social network analysis & network optimization

Social Network Analysis &

Network Optimization

Dimitrios Katsaros, Ph.D.Koblenz, February 18th, 2008

@ Dept. of Computer & Communication Engineering, University of Thessaly@ Dept. of Informatics, Aristotle University

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Outline of the talk

•A summary of my research•Latest results: “Social Network Analysis for

Network Optimization”•Web (2nd round review @ IEEE Transactions on Knowledge & Data Engineering)

•PRIMITIVE: Community Identification•PROTOCOL: Content Outsourcing•GOAL: Latency Reduction

•Wireless Multimedia Sensor Nets (2nd round review @ ACM Mobile Networks & Applications)

•PRIMITIVE: “Important” Sensor Nodes Identification•PROTOCOL: Cooperative Caching•GOAL: Latency Reduction

•Collective Intelligence: Latest step of cyberspace

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Research areas: Ultimately ???

Overlay NetsMobile/Pervasive Computing

Sensors

Ad

Hoc

Information Retrieval

Web

Location Tracking

Caching &

Air-Indexing Peer-to-Peer NetworksContent

Distribution

Networks

Caching &

Prefetching &

Replication &

Semistructured Data &

Web views

Web Ranking &

Search Engines

Social Network Analysis

Cooperative Caching &Sensor Node Clustering &

Distributed Indexing &Coverage/Connectivity &

Flash storage &

Content-Based MIR

Broadcasting &Data Dissemination

Web

cast

ing

INTELLIG

ENCE

Pervasive

Web

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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]

1. Mathematical Representation2. Structural & Locational Properties3. Roles & Positions4. Dyadic & Triadic Methods

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Social Network Analysis

[Stanley Wasserman & Katherine Faust]

1. Mathematical Representation2. Structural & Locational Properties

1. Centrality1. Betweenness Centrality

3. Roles & Positions4. Dyadic & Triadic Methods

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Betweenness Centrality

• Let σuw= σwu denote the number of shortest paths

from u V to w V (by definition, σuu= 0)

• Let σuw(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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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

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)>deg(outCom)• exploited “edge betweenness” to perform hierarchical

agglomerative clustering

sCd

Cd

in

out )(

)(

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CiBC Method

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 0.000

12

3 4

5

6

7

8

10

9

11

Phase 1: NI Computation -O(nm)

Phase 2: Initialization of cliques

O(n)

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CiBC Method

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 0.000

12

3 4

5

6

7

8

10

9

11

Phase 2: Initialization of cliques

O(n)

16

CiBC Method

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 0.000

12

3 4

5

6

7

8

10

9

11

Phase 2: Initialization of cliques

O(n)

17

CiBC Method

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 0.000

12

3 4

5

6

7

8

10

9

11

Phase 2: Initialization of cliques

O(n)

18

CiBC Method

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 0.000

12

3 4

5

6

7

8

10

9

11

Phase 2: Initialization of cliques

O(n)

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CiBC Method

A

B

A B C D

A 3 3 0 0

B 3 3 1 1

C 0 1 3 4

D 0 1 4 3

0

12

3 4

5

6

7

8

10

9

11C D

Phase 3: Clique Merging &

Creation of Communities

Complexity: O(l2)l is the number of cliques

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CiBC Method

A

B

A B C D

A 3 3 0 0

B 3 3 1 1

C 0 1 3 4

D 0 1 4 3

0

12

3 4

5

6

7

8

10

9

11C D

Phase 3: Clique Merging &

Creation of Communities

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21

CiBC Method

A

B

A B C

A 3 3 0

B 3 3 2

C 0 2 10

0

12

3 4

5

6

7

8

10

9

11C

Phase 3: Clique Merging &

Creation of Communities

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CiBC Method

A

B

A B C

A 3 3 0

B 3 3 2

C 0 2 10

0

12

3 4

5

6

7

8

10

9

11C

Phase 3: Clique Merging &

Creation of Communities

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CiBC Method

A

A C

A 9 2

C 2 10

0

12

3 4

5

6

7

8

10

9

11C

Phase 3: Clique Merging &

Creation of Communities

Phase 4: Check constraints

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CiBC vs. Clique Percolation Method, LRU

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 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 computingUnicode + XML + RDF + Ontologies

Internet + Multimedia + URL + HTTP + HTML

Servers + Telecom Networks + PCs + TCP-IP + e-mail + FTP

Computers + Micro-chips + Application Software + WYSIWYG Interfaces

Transistors+Formal Logic+Digital Coding+ Program. Languages

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 could be treated as “opinions” or “votes”• Under some conditions: group IQ > 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: Some challenges

• Statistical analysis of social networks• Identification of influential opinions

and/or producers• Discover social context to provide

personalization• Opinion spam• Bias filtering

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Collective Intelligence: Some challenges

• Finding high-quality content• Opinion mining• Dealing with controversies• Metadata from data analysis• Storage of metadata• …………….

MOST IMPORTANTLY• In Centralized and/or Distributed

settings

Thank you for your attention!

Questions?

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ReferencesOur 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):814–818, 2005.

• [Hybrid cooperative caching method] L. Yin and G. Cao. Supporting cooperative caching in ad hoc networks. IEEE Transactions on Mobile Computing, 5(1):77–89, 2006.