Seminars in Software and Services for the Information Society - - PowerPoint PPT Presentation

Master of Science in Engineering in Computer Science (MSE-CS)

DIPARTIMENTO DI INGEGNERIA INFORMATICA AUTOMATICA E GESTIONALE ANTONIO RUBERTI

(MSE-CS)

Seminars in Software and Services for the Information Society

Umberto Nanni

1 Seminars of Software and Services for the Information Society Umberto Nanni

Social Networks – A case study

A case study: social networking in TLC

node: subject of interest arc: frequency / volume of communication in a given time communication in a given time window weighted arc: measured value unweighted arc: the threshold is exceeded problems: quantitative thresholds dynamic over time

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dynamic over time goal: identification of a “community” and roles within the community

Analysis of telephone traffic (India, 53 mln users)

• Call graph: Gcall
• SMS graph: G
• SMS graph: Gsms

in

• ut

nucleus

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Social Networks in telephone traffic

• fixed network: properties less focused,

because the phone is used by several people because the phone is used by several people

• mobile traffic: more distinct properties –

suitable for a social network interpretation Gcall : Call Graph

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Gcall : Call Graph Gsms : SMS Graph Gsms is more “social” than Gcall

Call Graph: Gcall

• degree distributions gin, gout follow Power Law
• gin similar to Gint, but gout different from Gint
• gin similar to Gint, but gout different from Gint

and Gmail

• diameter < 13 → Small World Phenomenon
• page-rank: p(i) = q/N + (1 − q) ∑[j:j→i]

p(j)/dout(j)

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p(j)/dout(j)

• dimension of SCC: 1 giant component, then

Power Law

Some results on Gcall

Bow tie nodes % of total

• IN

8.5

• nucleus (SCC)

63

• OUT

18.7

• TENDRIL, TUBE and DISC

9.8

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in

• ut

nucleus

Some results on Gsms

• Gsms
• about half number of nodes of Gcall
• about half number of nodes of Gcall
• about one third of arcs of Gcall
• reciprocity (Gcall)= 30%
• reciprocity(Gsms)= 60%

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sms

• distribution of cliques: very different between Gcall

and Gsms

Where to start from ...

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Property (from extremal graph theory)

Each dense component (50%) has very often (90%) a complete nucleus of small dimension (3 or 4). complete nucleus of small dimension (3 or 4).

A possible algorithmic approach (a heuristics, since the problem is untractable, in general):

• a. find for nucleus’: small complete connected components of

small size (3 o 4)

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small size (3 o 4)

• b. expand each nucleus as much as possible, by finding nodes

connected to all (or most of) the nodes in the nucleus

Analysis of a social network

• A. Identification of basic communities (clusters)

– identification of complete subgraphs of small cardinality (NUCLEUS’) (NUCLEUS’) – progressive expansion of the nuclei, to embrace the community

• B. Identification of social patterns

– sequential analysis of communication events within the community

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• C. Identification of roles within the community

– role in the events of communication and sequences

• D. Identification of community related

– classification of communities according to the social patterns

Communities and churning – a sequential analysis

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Communities and churning – a sequential analysis

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Communities and churning – a sequential analysis

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Communities and churning – a sequential analysis

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Communities and churning – a sequential analysis

stable contended

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• pen contest

Procedure

Control information for verification

• historical analysis of churning: MNP + CDR

identification of communities identification of communities sequence analysis of events within the community individuating roles Dynamic behavior of the Network

• automatic activation of the recalculation when the

traffic graph shows deviation from the available

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• automatic activation of the recalculation when the

traffic graph shows deviation from the available Possible improvements may come from exogenous kills (e.g., a psychologist)

Some examples

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Social Network and marketing – miniglossary

• social marketing: marketing for social interest
• word of mouth: (it: passaparola)
• influencer marketing: based on influential people
• engagement marketing: involvement of customer

in marketing

• viral marketing: exploitation of a social network

for disseminating a brand or a product

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for disseminating a brand or a product

• stealth marketing: hidden marketing (unfair)
• astroturfing: hidden marketing simulating a

spontaneous positive opinion (unfair)

Viral Marketing

• 1. there is an offer of products and / or

services (those not covered by the business) business)

• 2. common motivations and behaviors can

be exploited

• 3. there is an easy way to communicate to
• thers the message
• 4. existing forms of communication can be

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• 4. existing forms of communication can be

used

• 5. there exists a scalable solution
• 6. others' resources are available

Viral Marketing: a masterpiece

Tohato (Japan) launches two types of chips

• Habanero (hot like the fire of hell)
• Habanero (hot like the fire of hell)
• Satan Jorquia (spicy like a fatal bazooka)

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