Map Equation Centrality: Community-aware Centrality based on the Map Equation
Christopher Bl\"ocker, Juan Carlos Nieves, Martin Rosvall

TL;DR
This paper introduces a novel community-aware centrality measure based on the map equation, capturing node importance from a flow perspective and outperforming traditional measures in identifying influential nodes.
Contribution
The paper derives a new information-theoretic centrality score using the map equation framework, enabling community-aware importance assessment from a local network context.
Findings
Map equation centrality differentiates nodes more finely than traditional measures.
It predicts influential nodes that generate larger cascades in dynamical models.
The measure is adaptable to various network flow models.
Abstract
To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network structure. However, traditional centrality measures such as degree centrality, betweenness centrality, or PageRank neglect the community structure found in real-world networks. To study node importance based on network flows from a mesoscopic perspective, we analytically derive a community-aware information-theoretic centrality score based on network flow and the coding principles behind the map equation: map equation centrality. Map equation centrality measures how much further we can compress the network's modular description by not coding for random walker transitions to the respective node, using an adapted coding scheme and determining node importance from a network flow-based point of view. The…
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Taxonomy
TopicsRecommender Systems and Techniques · Complex Network Analysis Techniques
