Shallow Node Representation Learning using Centrality Indices
Paper i proceeding, 2021

In recent years, learning embeddings for nodes of a graph has become one of the most efficient w ays t o solve different graph problems such as link prediction, clustering and classification. I n t his p aper, w e p ropose a n ovel m ethod, called SECI, for learning embeddings of nodes, with application to link prediction. SECI samples from the network using breadth-first search and depth-first s earch, a nd i nterpolates b etween these two using centrality indices. The intuition behind SECI is that for nodes that have a low centrality score only a very small neighborhood is explored; and for dominant nodes that have a high centrality score a large neighborhood is explored. We evaluate the empirical performance of SECI over several realworld networks and show that it outperforms well-known existing algorithms.

Graphs (networks)

link prediction

shallow representation learning

node embeddings

centrality indices


Masoud Malek

Amirkabir University of Technology

Mostafa Haghir Chehreghani

Amirkabir University of Technology

Ehsan Nazerfard

Amirkabir University of Technology

Morteza Haghir Chehreghani

Data Science och AI 1

Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

9781665439022 (ISBN)

2021 IEEE International Conference on Big Data, Big Data 2021
Virtual, Online, USA,




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