Abstract

Entities in networks may interact positively as well as negatively with each other, which may be modeled by a signed network containing both positive and negative edges between nodes. Understanding how entities behave and not just with whom they interact positively or negatively leads us to the new problem of structural role mining in signed networks. We solve this problem by developing structural node embedding methods that build on sociological theory and technical advances developed specifically for signed networks. With our methods, we can not only perform node-level role analysis, but also solve another new problem of characterizing entire signed networks to make network-level predictions. We motivate our work with an application to social media analysis, where we show that our methods are more insightful and effective at detecting user-level and session-level malicious online behavior …

Date

December 1, 2020

Authors

Mark Heimann, Goran Murić, Emilio Ferrara

Book

International Conference on Complex Networks and Their Applications

Pages

3-14

Publisher

Springer International Publishing