Social Network Analysis

1
Social Network Analysis Network Optimization
_at_ Dept. of Computer Communication Engineering,
University of Thessaly _at_ Dept. of Informatics,
Aristotle University

• Dimitrios Katsaros, Ph.D.

Koblenz, February 18th, 2008
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Outline of the talk

• A few words about my research
• My latest results Social Network Analysis for
  Network Optimization
• Web (2nd round review _at_ IEEE Transactions on
  Knowledge Data Engineering)
• PRIMITIVE Community Identification
• PROTOCOL Content Outsourcing
• GOAL Latency Reduction
• Wireless Multimedia Sensor Nets (2nd round review
  _at_ ACM Mobile Networks Applications)
• PRIMITIVE Important Sensor Nodes Identification
• PROTOCOL Cooperative Caching
• GOAL Latency Reduction
• Why Collective Intelligence?

3
My Research Areas (chronological info)

• WIRELESS NETWORKS
• Mobile Pervasive Computing
• Data Management
• Caching (04)
• Air-Indexing (07)
• Data Dissemination
• Broadcast Scheduling (04)
• Prediction
• Mobility Prediction (0308)
• Prefetching (03)
• Mobile Ad Hoc Networks
• Content-based Multimedia Retrieval (0508)
• Broadcasting (0608)
• Wireless Sensor Networks
• Sensor Network Clustering (07)
• (DistrLocal) Data Indexing (0608)
• Cooperative Caching (0708)
• Data Dissemination (08)

• WIRED NETWORKS
• Conventional and Streaming Media Distribution in
  the Web
• Replication (03)
• Prefetching (010203)
• Caching (04)
• Overlay and P2P Networks
• Content Distribution Networks (0506)
• Content Placement in CDNs (0708)
• Indexing Query Routing in P2P (progress)
• Distributed Structures over P2P (progress)
• Web Information Retrieval and Data Mining
• Web Link Mining (05)
• Web Ranking (0708)
• Rank Aggregation (0708)
• Social Network Analysis (0708)
• Bibliometrics (060708)

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Research areas Ultimately ? ???
Mobile/Pervasive Computing
Web
Pervasive Web
Overlay Nets
Caching Air-Indexing
Peer-to-Peer Networks
Caching Prefetching Replication
Semistructured Data
Web views
Webcasting
Content Distribution Networks
Location Tracking
Ad Hoc
Content-Based MIR
Broadcasting Data Dissemination
Web Ranking Search Engines
Cooperative Caching Sensor Node Clustering
Distributed Indexing Coverage/Connectivity
Flash storage
Social Network Analysis
Information Retrieval
Sensors
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Social Network Analysis

• A social network is a social structure to
  describe social relations (wikipedia)
• The history of Social Network is older than
  everybody who is here
• More than 100 years (Cooley 1909, Durkheim 1893)
• Focusing on small groups
• Information Techniques give it a new life
• book Stanley Wasserman Katherine Faust
• Mathematical Representation
• Structural Locational Properties
• Roles Positions
• Dyadic Triadic Methods

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Social Network Analysis

• Stanley Wasserman Katherine Faust
• Mathematical Representation
• Structural Locational Properties
• Centrality
• Betweenness Centrality
• Roles Positions
• Dyadic Triadic Methods

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Betweenness Centrality

• Let suw swu denote the number of shortest paths
  from u ? V to w ? V (by definition, suu 0)
• Let suw(v) denote the number of shortest paths
  from u to w that some vertex v ? V lies on
• The Betweenness Centrality index NI(v) of a
  vertex v is defined as
• Large values for the NI index of a node v
  indicate that this node can reach others on
  relatively short paths, or that v lies on
  considerable fractions of shortest paths
  connecting others

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The NI index in sample graphs
In parenthesis, the NI index of the respective
node i.e., 7(156) node with ID 7 has NI equal
to 156.

• Nodes with large NI
• Articulation nodes (in bridges), e.g., 3, 4, 7,
  16, 18
• With large fanout, e.g., 14, 8, U
• Therefore geodesic nodes

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The NI index in a localized algorithm

• For any node v, the NI indexes of the nodes in
  N12(v) calculated only for the subgraph of the
  2-hop (in general, k-hop) neighborhood reveal the
  relative importance of the nodes in N12
• For a node u (of the 2-hop neighbourhood of a
  node v), the NI index of u will be denoted as
  NIv(u)

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Betweenness Centrality in

• WEB Performing graph clustering and recognizing
  communities in Web site graphs
• WIRELESS MULTIMEDIA SENSOR NETWORKS Recognizing
  (in a distributed fashion) important sensor
  nodes, the mediators, that coordinate cooperative
  caching decisions

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Community Identification Content Outsourcing
for the Web
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The need for content outsourcing
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CiBC Method

• Target is true
• CiBC method
• Building cliques and clusters around
  representative (pole) nodes (with low CB)
• Earlier methods have
• Defined hard communities ?node
  deg(inCom)gtdeg(outCom)
• exploited edge betweenness to perform
  hierarchical agglomerative clustering

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CiBC Method
Phase 1 NI Computation -O(nm) Phase 2
Initialization of cliques O(n)
ID NI index
10 20.68
2 19.61
6 11.38
1 10.28
7 2.06
0 1.73
9 0.99
8 0.99
4 0.75
5 0.00
11 0.00
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CiBC Method
Phase 2 Initialization of cliques O(n)
ID NI index
10 20.68
2 19.61
6 11.38
1 10.28
7 2.06
0 1.73
9 0.99
8 0.99
4 0.75
5 0.00
11 0.00
16
CiBC Method
Phase 2 Initialization of cliques O(n)
ID NI index
10 20.68
2 19.61
6 11.38
1 10.28
7 2.06
0 1.73
9 0.99
8 0.99
4 0.75
5 0.00
11 0.00
17
CiBC Method
Phase 2 Initialization of cliques O(n)
ID NI index
10 20.68
2 19.61
6 11.38
1 10.28
7 2.06
0 1.73
9 0.99
8 0.99
4 0.75
5 0.00
11 0.00
18
CiBC Method
Phase 2 Initialization of cliques O(n)
ID NI index
10 20.68
2 19.61
6 11.38
1 10.28
7 2.06
0 1.73
9 0.99
8 0.99
4 0.75
5 0.00
11 0.00
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CiBC Method
A B C D
A 3 3 0 0
B 3 3 1 1
C 0 1 3 4
D 0 1 4 3
A
Phase 3 Clique Merging Creation of
Communities
B
Complexity O(l2) l is the number of cliques
C
D
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CiBC Method
A B C D
A 3 3 0 0
B 3 3 1 1
C 0 1 3 4
D 0 1 4 3
A
Phase 3 Clique Merging Creation of
Communities
B
4 3
C
D
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CiBC Method
A B C
A 3 3 0
B 3 3 2
C 0 2 10
A
Phase 3 Clique Merging Creation of
Communities
B
C
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CiBC Method
A B C
A 3 3 0
B 3 3 2
C 0 2 10
A
Phase 3 Clique Merging Creation of
Communities
B
C
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CiBC Method
A C
A 9 2
C 2 10
A
Phase 3 Clique Merging Creation of
Communities
Phase 4 Check constraints
C
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CiBC vs. Clique Percolation Method, LRU
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Cooperative Caching in Wireless Multimedia Sensor
Networks
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The NICoCa protocol

• Each node is aware of its 2-hop neighborhood
• Uses NI to characterize some neighbors as
  mediators
• A node can be either a mediator or an ordinary
  node
• Each sensor node stores
• the dataID, and the actual multimedia datum
• the data size, TTL interval
• for each cached item, the timestamps of the K
  most recent accesses
• each cached item is characterized either as O
  (i.e., own) or H (i.e., hosted)

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The cache discovery protocol (1/2)

• A sensor node issues a request for a multimedia
  item
• Searches its local cache and if it is found
  (local cache hit) then the K most recent access
  timestamps are updated
• Otherwise (local cache miss), the request is
  broadcasted and received by the mediators
• These check the 2-hop neighbors of the requesting
  node whether they cache the datum (proximity hit)
• If none of them responds (proximity cache miss),
  then the request is directed to the Data Center

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The cache discovery protocol (2/2)

• When a mediator receives a request, searches its
  cache
• If it deduces that the request can be satisfied
  by a neighboring node (remote cache hit),
  forwards the request to the neighboring node with
  the largest residual energy
• If the request can not be satisfied by this
  mediator node, then it does not forward it
  recursively to its own mediators, since this will
  be done by the routing protocol, e.g., AODV
• If none of the nodes can help, then requested
  datum is served by the Data Center (global hit )

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Cache vs. hits (MB files uniform access) in a
sparse WMSN (d 4)
HYBRID appears at L. Yin and G. Cao,
Supporting cooperative caching in ad hoc
networks, IEEE Transactions on Mobile Computing,
5(1)77-89, 2006
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Cache vs. hits (MB files uniform access) in a
dense WMSN (d 7)
HYBRID appears at L. Yin and G. Cao,
Supporting cooperative caching in ad hoc
networks, IEEE Transactions on Mobile Computing,
5(1)77-89, 2006
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Evolution of cyberspace

• Semantic Web Pervasive Computing
• WWW Broadband WIFI grid computing
• Unicode XML RDF Ontologies
• Internet Multimedia URL HTTP HTML
• Servers Telecom Networks PCs TCP-IP
  e-mail FTP
• Computers Micro-chips Application Software
  WYSIWYG Interfaces

Collective Intelligence Net
Semantic Web
WWW
Internet
PC
Computer
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Why Collective Intelligence?

• Users/ devices generate data at an unprecedented
  rate
• Blogs
• Tags
• Sensor measurements
• Web pages
• Rankings by search engines
• They are opinions or votes
• Under some conditions group IQ gt individual IQ
• So far Opinion/Vote fusion
• PageRank (i.e., collective linking preferences)
• Metasearching (ranked list merging)
• Collaborative filtering (what is interesting from
  what other people say, what people like you say)
• ..

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Collective Intelligence challenges

• Statistical analysis of social networks
• Identification of influential opinions and/or
  producers (e.g., bloggers)
• Discover social context to provide
  personalization searching
• Opinion spam
• Bias filtering

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Collective Intelligence challenges

• Finding high-quality content
• Opinion mining
• Dealing with controversies (e.g., in Wikipedia)
• Metadata from data analysis
• Storage of metadata
• MOST IMPORTANTLY
• In Centralized and/or Distributed settings

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Thank you for your attention!

• Questions?

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References

• Our work
• D. Katsaros?, G. Pallis, K. Stamos, A.
  Sidiropoulos, A. Vakali, Y. Manolopoulos. CDNs
  Content Outsourcing via Generalized Communities.
  IEEE Transactions on Knowledge and Data
  Engineering, (under second round review),
  December, 2007.
• N. Dimokas, D. Katsaros, and Y. Manolopoulos,
  Cooperative Caching in Wireless Multimedia
  Sensor Networks ACM Mobile Networks and
  Applications, (under second round review),
  February, 2008.
• Competing methods
• CPM community identification method G. Palla,
  I.Derenyi, I.Farkas, and T.Vicsek. Uncovering the
  overlapping community structure of complex
  networks in nature and society. Nature,
  435(7043)814818, 2005.
• Hybrid cooperative caching method L. Yin and G.
  Cao. Supporting cooperative caching in ad hoc
  networks. IEEE Transactions on Mobile Computing,
  5(1)7789, 2006.