Abstract
Vector embedding over a real network is considered as feature learning of nodes of the network which is utilized in many downstream machine learning applications such as link prediction. A network of size n can be represented as a collection of n vectors (feature vectors) of dimension d (≪ n) which have encoded structural and spectral information of the associated network. These feature vectors can be used in two ways: first, in the extraction of existing links and other higher order structural or functional relations among the nodes of the network and second, in the prediction of the structural evolution of the network in near future. It is observed that matrix factorization based vector embedding algorithms are able to learn more informative feature vectors but scalability is a major bottleneck due to memory and computationally intensive task.
In this paper, we present a novel distributed algorithm to learn feature vectors. It is considered that a node stores one feature vector of one of its neighbours known as shared vector, along with its own feature vector. And the learning of feature vector of a node includes only feature vectors and shared vectors stored in its neighbourhood only. Feature vectors get updated during the learning, so is shared vectors. Hence, a local sharing phenomenon leads to sharing of global information dynamically. The proposed distributed algorithm learns matrix factorization of a given network in which a node only utilizes the information available at its neighbouring nodes and connected nodes exchange feature vectors dynamically. Thus, the proposed algorithm doesn’t have the limitation on its scalability. The performance of the proposed distributed algorithm for network representation learning (NRL) is evaluated for the learning of first-order proximity, spectral distance, and link prediction. The proposed network representation learning algorithm outperforms the existing state-of-the-art NRL algorithms such as node2vec, deep walk, and edge-based matrix factorization.
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Pandey, P.K. (2019). Network Representation Learning Using Local Sharing and Distributed Matrix Factorization (LSDMF). In: Ghanbarnejad, F., Saha Roy, R., Karimi, F., Delvenne, JC., Mitra, B. (eds) Dynamics On and Of Complex Networks III. DOOCN 2017. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-14683-2_8
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