Work

Adversarial Graph Embedding for Ensemble Clustering

Year: 2019

Type: conference-paper

Abstract: Ensemble clustering generally integrates basic partitions into a consensus one through a graph partitioning method, which, however, has two limitations: 1) it neglects to reuse original features; 2) obtaining consensus partition with learnable graph representations is still under-explored. In this paper, we propose a novel Adversarial Graph Auto-Encoders (AGAE) model to incorporate ensemble clustering into a deep graph embedding process. Specifically, graph convolutional network is adopted as probabilistic encoder to jointly integrate the information from feature content and consensus graph, and a simple inner product layer is used as decoder to reconstruct graph with the encoded latent variables (i.e., embedding representations). Moreover, we develop an adversarial regularizer to guide the network training with an adaptive partition-dependent prior. Experiments on eight real-world datasets are presented to show the effectiveness of AGAE over several state-of-the-art deep embedding and ensemble clustering methods.

Source: International Joint Conference on Artificial Intelligence

DOI: 10.24963/ijcai.2019/494

Authors: Zhiqiang Tao, Hongfu Liu, Jun Li, Zhaowen Wang, Yun Fu

Institutions: Northeastern University, Brandeis University, Massachusetts Institute of Technology, Adobe Systems (United States)

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, Army Research Office

Awards: G3571051887, EAGER: Vision-Based Activity Forecasting by Mining Temporal Causalities, G7452299184