Network analysis with negative links

Abstract

As we rapidly continue into the information age, the rate at which data is produced has created an unprecedented demand for novel methods to effectively/efficiently extract insightful patterns. Then, once paired with domain knowledge, we can seek to understand the past, make predictions about the future, and ultimately take actionable steps towards improving our society. Thus, due to the fact that much of today’s big data can be represented as graphs, emphasis is being taken to harness the natural structure of data through network analysis. Furthermore, many real-world networks can be better represented as signed networks, e.g., in an online social network such as Facebook, friendships can be represented as positive links while negative links can represent blocked users. Hence, due to signed networks being ubiquitous, in this work we seek to provide a fundamental background into the domain, a hierarchical categorization of existing work highlighting both seminal and state of the art, provide a curated collection of signed network datasets, and discuss important future directions.