A collaborative network is a social network that is comprised of experts who cooperate with each other to fulfill a special goal. Analyzing the graph of 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 a promising tool. Graph embedding attempts to represent graph nodes as low-dimensional vectors. In this paper, we propose a graph embedding method, called ExEm, which using dominating-set theory and deep learning approaches . In the proposed method, the dominating set theory is applied to the collaborative network and dominating nodes of this network are found. After that, a set of random walks is created which starts from dominating nodes(experts). The main condition for constricting these random walks is the existence of another dominating node . After making the walks that satisfy the stated conditions, they are stored as a sequence in a corpus. In the next step, the corpus is fed to the SKIP-GRAM neural network model. Word2vec, fastText and their combination are employed to train the neural network of the SKIP-GRAM model. Finally, the result is the low dimensional vectors of experts, called expert embeddings. Expert embeddings can be used for various purposes including accurately modeling experts' expertise or computing experts' scores in expert recommendation systems. Hence, we also introduce a novel strategy  to calculate experts' scores by using the extracted expert embedding vectors. The effectiveness of ExEm is validated through assessing its performance on multi-label classification, link prediction, and recommendation tasks . We conduct extensive experiments on common datasets . Moreover in this study, we present data related to a co-author network formed by crawling the vast author profiles from Scopus .
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 .
D
the graph of
R
a
an effective and
R
which using
that uses
R
. In the proposed method, the dominating set theory is applied to
to capture node representations. ExEm finds dominating nodes of
R
dominating nodes of this network are found. After that, a set of random walks is created which starts from dominating nodes(experts). The main condition for constricting these random walks is the existence of another dominating node . After making the walks that satisfy the stated conditions, they are stored as a sequence in a corpus. In the next step, the corpus is fed to the SKIP-GRAM neural network model.
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
D
their combination are employed to train the neural network of
R
SKIP-GRAM model. Finally, the
concatenation of these two. The final
R
low dimensional
low-dimensional
R
Expert
The extracted expert
R
used for various purposes including accurately modeling experts' expertise or computing experts' scores in expert recommendation systems. Hence, we also
applied to many applications. In order to extend these embeddings into the expert recommendation system, we
A
that uses expert vectors
R
by using the extracted expert embedding vectors. The
and recommend experts. At the end, we conduct extensive experiments to validate the
D
is validated
R
on
over the
D
. We conduct extensive experiments
R
. Moreover in this study, we present data related to a co-author network
and our collected data
R
profiles from Scopus
Scopus profiles. The experiments show that ExEm outperforms the baselines especially in dense networks