A general model of a socio-semantic network is presented in terms of state-transition systems. We provide some examples and indicate research directions, which seem to us the most important from the application point of view. { V represents members of the network, { E1; : : : ; Ek V V denote di erent relations between the members, e.g. being a friend, follower, relative, co-worker etc. { : V ! is a user pro le function, which stores personal information about the network members. { i : Ei ! i (i 2 f1; : : : ; kg) keeps parameters and details of the corresponding relation.
1.1 1.2
where The model of the content has a very similar de nition. It is a multi-graph
C = fT; R1; : : : ; Rm; ; 1; : : : ; mg; { T stands for the set of all elements of the generated content, e.g. posts, comments, evaluations, tags etc. { R1; : : : ; Rm T T denote di erent relations on the content, e.g. being a reply on, have the same subject, etc. { : T ! stores parameters of the content; { i : Ri ! i (i 2 f1; : : : ; kg), similarly, keeps parameters and details of the corresponding relation. 1.3
The basic connections between the social graph and the content are de ned by the authorship relation A,
A
V
T: One can also consider other kinds of connections of this kind, but usually all of them could be modeled via introducing a new type of content. For example, the relation John is interested in post \Announcement" could be modeled by introducing a new content node interest evidence, which points to \Announcement" (use the corresponding relation Ri here) and is authored by John. 1.4
Before we turn to description of the socio-semantic network dynamics there is one more important parameter not to be missed. It is external context, , which may include di erent parameters like project or campaign phase, ag for a bank holiday, or a maintenance status of the network. 2
The socio-semantic network dynamics Now, when all the components of the network have been de ned, the list of possible system updates, which determine the network evolution, is rather evident: { addition of new members to V ; { changes in user pro les ; { updates of social relations E1; : : : ; Ek and their parameters 1; : : : ; k; { creation and update of content nodes in T , (also a ects the authorship relation A); { changes in properties of and relations between the content nodes ; 1; : : : ; m; { changes in context . 3 3.1
A training center usually has a standardized set of reading materials, textbooks, tasks, assignments and exam tests. At the same time, the students' network evolves permanently. In terms of the de nition above one can say that the content part of this socio-semantic network is rather stable while the social network is very dynamic. 3.2
This example resides on the opposite side of the spectra. The team (the social network part) is rather stable while the content is actively processed and generated.
A targeted crowdsourcing project or a collective intelligence venture provide an example of a dynamic socio-semantic network, which is created from the scratch and is aimed at solving a particular task or a problem. New content is generated and new members join the network at all stages of its lifecycle. 4
Assume that we have all necessary data about the network dynamics available.
In all the examples mentioned above one can identify two principal tasks for
analysis:
{ Given all the data about the users activities and the content discover the
right people (knowing, capable, skillfull etc.)
{ Given all the data about the social network dynamics and the content
evolution discover the right texts (interesting, in uential, prominent etc.)
Many promising approaches and useful algorithms have already been
developed during the last decades [1{4], several new ideas are implemented in the
Witology platform [