  Purpose of our work is to obtain a basic understanding and comparison of the
performance and structure of real Knowledge Networks, to identify strengths and
weaknesses and to highlight guidelines for improvements. We selected 18
Knowledge Networks from the service sector and 12 networks from the production
sector and estimated their Performance and Structure in terms of 19 indices
from graph theory. Highlights from our work include: 1) As most networks are
unilaterally structured, the direction of knowledge transfer should be taken
into account as illustrated in the analysis of clubs and entropy, 2) The
stability of most Knowledge Networks is questionable, 3) Few networks are
effective in sharing information, while most Knowledge Networks cannot benefit
from the network effect, have rather limited capability for coordination,
information propagation and synchronization and are not able to integrate Tacit
knowledge, 4) Few networks have large cliques which have to be managed with
caution as their role may be highly constructive or destructive, 5) While
agents with rich connections form clubs, as in most social networks, the poor
club effect is not negligible when we take into account the link direction, 6)
The directed link analysis of entropy reveals the low
complexity-diversification of the Knowledge Networks. In fact the only high
entropy network found, has been improved by Knowledge Management Professionals.
As most Knowledge Networks underperform, there is plenty of room for further
customized analysis in order to improve communication efficiency, coordination,
Tacit knowledge dissemination and robustness. This is the first comparative
study of real Knowledge Networks in terms of graph theoretic methods.
