2010.05.28 Slide 1 ManyNets Multiple Network Analysis and Visualization Catherine Plaisant Ben Shneiderman Jennifer Golbeck – [email protected]Awalin Nabila Miguel Rios Manuel Freire
2010.05.28 Slide 1 ManyNets Multiple Network Analysis and Visualization Catherine Plaisant Ben Shneiderman Jennifer Golbeck Manuel Freire-Moran – [email protected]
2010.05.28 Slide 1 ManyNets Multiple Network Analysis and
Visualization Catherine Plaisant Ben Shneiderman Jennifer Golbeck
Manuel Freire-Moran [email protected] 2010.05.18 Awalin Nabila
Miguel Rios Manuel Freire
Slide 3
2010.05.28 Slide 2 1 social network What about comparing
thousands?
2010.05.28 Slide 7 Split large networks to compare parts
Multiple criteria sort, Filter using custom expressions Tight
coupling with node-link diagrams e.g. all ego-networks FilmTrust
[Golbeck06]
2010.05.28 Slide 12 Motivation Analysis of separate networks:
compare a set of networks Analysis of parts of a single network:
divide and conquer Local neighborhoods (ego networks) within a
social network Compare larger neighborhoods (clusters or
communities) Find prevalence of certain network motifs Compare
sub-networks with certain attributes (eg.: time-slices) Analysis of
multi-modal networks Handle networks with multiple types of nodes
and edges Generate new edges (two users are connected if)
Slide 14
2010.05.28 Slide 13 separate networks example Facebook networks
from 5 US universities, from [Traud09]
Slide 15
2010.05.28 Slide 14 separate networks example Facebook networks
from 5 US universities, from [Traud09]
Slide 16
2010.05.28 Slide 15 separate networks example Facebook networks
from 5 US universities, from [Traud09]
Slide 17
2010.05.28 Slide 16 separate networks example
Slide 18
2010.05.28 Slide 17 separate networks example
Slide 19
2010.05.28 Slide 18 Motivation Analysis of separate networks:
compare a set of networks Analysis of parts of a single network:
divide and conquer Local neighborhoods (ego networks) within a
social network Compare larger neighborhoods (clusters or
communities) Find prevalence of certain network motifs Compare
sub-networks with certain attributes (e.g.: time-slices) Analysis
of multi-modal networks Handle networks with multiple types of
nodes and edges Generate new edges (two users are connected
if)
Slide 20
2010.05.28 Slide 19 single network example Nodes are users
Links are trust ratings in other users film rating expertise
JoeMary 10 PeterPaul 8 2 Mark Tim 8 9 Ed ? FilmTrust
[Golbeck06]
Slide 21
2010.05.28 Slide 20 Mark ego network radius 0
Slide 22
2010.05.28 Slide 21 ego network radius 1 Mark TimEd Joe Mary
Peter Paul
Slide 23
2010.05.28 Slide 22 ego network radius 1.5 Mark TimEd Joe Mary
Peter Paul
Slide 24
2010.05.28 Slide 23 ego network radius 2 Mark TimEd Joe Mary
Peter Paul Liz Ben Jane Beth Tom
Slide 25
2010.05.28 Slide 24 Q: are big ego nets similar to small ones?
picture of trust distribution in big ego nets (large neighborhood)
picture of trust distribution in small ego nets (small
neighborhood)
Slide 26
2010.05.28 Slide 25
Slide 27
2010.05.28 Slide 26 are big ego nets similar to small ones?
picture of trust distribution in big ego nets (large neighborhood)
picture of trust distribution in small ego nets (small
neighborhood)
Slide 28
2010.05.28 Slide 27 Motivation Analysis of separate networks:
compare a set of networks Analysis of parts of a single network:
divide and conquer Local neighborhoods (ego networks) within a
social network Compare larger neighborhoods (clusters or
communities) Find prevalence of certain network motifs Compare
sub-networks with certain attributes (eg.: time-slices) Analysis of
multi-modal networks Handle networks with multiple types of nodes
and edges Generate new edges (two users are connected if)
2010.05.28 Slide 29 Interface Support for multi-modal networks
Schemas Table levels Columns (network metrics, features) can be
removed, rearranged, added From menu Via user-specified expression
Filter and sort Details on demand in side-pane, tooltips Create new
relationships, access the overall schema
2010.05.28 Slide 35 multiple node and edge types: levels Lowest
level: entity and relationship tables Entities are stand-alone, can
be used as nodes Relationships relate two entities, map to edges
Inside a network: node and edge tables Nodes come from entities
Edges come from relationships Can mix multiple entities,
relationships in a network: multi-relational or multi-modal Network
tables Each row is a network
Slide 37
2010.05.28 Slide 36 Interface Support for multi-modal networks
Schemas Table levels Columns (network metrics, features) can be
removed, rearranged, added From menu Via user-specified expression
Filter and sort table Details on demand in side-pane, tooltips
Create new relationships, access the overall schema
Slide 38
2010.05.28 Slide 37
Slide 39
2010.05.28 Slide 38
Slide 40
2010.05.28 Slide 39 Interface Support for multi-modal networks
Schemas Table levels Columns (network metrics, features) can be
removed, rearranged, added From menu Via user-specified expression
Filter and sort Details on demand in side-pane, tooltips Advanced
column overviews Create new relationships, access the overall
schema
Slide 41
2010.05.28 Slide 40 Overviews of Distribution Columns ManyNets
Overviews [Sopan10 / under review]
Slide 42
2010.05.28 Slide 41 Overviews of Distribution Columns ManyNets
Overviews [Sopan10 / under review]
Slide 43
2010.05.28 Slide 42 Interface Support for multi-modal networks
Schemas Table levels Columns (network metrics, features) can be
removed, rearranged, added From menu Via user-specified expression
Filter and sort table Details on demand in side-pane, tooltips
Create new relationships, access the schema
Slide 44
2010.05.28 Slide 43 Deriving new relationships BobAlice Jaws
Star-wars trust = 8/10 rating = 4/5 rating = 3/5 rating = 2/5 user
film trust rating FilmTrust Schema
2010.05.28 Slide 45 Deriving new relationships Build new
relationships on the fly Extend schema with each relationship
Retain access to original data Compare resulting networks to each
other user film trust rating Co-rated Good predictor for
Slide 47
2010.05.28 Slide 46 Validation Original ManyNets (presented at
CHI 2010) Case Study on FilmTrust with domain expert Formative
usability test (7 users) ManyNets2 (work in progress) NSF grant
data Your dataset here!
Slide 48
2010.05.28 Slide 47 Conclussion Multimodal network analysis is
hard ManyNets can help! build and explore sets of networks split,
filter, rank, overview, drill, elide, synthesize Reveals patterns
within network attributes Does so interactively, allowing
exploratory search Development page (application, datasets, manual)
tangow.ii.uam.es/mn/ open-source, feedback welcome! (please contact
us) Academic page (publications, demo videos)
www.cs.umd.edu/projects/hcil/manynets/ Acknowledgements Partial
support from Lockheed Martin Manuel Freire supported by Fulbright
Scholarship