  Sampling a network is an important prerequisite for unsupervised network
embedding. Further, random walk has widely been used for sampling in previous
studies. Since random walk based sampling tends to traverse adjacent neighbors,
it may not be suitable for heterogeneous network because in heterogeneous
networks two adjacent nodes often belong to different types. Therefore, this
paper proposes a K-hop random walk based sampling approach which includes a
node in the sample list only if it is separated by K hops from the source node.
We exploit the samples generated using K-hop random walker for network
embedding using skip-gram model (word2vec). Thereafter, the performance of
network embedding is evaluated on co-authorship prediction task in
heterogeneous DBLP network. We compare the efficacy of network embedding
exploiting proposed sampling approach with recently proposed best performing
network embedding models namely, Metapath2vec and Node2vec. It is evident that
the proposed sampling approach yields better quality of embeddings and
out-performs baselines in majority of the cases.
