Node2binary : Compact Graph Node Embeddings using Binary Vectors

Abstract

With the adoption of deep learning models to low-power, small-memory edge devices, energy consumption and storage usage of such models have become a key concern. The problem exacerbates even further with ever-growing data and equally-matched bulkier models. This concern is particularly pronounced for graph data due to its quadratic storage, irregular (non-grid) geometry, and very large size. Typical graph data, such as road networks, infrastructure networks, and social networks, easily exceeds millions of nodes, and several gigabytes of storage is needed just to store the node embedding vectors, let alone the model parameters. In recent years, the memory issue has been addressed by moving away from memory-intensive double precision floating-point arithmetic towards single-precision or even half-precision, often by trading-off marginally small performance. Along this effort, we propose Node2Binary, which embeds graph nodes in as few as 128 binary bits, thereby reducing the memory footprint of vertex embedding vectors by several orders of magnitude. Node2Binary. leverages a fast community detection algorithm to convert the given graph into a hierarchical partition tree and then find embeddings of graph vertices in binary space by solving a combinatorial optimization (CO) task over the tree edges. CO is NP-hard, but Node2Binary uses an innovative combination of discrete gradient descent and randomization to solve this task effectively and efficiently. Extensive experiments over four real-world graphs show that Node2Binary achieves competitive performance compared to the state-of-the art graph embedding methods in both node classification and link prediction tasks.