Inferring Attitude in Online Social Networks Based On Quadratic Correlation

TL;DR

This paper introduces a machine learning model that predicts positive or negative relationships in online social networks using quadratic correlation, achieving high accuracy and adaptability to network changes.

Contribution

It presents a novel quadratic correlation-based approach for predicting relationship signs, learning peer influence sets dynamically during training.

Findings

Achieves near-perfect prediction accuracy on datasets like Epinions, Slashdot, and Wikipedia.

The model can be efficiently updated as the social network evolves.

Demonstrates effectiveness in real-world online social network analysis.

Abstract

The structure of an online social network in most cases cannot be described just by links between its members. We study online social networks, in which members may have certain attitude, positive or negative toward each other, and so the network consists of a mixture of both positive and negative relationships. Our goal is to predict the sign of a given relationship based on the evidences provided in the current snapshot of the network. More precisely, using machine learning techniques we develop a model that after being trained on a particular network predicts the sign of an unknown or hidden link. The model uses relationships and influences from peers as evidences for the guess, however, the set of peers used is not predefined but rather learned during the training process. We use quadratic correlation between peer members to train the predictor. The model is tested on popular online datasets such as Epinions, Slashdot, and Wikipedia. In many cases it shows almost perfect prediction accuracy. Moreover, our model can also be efficiently updated as the underlaying social network evolves.