Scalable Algorithms for Densest Subgraph Discovery

Luo, Wensheng; Fang, Yixiang; Ma, Chenhao; Zhou, Xu

doi:10.1109/icde55515.2023.00029

2023 IEEE 39th International Conference on Data Engineering (ICDE) 2023

DOI: 10.1109/icde55515.2023.00029

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Scalable Algorithms for Densest Subgraph Discovery

Wensheng Luo

Yixiang Fang

Chenhao Ma

et al.

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2024

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Cited by 2 publications

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A Survey on the Densest Subgraph Problem and its Variants

Lanciano,

Miyauchi,

Fazzone

et al. 2024

ACM Comput. Surv.

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The Densest Subgraph Problem requires to find, in a given graph, a subset of vertices whose induced subgraph maximizes a measure of density. The problem has received a great deal of attention in the algorithmic literature over the last five decades, with many variants proposed and many applications built on top of this basic definition. Recent years have witnessed a revival of research interest in this problem with several important contributions, including some groundbreaking results, published in 2022 and 2023. This survey provides a deep overview of the fundamental results and an exhaustive coverage of the many variants proposed in the literature, with a special attention to the most recent results. The survey also presents a comprehensive overview of applications and discusses some interesting open problems for this evergreen research topic.

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A Survey on the Densest Subgraph Problem and its Variants

Lanciano,

Miyauchi,

Fazzone

et al. 2024

ACM Comput. Surv.

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A Counting-based Approach for Efficient k-Clique Densest Subgraph Discovery

Zhou,

Guo,

Fang

et al. 2024

Proc. ACM Manag. Data

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Densest subgraph discovery (DSD) is a fundamental topic in graph mining. It has been extensively studied in the literature and has found many real applications in a wide range of fields, such as biology, finance, and social networks. As a typical problem of DSD, the k-clique densest subgraph (CDS) problem aims to detect a subgraph from a graph, such that the ratio of the number of k-cliques over the number of its vertices is maximized. This problem has received plenty of attention in the literature, and is widely used in identifying larger ''near-cliques''. Existing CDS solutions, either k-core or convex programming based solutions, often need to enumerate almost all the k-cliques, which is very inefficient because real-world graphs usually have a vast number of k-cliques. To improve the efficiency, in this paper, we propose a novel framework based on the Frank-Wolfe algorithm, which only needs k-clique counting, rather than k-clique enumeration, where the former one is often much faster than the latter one. Based on the framework, we develop an efficient approximation algorithm, by employing the state-of-the-art k-clique counting algorithm and proposing some optimization techniques. We have performed extensive experimental evaluation on 14 real-world large graphs and the results demonstrate the high efficiency of our algorithms. Particularly, our algorithm is up to seven orders of magnitude faster than the state-of-the-art algorithm with the same accuracy guarantee.

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Scalable Algorithms for Densest Subgraph Discovery

Cited by 2 publications

References 57 publications

A Survey on the Densest Subgraph Problem and its Variants

A Survey on the Densest Subgraph Problem and its Variants

A Counting-based Approach for Efficient k-Clique Densest Subgraph Discovery

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