem  by    [  embr  ]     Case  Study     Measuring     Networked  user   engagement  
  • 20. ENGAGEMENT NETWORKS Interac%on  data   •  July  2011   •  2M  user,  25M  sessions   •  728  Yahoo!  sites  (news,  mails,  search,  etc.)     Networks   •  12  weighted  directed  networks,  filtered  by   »  Edge  types    (internal,  internal  +  returning)   »  Time    (Wednesday,  Sunday)   »  User  loyalty    (causal,  acJve,  VIP)   »  Country    (5  EU  countries)     •  Applying  metrics  on  networks,  results  are   normalized  using  the  z-­‐score   20  Networked  User  Engagement  JaneHe  Lehmann   external   returning  edge   z-­‐score   trafficVolume Country1 Country3 Country4 Country2 Country5 average above average below average
  • 21. Network-LEVEL METRICS Edge-­‐based  networks                 Leaving  the  network  does  not  necessarily  entail  less  engagement   •  Traffic  increases  significant  when  accounJng  for  returning  traffic  [18.26%]   •  ConnecJvity  increases  à  User  use  new  navigaJon  paths   •  Modularity  increases  à  Users  stay  in  the  same  part  of  the  network     21  Networked  User  Engagement  JaneHe  Lehmann   trafficVolume density reciprocity subNWmod GCC Size Connectivity Modularity Edgeint Edgeint+ext Timewed Timesun Usercasual Usernormal UserVIP
  • 22. Network-LEVEL METRICS Time-­‐based  networks                 Naviga%on  is  more  goal-­‐oriented  during  the  week   •  TrafficVolume  and  density  are  lower  on  Sunday  à  Less  acJvity   •  Reciprocity,  subNW,  and  GCC  are  higher  on  Sunday  à  User  return  more  omen  to  already   visited  sites  and  visit  more  sites  than  usual  (browsing  with  less  specific  goals)     22  Networked  User  Engagement  JaneHe  Lehmann   trafficVolume density reciprocity subNWmod GCC Size Connectivity Modularity Edgeint Edgeint+ext Timewed Timesun Usercasual Usernormal UserVIP
  • 23. Network-LEVEL METRICS User-­‐based  networks                 Loyal  users  have  also  a  higher  networked  user  engagement   •  TrafficVolume  is  higher  for  VIP  user  à  More  acJvity   •  ConnecJvity  and  modularity  is  higher  for  VIP  user  à  Users  are  interested  in  more  sites   •  GCC  is  lower  for  causal  users  à  Users  access  sites  using  the  front  pages     23  Networked  User  Engagement  JaneHe  Lehmann   trafficVolume density reciprocity subNWmod GCC Size Connectivity Modularity Edgeint Edgeint+ext Timewed Timesun Usercasual Usernormal UserVIP
  • 24. trafficVolume density reciprocity subNWmod GCC Size Connectivity Modularity Country1 Country3 Country4 Country2 Country5 Network-LEVEL METRICS Country-­‐based  networks                 Networked  user  engagement  differs  between  countries   •  Country1:  Highest  trafficVolume  +  GCC,  high  density  +  reciprocity,  lowest  subNWmod  à  high   engagement   •  Country5:  Low  trafficVolume,  but  high  density    +  GCC,  low  subNWmod  à  less  popular,  but  high   engagement   24  Networked  User  Engagement  JaneHe  Lehmann  
  • 25. CONCLUSION AND FUTURE WORK •  Network  analysis  enhances  the  understanding  of  user  engagement.   It  allows:   •  To  analyze  users  browsing  behavior  on  a  global  scale   •  To  compare  networks  of  sites  (e.g.  of  different  countries)   •  To  observe  differences  over  Jme   •  Leaving  the  network  does  not  entail  less  engagement   •  Some  network  metrics  are  more  useful  than  others   Future  work:   •  DefiniJon  of  metrics  that  combine  network  traffic  and  engagement   •  Comparing  traffic-­‐  and  hyperlink-­‐networks   •  Studying  the  effect  of  changes  in  the  network   25  Networked  User  Engagement  JaneHe  Lehmann  
  • 26. Janebe  Lehmann   Universitat  Pompeu  Fabra,  Spain   lehmannj@acm.org     Mounia  Lalmas   Yahoo  Labs  London   mounia@acm.org     Ricardo  Baeza-­‐Yates   Yahoo  Labs  Barcelona   rbaeza@acm.org     Elad  Yom-­‐Tov   Microsof  Research,  Herzliya,  Israel   elad@ieee.org