# Multi-Hot Compact Network Embedding

## TL;DR

This paper introduces a multi-hot compact embedding method for network representation that significantly reduces memory usage while maintaining performance, by learning shared basis vectors for node embeddings.

## Contribution

The paper proposes a novel multi-hot embedding strategy and an end-to-end MCNE model with a compressor to efficiently learn compact network embeddings from node features and network data.

## Key findings

- Reduces memory cost by about 90% compared to traditional methods
- Maintains similar network embedding performance despite compression
- Demonstrates effectiveness on three real-world network datasets

## Abstract

Network embedding, as a promising way of the network representation learning, is capable of supporting various subsequent network mining and analysis tasks, and has attracted growing research interests recently. Traditional approaches assign each node with an independent continuous vector, which will cause huge memory overhead for large networks. In this paper we propose a novel multi-hot compact embedding strategy to effectively reduce memory cost by learning partially shared embeddings. The insight is that a node embedding vector is composed of several basis vectors, which can significantly reduce the number of continuous vectors while maintain similar data representation ability. Specifically, we propose a MCNE model to learn compact embeddings from pre-learned node features. A novel component named compressor is integrated into MCNE to tackle the challenge that popular back-propagation optimization cannot propagate through discrete samples. We further propose an end-to-end model MCNE$_{t}$ to learn compact embeddings from the input network directly. Empirically, we evaluate the proposed models over three real network datasets, and the results demonstrate that our proposals can save about 90\% of memory cost of network embeddings without significantly performance decline.