Investigating Link Inference in Partially Observable Networks: Friendship Ties and Interaction

Mehwish Nasim, Raphaël Charbey, Christophe Prieur, Ulrik Brandes

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

While privacy preserving mechanisms, such as hiding one's friends list, may be available to withhold personal information on online social networking sites, it is not obvious whether to which degree a user's social behavior renders such an attempt futile. In this paper, we study the impact of additional interaction information on the inference of links between nodes in partially covert networks. This investigation is based on the assumption that interaction might be a proxy for connectivity patterns in online social networks. For this purpose, we use data collected from 586 Facebook profiles consisting of friendship ties (conceptualized as the network) and comments on wall posts (serving as interaction information) by a total of 64 000 users. The link-inference problem is formulated as a binary classification problem using a comprehensive set of features and multiple supervised learning algorithms. Our results suggest that interactions reiterate the information contained in friendship ties sufficiently well to serve as a proxy when the majority of a network is unobserved.

Original languageEnglish
Article number7747493
Pages (from-to)113-119
Number of pages7
JournalIEEE Transactions on Computational Social Systems
Volume3
Issue number3
DOIs
Publication statusPublished - Sep 2016
Externally publishedYes

Keywords

  • Data privacy
  • Facebook
  • supervised learning

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