Multiple Kernel Representation Learning on Networks

Abstract

Learning representations of nodes in a low dimensional space is a crucial task with numerous interesting applications in network analysis, including link prediction, node classification, and visualization. Two popular approaches for this problem are matrix factorization and random walk-based models. In this paper, we aim to bring together the best of both worlds, towards learning node representations. In particular, we propose a weighted matrix factorization model that encodes random walk-based information about nodes of the network. The benefit of this novel formulation is that it enables us to utilize kernel functions without realizing the exact proximity matrix so that it enhances the expressiveness of existing matrix decomposition methods with kernels and alleviates their computational complexities. We extend the approach with a multiple kernel learning formulation that provides the flexibility of learning the kernel as the linear combination of a dictionary of kernels in data-driven fashion. We perform an empirical evaluation on real-world networks, showing that the proposed model outperforms baseline node embedding algorithms in downstream machine learning tasks.

This manuscript is an extended version of the previous work entitled "Kernel Node Embeddings" (arXiv:1909.03416), and it has been accepted for publication in IEEE Transactions on Knowledge and Data Engineering

References in corpus (11)

• Fast unfolding of communities in large networks
• LINE: Large-scale Information Network Embedding
• Kernel methods in machine learning
• Graph Evolution: Densification and Shrinking Diameters
• HARP: Hierarchical Representation Learning for Networks
• NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
• Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
• Rethinking Kernel Methods for Node Representation Learning on Graphs
• GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis
• Topic-aware latent models for representation learning on networks
• InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity