PINE: Pipeline for Important Node Exploration in Attributed Networks

TL;DR

PINE is an unsupervised, attention-based graph model that effectively identifies important nodes in attributed networks by leveraging node features and structure, outperforming traditional centrality measures.

Contribution

The paper introduces PINE, a novel unsupervised framework combining attention mechanisms and node attributes for importance ranking in attributed networks.

Findings

PINE outperforms traditional centrality measures in importance detection.

The method effectively handles both homogeneous and heterogeneous networks.

PINE is applicable to large-scale real-world enterprise graphs.

Abstract

A graph with semantically attributed nodes are a common data structure in a wide range of domains. It could be interlinked web data or citation networks of scientific publications. The essential problem for such a data type is to determine nodes that carry greater importance than all the others, a task that markedly enhances system monitoring and management. Traditional methods to identify important nodes in networks introduce centrality measures, such as node degree or more complex PageRank. However, they consider only the network structure, neglecting the rich node attributes. Recent methods adopt neural networks capable of handling node features, but they require supervision. This work addresses the identified gap--the absence of approaches that are both unsupervised and attribute-aware--by introducing a Pipeline for Important Node Exploration (PINE). At the core of the proposed framework is an attention-based graph model that incorporates node semantic features in the learning process of identifying the structural graph properties. The PINE's node importance scores leverage the obtained attention distribution. We demonstrate the superior performance of the proposed PINE method on various homogeneous and heterogeneous attributed networks. As an industry-implemented system, PINE tackles the real-world challenge of unsupervised identification of key entities within large-scale enterprise graphs.