Subspace properties of randomized network coding

Randomized network coding has network nodes randomly combine and exchange linear combinations of the source packets. A header appended to the packet, called coding vector, specifies the exact linear combination that each packet carries. The main contribution of this work is to investigate properties of the subspacesspanned by the collected coding vectors in each network node. We use these properties to exhibit the relationship between the network topology and the subspaces collected at the nodes. This allows us to passively infer the network topology for a general class of graphs.