Motif Iteration Model for Network Representation

TL;DR

The paper introduces the Motif Iteration Model (MIM) for social network representation, utilizing network motifs and a novel visualization algorithm to better understand social media structures.

Contribution

It presents a new network representation model based on iterative motifs and a heuristic algorithm for visualizing network structures as binary images.

Findings

MIM effectively captures social network properties.

VRA algorithm improves network visualization.

Provides a new perspective linking images and network structures.

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.