node2binary: Compact Graph Node Embeddings Using Binary Vectors
Track: Social networks and social media
Keywords: Binary Space Embedding, Graph Embedding, Discrete Gradient Descent, Randomized Algorithm
Abstract: With the adoption of deep learning models to low-power, small-memory edge devices, energy consumption and storage usage of such models has become a key concern. The problem acerbates 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, 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 low as 128 binary bits, which drastically reduces the memory footprint of vertex embedding vectors by several order of magnitude. Node2binary leverages a fast community detection algorithm to covert the given graph into a hierarchical partition tree and then find embedding 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 effectively and efficiently. Our extensive experiments over four real-world graphs show that Node2binary achieves competitive performances compared to the state-of-the art graph embedding methods in both node classification and link prediction tasks.
Submission Number: 910
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