My problem is to profile the individual user (i.e. mine individual user's interest, location, and many more).
What we have as input are the network structure (e.g. linked-in network) including user set $V$ and link set $E$ and the attributes for each user (e.g. affiliations, employment, skills). There is NO explicit weight on the link, of course. The attributes are only available for a subset of labeled user $V_l$.
A user $u_i \in V$ has have partial attributes or no attribute available. Now we want to infer those attributes from his friends. The intuition is that if all friends of $u_i$ are PhD students in University B, then $u_i$ is very likely to be a student in University B.
To solve this problem, the first step is to select a proper set of user $V_r$ to propagate their attributes to $u_i$. The criteria of $V_r$ is
- They are relevant to $u_i$.
- At least one of them has available attributes.
Existing Solution for $V_r$ selection
In social networks analysis, I only know the following two kinds of networks:
- Whole social networks -- all the nodes and links among them.
- Ego network -- a focal node and all the directly connected nodes (1-hop neighbors) and all the links among those nodes.
In my opinion, using the whole social network is not feasible. From social network service, we can collect millions of users, most of whom may be completely irrelevant to our target. Besides, the full network structure is unavailable to public in general. Therefore, I want a proper sub-network. However, the ego network seems to be too small. Because, some nodes may have only a few directly connected neighbors.
I want to know what other $V_r$ can I use and why.
It is clear that the ego network is one connected component. Is the whole social network necessary one connected component?
If not, then there should be several connected components in this "whole" social network. Then given one focal node, we can find one connected component centered on this node. Is there a terminology (such as ego network) on this kind of network?