Approximating the Community Structure of the Long Tail

Authors

  • Akshay Java University of Maryland, Baltimore County
  • Anupam Joshi University of Maryland, Baltimore County
  • TIm Finin University of Maryland, Baltimore County

Abstract

In many social media applications, a small fraction of the members are highly linked while most are sparsely connected to the network. Such a skewed distribution is sometimes referred to as the "long tail". Popular applications like meme trackers and content aggregators mine for information from only the popular blogs located at the head of this curve. On the other hand, the long tail contains large volumes of interesting information and niches. The question we address in this work is how best to approximate the community membership of entities in the long tail using only a small percentage of the entire graph structure. Our technique utilizes basic linear algebra manipulations and spectral methods. It has the advantage of quickly and efficiently finding a reasonable approximation of the community structure of the overall network. Such a method has significant applications in blog analysis engines as well as social media monitoring tools in general.

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Published

2021-09-25

How to Cite

Java, . A., Joshi, A., & Finin, T. (2021). Approximating the Community Structure of the Long Tail. Proceedings of the International AAAI Conference on Web and Social Media, 2(1), 194-195. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18647