The Advantage of Evidential Attributes in Social Networks

TL;DR

This paper demonstrates that using evidential attributes in social network community detection improves accuracy over probabilistic and numerical attributes, especially in noisy, uncertain data scenarios.

Contribution

It introduces a novel approach for community detection that leverages evidential attributes, showing their advantages over traditional attribute types in noisy environments.

Findings

Evidential attributes outperform probabilistic and numerical attributes in community detection.

Adding noise to attributes tests robustness of detection methods.

Experiments confirm evidential attributes lead to higher NMI scores.

Abstract

Nowadays, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while others take use of both the graph structure and the node attributes. In real-world networks, there are many uncertain and noisy attributes in the graph. In this paper, we will present how we detect communities in graphs with uncertain attributes in the first step. The numerical, probabilistic as well as evidential attributes are generated according to the graph structure. In the second step, some noise will be added to the attributes. We perform experiments on graphs with different types of attributes and compare the detection results in terms of the Normalized Mutual Information (NMI) values. The experimental results show that the clustering with evidential attributes gives better results comparing to those with probabilistic and numerical attributes. This illustrates the advantages of evidential attributes.