English

Motif Iteration Model for Network Representation

Social and Information Networks 2017-10-04 v2

Abstract

Social media mining has become one of the most popular research areas in Big Data with the explosion of social networking information from Facebook, Twitter, LinkedIn, Weibo and so on. Understanding and representing the structure of a social network is a key in social media mining. In this paper, we propose the Motif Iteration Model (MIM) to represent the structure of a social network. As the name suggested, the new model is based on iteration of basic network motifs. In order to better show the properties of the model, a heuristic and greedy algorithm called Vertex Reordering and Arranging (VRA) is proposed by studying the adjacency matrix of the three-vertex undirected network motifs. The algorithm is for mapping from the adjacency matrix of a network to a binary image, it shows a new perspective of network structure visualization. In summary, this model provides a useful approach towards building link between images and networks and offers a new way of representing the structure of a social network.

Keywords

Cite

@article{arxiv.1710.00644,
  title  = {Motif Iteration Model for Network Representation},
  author = {Lintao Lv and Zengchang Qin and Tao Wan},
  journal= {arXiv preprint arXiv:1710.00644},
  year   = {2017}
}

Comments

10 pages, 3 figures and it is an extended vision of our conference paper in ICONIP 2017

R2 v1 2026-06-22T22:01:01.184Z