article · 2018
Feature Hashing for Network Representation Learning
Qixiang Wang, Shanfeng Wang, Maoguo Gong, Yue Wu
Cited by 11Open access
Abstract
The goal of network representation learning is to embed nodes so as to encode the proximity structures of a graph into a continuous low-dimensional feature space. In this paper, we propose a novel algorithm called node2hash based on feature hashing for generating node embeddings. This approach follows the encoder-decoder framework. There are two main mapping functions in this framework. The first is an encoder to map each node into high-dimensional vectors. The second is a decoder to hash these vectors into a lower dimensional feature space. More specifically, we firstly derive a proximity measurement called expected distance as target which combines position distribution and co-occurrence statistics of nodes over random walks so as to build a proximity matrix, then introduce a set of T different hash functions into feature hashing to generate uniformly distributed vector representations of nodes from the proximity matrix. Compared with the existing state-of-the-art network representation learning approaches, node2hash shows a competitive performance on multi-class node classification and link prediction tasks on three real-world networks from various domains.
Cite this paper
Wang, Q., Wang, S., Gong, M., & Wu, Y. (2018). Feature Hashing for Network Representation Learning. https://doi.org/10.24963/ijcai.2018/390
Wang, Qixiang, et al. “Feature Hashing for Network Representation Learning.” 2018, pp. 2812–18, https://doi.org/10.24963/ijcai.2018/390.
Wang, Qixiang, Shanfeng Wang, Maoguo Gong, and Yue Wu. 2018. “Feature Hashing for Network Representation Learning.” https://doi.org/10.24963/ijcai.2018/390.
Wang, Q. et al. (2018) ‘Feature Hashing for Network Representation Learning’. Available at: https://doi.org/10.24963/ijcai.2018/390.
@article{wang2018feature,
title = {{Feature Hashing for Network Representation Learning}},
author = {Wang, Qixiang and Wang, Shanfeng and Gong, Maoguo and Wu, Yue},
pages = {2812--2818},
year = {2018},
doi = {10.24963/ijcai.2018/390}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.