Towards Scalable Topic Detection on Web via Simulating Levy Walks Nature of Topics in Similarity Space
<|reference_start|>Towards Scalable Topic Detection on Web via Simulating Levy Walks Nature of Topics in Similarity Space: Organizing a few webpages from social media websites into popular topics is one of the key steps to understand trends on web. Discovering popular topics from web faces a sea of noise webpages which never evolve into popular topics. In this paper, we discover that the similarity values between webpages in a popular topic contain the statistically similar features observed in Levy walks. Consequently, we present a simple, novel, yet very powerful Explore-Exploit (EE) approach to group topics by simulating Levy walks nature in the similarity space. The proposed EE-based topic clustering is an effective and effcient method which is a solid move towards handling a sea of noise webpages. Experiments on two public data sets demonstrate that our approach is not only comparable to the state-of-the-art methods in terms of effectiveness but also significantly outperforms the state-of-the-art methods in terms of efficiency.<|reference_end|>
@article{pang2024towards,
    title={Towards Scalable Topic Detection on Web via Simulating Levy Walks Nature
  of Topics in Similarity Space},
    author={Junbiao Pang, Qingming Huang},
    journal={arXiv preprint arXiv:2408.05348},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05348},
    primaryClass={cs.IR}
}