Leskovecl et al., 2010,Predicting Positive and Negative Linksin Online Social Networks
Contents
Online version
An online version of this paper is available here.
Summary
Machine learning has previously been used as a way of Determining Social Network Attributes. This paper follows this path and addresses a sub-problem: predicting negative links in social networks. Certain social networks allow you to express negative relations with other users, such as Slashdot (foes), Epinions (distrust) and Wikipedia (voting down). This paper introduces ways of predicting these negative relations, and propose explanations for the models that are based on psychology theory. Finally, they use a SlashDot dataset to evaluate the quality of their approach.
Key Contributions
Ways of predicting negative relations in social networks.
Models
Logistic Regression
The task of predicting edge sign (positive or negative) is accomplished by a logistic regression model that uses the following features:
- signed degree of a node (i.e., how many positive edges it has)
- "type" of triads formed by (u,v and w) in a way that the node w has an edge to both u and v. For example, a negative/negative relations exists if w foe with both u and v (thus u and v might be friends, as explained below). There are 16 types of such triads.
Experiments and Evaluation
The model is compared to a random approach, where positive and negative are randomly selected. For this approach to be valid, the authors sample from the SlashDot dataset to obtain 50% of positive and negative. The effects of the different features on the logistic regression are shown in the following results graph:
File:Result predicting negative.jpg Source: the original paper
