Complex Network Analysis in Corporate Social Networking. Improving Performance through Collective Intelligence

Transcription

1 Complex Network Analysis in Corporate Social Networking Improving Performance through Collective Intelligence

2 David Easley and Jon Kleinberg Networks, Crowds and Markets Reasoning about a Highly Connected World Cambridge University Press Aggarwal, Charu C. (Ed.) 2011, XIV, 502p. Social Network Data Analytics Springer 2

3 Sistemi ICT Mondo sociale /organizzativo/economico Life sciences "La filosofia è scritta in questo grandissimo libro che continuamente ci sta aperto innanzi a gli occhi [...] scritto in lingua matematica e i caratteri son triangoli, cerchi ed altre figure geometriche [...]" 3

4 Network science: structure, contents & dynamics Interacting agents & game theory Diffusion of Innovation Open Innovation & Crowd sourcing Hong, L., and Page, S.E. (2004), Groups of diverse problem solvers can outperform groups of high-ability problem solvers, Proceedings of National Academy of Science of the United States of America, 101(46): Diversity trumps ability People with different backgrounds can come up with smarter solutions 4

5 Massive Online Open Course 5

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7 Social Network Approach for Energy consumption monitoring (human sensors) 7

8 Network science Linkage-based structural analysis Content-based analysis Dynamics vs Static Privacy Visualization Integrating sensors and Social Networks 8

9 Network science Statistical Analysis Ranking, centrality, betweenness Community Detection Evolution Influence Text Mining & Data Mining Big Data Analytics The size of the network and the scalability requirements, require new computational concepts and technologies, largely based on statistical sampling «You can harness randomness to produce better solutions to complex problems» 9

10 Interacting agents & game theory Prisoner s dilemma to induce co-operation Networked coordination games in which two nodes can choose between behaviour A and B and have an incentive to match their behaviour The pay-off values influence the adoption threshold Groups with a strong internal homogeneity (clusters/communities) are obstacles to innovation cascades From winners take all to givers take all culture: Collaborative Intelligence: using teams to solve hard problems The single strongest predictor of a group effectiveness was the amount of help the analystis gave to each other Adam Grant, Give and Take: A Revolutionary Approach to Success (Viking, April 2013) 10

11 Innovation diffusion Pharma calls plan, opinion leaders, how ties evolve and optimization The impact of social contagion on the drug prescription made by the physicians has attracted the attention of several companies. General Practitioners tend to consult strongly credible peers within their professional community. These opinion leaders have a prevalent influence on their opinion seekers, even if they could be also influenced by these latter. Pharmaceutical companies should be very interested in the detection of the opinion leaders, because the calls directed to them may have a social multiplier effect, thanks to their influence on the other physicians 11

12 Open Data The City of Chicago's Data Portal is dedicated to promoting access to government data and encouraging the development of creative tools to engage and serve Chicago's diverse community. The site hosts over 200 datasets presented in easy-to-use formats about City departments, services, facilities and performance. Open City ( Civic apps built with open data. Open City is a group of volunteers that create apps with open data to improve transparency and citizen understanding of our government. All of our projects are open source and free to use under the MIT license. Examples: Chicago traffic Tracker (realtime traffic information) Using a Chicago bus as a data tool Civic Data Apps MetroChicagoData.org brings interactive information and data from the City of Chicago, Cook County and the State of Illinois into one single, citizen-friendly interface. Search for keywords or browse a unified set of topics and categories. 12

13 Water Analytics: From the physic network Anytown (benchmark) H2OLeak (Torbole-Casaglia) ICeWater (Timisoara pilot) ICeWater (Abbiategrasso, Milan pilot) Analyse for: Resilience leak detection 13

14 Water Analytics: to the Analytical Network Anytown (benchmark) H2OLeak (Torbole-Casaglia) ICeWater (Timisoara pilot) ICeWater (Abbiategrasso, Milan pilot) Generate simulated leakage scenarios 14

15 From Sensors Space to a Scenarios Network Spectral clustering to enrich analysis with the relationship between scenarios Physical Space Sensors Space Scenarios Network Space Data Affinity Graph Laplacian, Spectrum & Eigenvalues Traditional Clustering Post Processing Clustering Evaluation 2 nd smallest eigenvectors ( bi-partitioning ) m smallest eigenvectors ( k-means in R m ) Traditional Clustering Spectral Clustering 15

16 Corporate Social Networking: Knowledge On the Network KON-Social Social Network Aziendale in cui i nodi sono individui la cui attività rileva a quella dell azienda. I nodi sono caratterizzati secondo alcune informazioni personali, il ruolo all interno dell azienda ed una serie di attributi che caratterizzano le competenze e le esperienze specifiche Gli archi sono le relazioni che si instaurano tra gli individui, eventualmente pesati sulla base della «frequenza» o della «freschezza» delle connessioni Possibili analisi: Community Discovery (Spectral Clustering, Markov Clustering) Dynamic Network analysis (Community Discovery & Tracking) Influence Analysis (statistical properties: in-degree for questions & out-degree for answers [weighted by vote/ranking]) Influence Analysis (Diffusion Model: Topic Affinity Propagation vs Actions Propagation) Experts identification and tracking (statistical properties + TAP + dynamic networks) 16

17 Network science 17