Social Networks - Where Are the Connections
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This document discusses social networks and how analyzing connections within and between groups can provide useful information. It provides an example of mapping connections between different communities within a company to identify which people and groups are well-connected versus isolated. The analysis can help identify key individuals that connect different groups and increase social capital by facilitating more interaction between isolated communities. The document concludes that networks represent the connections between people, technology enables modeling, analysis and visualization of these networks, but human interpretation is still needed.
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Social Networks - Where Are the Connections
- 1. Social Networks ‘Where’ are the connections?
- 2. Why are we at wherecamp?
- 3. You could just follow wherecamp on Ustream/Twitter/Blogs/IRC
- 6. Who should I network with?
- 7. Who can introduce me to who?
- 8. What are the cliques?
- 9. Who should I introduce to who?
- 11. It is all about mining the data...
- 12. An Example ‘Social Capital’ in a company
- 13. Networks are pervasive Also in your company
- 14. Another net view of your company Vaccine XX ppl Cell Biologists DNA ppl
- 15. What does it tell us? These ppl don’t interact with each other Those ppl are the real company core This is a very strong duo, albeit peripheral
- 16. Who connects these communities? Let us zoom and expand this group!
- 17. Inside a community Links toward other communities Links toward other communities These guys are the mediators!
- 18. Looking for valuable information Links toward other communities Links toward other communities Hey! That guys seems pretty well-connected!
- 19. Axon solution with your company data … All your data here Axon rankings clusters and indicators here
- 20. Analyzing data across clusters e.g. Density of team leaders with a PhD from Harvard Makes sense: these topics are already in a close community
- 21. Social capital and catalysts Groups with high social capital = those with many intra-group relations and also good inter-group connections These two have lower social capital scores
- 22. Social capital and catalysts Isolate the catalysts: people from group A and B to be put together to increase the overall communication
- 23. Social capital and catalysts Zoom in group #1 Choose local “gurus” for catalyzing the new inter-group exchange Zoom in group #2
- 24. Conclusion... Networks = the where of connections Tech = modelling, analysis and presentation Interpretation = human
Notes de l'éditeur
- Prev slide = picture of actual GSK research centers connected by pseudo volumes of joint research efforts
- Prev slide: another “axon” view of your company: employee/researchers are clusted according to their topic. The strength between the clusters are the number of projects / patents / collaborations / conferences / whatever these groups have in commonNote: small blue arrows = just to point the label at a cluster
- Prev slide: another “axon” view of your company: employee/researchers are clusted according to their topic. The strength between the clusters are the number of projects / patents / collaborations / conferences / whatever these groups have in commonNote: small blue arrows = just to point the label at a cluster
- Prev slide: zoom inside a community (the big blue blob)Only 5 or 6 persons connect the community to other communities (they are at the periphery of the blob)Axon indicators -> we can rank their local and global influence (see the size and color)
- Prev slide: zoom inside a community (the big blue blob)Only 5 or 6 persons connect the community to other communities (they are at the periphery of the blob)Axon indicators -> we can rank their local and global influence (see the size and color)
- Prev slide: we can zoom on any actor (employee/researcher but also project or center) and seeThe meta-data provided by the company as in a regular table browserThe axon indicators/rankings computed: who is influential, reachable, trends, hubs, etc.
- Prev slide: the color code = the number /density of researchers within a community (cell bio, virus XX, etc.) with a PhD from university YYY. It appears they are closely clustered together: make sense because there is a story behind: University YYY is at the forefront of research in those domains