Near linear time algorithm to detect community structures in large-scale networks

TLDR

Community detection is crucial for understanding real‑world networks, yet existing methods often require prior knowledge of community numbers or are computationally expensive. This study explores a simple label‑propagation algorithm that relies solely on network structure, avoiding optimization or prior community information. The algorithm initializes each node with a unique label and iteratively updates labels to the majority among neighbors, allowing densely connected groups to converge on a single label and form communities, which we validate on networks with known structures. Results show the method runs in near‑linear time, making it significantly less computationally demanding than previous approaches.

Abstract

Community detection and analysis is an important methodology for understanding the organization of various real-world networks and has applications in problems as diverse as consensus formation in social communities or the identification of functional modules in biochemical networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. In this paper we investigate a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities. In our algorithm every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities. We validate the algorithm by applying it to networks whose community structures are known. We also demonstrate that the algorithm takes an almost linear time and hence it is computationally less expensive than what was possible so far.