---
title: "Modeling the Homophily Effect between Links and Communities for Overlapping Community Detection"
authors:
  - "Hongyi Zhang"
  - "Tong Zhao"
  - "Irwin King"
  - "Michael R. Lyu"
doi: ""
url: "https://ijcai.org/proceedings/2016/554"
pdf: "https://ijcai.org/proceedings/2016/0554.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "3938-3943"
track: ""
keywords:
---

# Modeling the Homophily Effect between Links and Communities for Overlapping Community Detection

Hongyi Zhang, Tong Zhao, Irwin King, Michael R. Lyu

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 3938-3943.

## Abstract

Overlapping community detection has drawn much attention recently since it allows nodes in a network to have multiple community memberships. A standard framework to deal with overlapping community detection is Matrix Factorization (MF). Although all existing MF-based approaches use links as input to identify communities, the relationship between links and communities is still under-investigated. Most of the approaches only view links as consequences of communities (community-to-link) but fail to explore how nodes' community memberships can be represented by their linked neighbors (link-to-community). In this paper, we propose a Homophily-based Nonnegative Matrix Factorization (HNMF) to model both-sided relationships between links and communities. From the community-to-link perspective, we apply a preference-based pairwise function by assuming that nodes with common communities have a higher probability to build links than those without common communities. From the link-to-community perspective, we propose a new community representation learning with network embedding by assuming that linked nodes have similar community representations. We conduct experiments on several real-world networks and the results show that our HNMF model is able to find communities with better quality compared with state-of-the-art baselines.