1 research outputs found
ExEm: Expert Embedding using dominating set theory with deep learning approaches
A collaborative network is a social network that is comprised of experts who
cooperate with each other to fulfill a special goal. Analyzing this network
yields meaningful information about the expertise of these experts and their
subject areas. To perform the analysis, graph embedding techniques have emerged
as an effective and promising tool. Graph embedding attempts to represent graph
nodes as low-dimensional vectors. In this paper, we propose a graph embedding
method, called ExEm, that uses dominating-set theory and deep learning
approaches to capture node representations. ExEm finds dominating nodes of the
collaborative network and constructs intelligent random walks that comprise of
at least two dominating nodes. One dominating node should appear at the
beginning of each path sampled to characterize the local neighborhoods.
Moreover, the second dominating node reflects the global structure information.
To learn the node embeddings, ExEm exploits three embedding methods including
Word2vec, fastText and the concatenation of these two. The final result is the
low-dimensional vectors of experts, called expert embeddings. The extracted
expert embeddings can be applied to many applications. In order to extend these
embeddings into the expert recommendation system, we introduce a novel strategy
that uses expert vectors to calculate experts' scores and recommend experts. At
the end, we conduct extensive experiments to validate the effectiveness of ExEm
through assessing its performance over the multi-label classification, link
prediction, and recommendation tasks on common datasets and our collected data
formed by crawling the vast author Scopus profiles. The experiments show that
ExEm outperforms the baselines especially in dense networks.Comment: Submitted to Expert Systems with Applications for revie