Exploring Hierarchies in Online Social Networks
Can Lu, Jeffrey Xu Yu, Rong-Hua Li, Hao Wei

TL;DR
This paper introduces a novel method for extracting social hierarchies from online social networks by modeling them as DAGs, using a two-phase algorithm to efficiently remove cycles and reveal hierarchical structures.
Contribution
We propose a new two-phase algorithm, Greedy-&-Refine, for efficiently finding the maximum Eulerian subgraph to uncover social hierarchies in networks.
Findings
Algorithm is at least 100 times faster than baseline.
Effective in extracting hierarchical structures from real-world data.
High-quality solutions with strong theoretical bounds.
Abstract
Social hierarchy (i.e., pyramid structure of societies) is a fundamental concept in sociology and social network analysis. The importance of social hierarchy in a social network is that the topological structure of the social hierarchy is essential in both shaping the nature of social interactions between individuals and unfolding the structure of the social networks. The social hierarchy found in a social network can be utilized to improve the accuracy of link prediction, provide better query results, rank web pages, and study information flow and spread in complex networks. In this paper, we model a social network as a directed graph G, and consider the social hierarchy as DAG (directed acyclic graph) of G, denoted as GD. By DAG, all the vertices in G can be partitioned into different levels, the vertices at the same level represent a disjoint group in the social hierarchy, and all…
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Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Graph Neural Networks · Opinion Dynamics and Social Influence
