Seminars of Software and Services for the Information Society. Social Networks A case study

Transcription

1 DIPARTIMENTO DI INGEGNERIA INFORMATICA AUTOMATICA E GESTIONALE ANTONIO RUBERTI Master of Science in Engineering in Computer Science (MSE-CS) Seminars in Software and Services for the Information Society Social Networks A case study 1

2 A case study: social networking in TLC node: arc: subject of interest frequency / volume of communication in a given time window weighted arc: measured value unweighted arc: the threshold is exceeded problems: goal: quantitative thresholds dynamic over time identification of a community and roles within the community 2

3 Analysis of telephone traffic (India, 53 mlnusers) Call graph: G call SMS graph: G sms in nucleus out 3

4 Social Networks in telephone traffic fixed network: properties less focused, because the phone is used by several people mobile traffic: more distinct properties suitable for a social network interpretation G call : Call Graph G sms : SMS Graph G sms is more social than G call 4

5 Call Graph: G call degree distributions g in, g out follow Power Law g in similar to G int, but g out different from G int and G mail diameter < 13 Small World Phenomenon page-rank: p(i) = q/n + (1 q) [j:j i] p(j)/d out (j) dimension of SCC: 1 giant component, then Power Law 5

6 Some results on G call Bow tie nodes % of total IN 8.5 nucleus (SCC) 63 OUT 18.7 TENDRIL, TUBE and DISC 9.8 in nucleus out 6

7 Some results on G sms G sms about half number of nodes of G call about one third of arcs of G call reciprocity (G call )= 30% reciprocity(g sms )= 60% distribution of cliques: very different between G call and G sms 7

8 Where to start from... 8

9 Property (from extremal graph theory) Each dense component (50%) has very often (90%) a 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) b. expand each nucleus as much as possible, by finding nodes connected to all (or most of) the nodes in the nucleus 9

10 Analysis of a social network A. Identification of basic communities (clusters) identification of complete subgraphsof small cardinality (NUCLEUS ) progressive expansion of the nuclei, to embrace the community B. Identification of social patterns sequential analysis of communication events within the community 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 10

11 Communities and churning a sequential analysis 11

12 Communities and churning a sequential analysis 12

13 Communities and churning a sequential analysis 13

14 Communities and churning a sequential analysis 14

15 Communities and churning a sequential analysis stable contended open contest 15

16 Procedure Control information for verification historical analysis of churning: MNP + CDR 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 Possible improvements may come from exogenous kills (e.g., a psychologist) 16

17 Some examples 17

18 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 stealth marketing: hidden marketing (unfair) astroturfing: hidden marketing simulating a spontaneous positive opinion (unfair) 18

19 Viral Marketing 1. there is an offer of products and / or services (those not covered by the business) 2. common motivations and behaviors can be exploited 3. there is an easy way to communicate to others the message 4. existing forms of communication can be used 5. there exists a scalable solution 6. others' resources are available 19

20 Viral Marketing: a masterpiece Tohato(Japan) launches two types of chips Habanero(hot like the fire of hell) Satan Jorquia(spicy like a fatal bazooka) 20