A Survey of Densest Subgraph Discovery on Large Graphs
Wensheng Luo, Chenhao Ma, Yixiang Fang, Laks V.S. Lakshmanan

TL;DR
This survey comprehensively reviews the densest subgraph discovery problem, its applications, existing solutions, and future research directions in large graph analysis.
Contribution
It classifies and analyzes around 50 research papers on DSD, providing a thorough overview and insights into current methods and challenges.
Findings
Classified existing DSD solutions into several groups
Compared models and solutions across different works
Identified promising future research directions
Abstract
With the prevalence of graphs for modeling complex relationships among objects, the topic of graph mining has attracted a great deal of attention from both academic and industrial communities in recent years. As one of the most fundamental problems in graph mining, the densest subgraph discovery (DSD) problem has found a wide spectrum of real applications, such as discovery of filter bubbles in social media, finding groups of actors propagating misinformation in social media, social network community detection, graph index construction, regulatory motif discovery in DNA, fake follower detection, and so on. Theoretically, DSD closely relates to other fundamental graph problems, such as network flow and bipartite matching. Triggered by these applications and connections, DSD has garnered much attention from the database, data mining, theory, and network communities. In this survey, we…
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