Finding groups with maximum betweenness centrality via integer programming with random path sampling

Lagos, Tomás; Prokopyev, Oleg A.; Veremyev, Alexander

doi:10.1007/s10898-022-01269-2

Cited by 3 publications

References 29 publications

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A survey on optimization studies of group centrality metrics

Camur,

Vogiatzis

2024

Networks

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Centrality metrics have become a popular concept in network science and optimization. Over the years, centrality has been used to assign importance and identify influential elements in various settings, including transportation, infrastructure, biological, and social networks, among others. That said, most of the literature has focused on nodal versions of centrality. Recently, group counterparts of centrality have started attracting scientific and practitioner interest. The identification of sets of nodes that are influential within a network is becoming increasingly more important. This is even more pronounced when these sets of nodes are required to induce a certain motif or structure. In this study, we review group centrality metrics from an operations research and optimization perspective for the first time. This is particularly interesting due to the rapid evolution and development of this area in the operations research community over the last decade. We first present a historical overview of how we have reached this point in the study of group centrality. We then discuss the different structures and motifs that appear prominently in the literature, alongside the techniques and methodologies that are popular. We finally present possible avenues and directions for future work, mainly in three areas: (i) probabilistic metrics to account for randomness along with stochastic optimization techniques; (ii) structures and relaxations that have not been yet studied; and (iii) new emerging applications that can take advantage of group centrality. Our survey offers a concise review of group centrality and its intersection with network analysis and optimization.

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A survey on optimization studies of group centrality metrics

Camur,

Vogiatzis

2024

Networks

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The Parameterized Complexity of Maximum Betweenness Centrality

Schierreich,

Smutný

2024

Lecture Notes in Computer Science

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Efficient Betweenness Centrality Computation over Large Heterogeneous Information Networks

Wang,

Lin

et al. 2024

Proc. VLDB Endow.

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Betweenness centrality (BC), a classic measure which quantifies the importance of a vertex to act as a communication "bridge" between other vertices in the network, is widely used in many practical applications. With the advent of large heterogeneous information networks (HINs) which contain multiple types of vertices and edges like movie or bibliographic networks, it is essential to study BC computation on HINs. However, existing works about BC mainly focus on homogeneous networks. In this paper, we are the first to study a specific type of vertices' BC on HINs, e.g., find which vertices with typeAare important bridges to the communication between other vertices also with typeA?We advocate a meta path-based BC framework on HINs and formalize both coarse-grained and fine-grained BC (cBC and fBC) measures under the framework. We propose a generalized basic algorithm which can apply to computing not only cBC and fBC but also their variants in more complex cases. We develop several optimization strategies to speed up cBC or fBC computation by network compression and breadth-first search directed acyclic graph (BFS DAG) sharing. Experiments on several real-world HINs show the significance of cBC and fBC, and the effectiveness of our proposed optimization strategies.

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Finding groups with maximum betweenness centrality via integer programming with random path sampling

Cited by 3 publications

References 29 publications

A survey on optimization studies of group centrality metrics

A survey on optimization studies of group centrality metrics

The Parameterized Complexity of Maximum Betweenness Centrality

Efficient Betweenness Centrality Computation over Large Heterogeneous Information Networks

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