Community Detection with Node Attributes and its Generalization

TL;DR

This paper introduces a novel community detection model that combines social network structure and node attributes without assuming their correlation, improving accuracy and robustness in uncorrelated scenarios.

Contribution

The paper presents a new model for community detection that does not require correlation between node attributes and communities, along with a derived detectability threshold and an optimal inference algorithm.

Findings

The model can recover communities with higher accuracy even when attributes are uncorrelated.

The detectability threshold for the model is derived and shown to be optimal.

Belief Propagation is used for inference, achieving optimal recovery down to the threshold.

Abstract

Community detection algorithms are fundamental tools to understand organizational principles in social networks. With the increasing power of social media platforms, when detecting communities there are two possi- ble sources of information one can use: the structure of social network and node attributes. However structure of social networks and node attributes are often interpreted separately in the research of community detection. When these two sources are interpreted simultaneously, one common as- sumption shared by previous studies is that nodes attributes are correlated with communities. In this paper, we present a model that is capable of combining topology information and nodes attributes information with- out assuming correlation. This new model can recover communities with higher accuracy even when node attributes and communities are uncorre- lated. We derive the detectability threshold for this model and use Belief Propagation (BP) to make inference. This algorithm is optimal in the sense that it can recover community all the way down to the threshold. This new model is also with the potential to handle edge content and dynamic settings.