Efficient Closeness Centrality Computation for Dynamic Graphs

Shao, Zhongcai; Guo, Na; Gu, Yu; Wang, Zhigang; Li, Fangfang

doi:10.1007/978-3-030-59416-9_32

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…The filtering method is an extension of level-based filtering to directed and weighted networks. The dynamic algorithm by Shao et al [224] maintains closeness centrality by efficiently detecting all affected shortest paths based on articulation points. The main observation is that a graph can be divided into a series of biconnected components which are connected by articulation points -the distances between two arbitrary vertices in the graph can be expressed as multiple distances between different biconnected components.…”

Section: Centralitiesmentioning

confidence: 99%

Recent Advances in Fully Dynamic Graph Algorithms

Hanauer¹,

Henzinger²,

Schulz³

2021

Preprint

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In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we survey recent engineering and theory results in the area of fully dynamic graph algorithms.

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Section: Centralitiesmentioning

confidence: 99%

Recent Advances in Fully Dynamic Graph Algorithms

Hanauer¹,

Henzinger²,

Schulz³

2021

Preprint

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“…In (Guan et al 2020) Guan and others use the closeness centrality based on the revised Floyd-Warshall algorithm (RFWA) for the weighted networks to measure the industrial position on the global value chain. In (Shao et al 2020) Shao et al use the closeness centrality measure for the dynamic networks (realtime networks) to find the influential nodes. They cluster the dynamic network into a biconnected network and then find the network's influential nodes using the closeness centrality.…”

Section: Cc(a) =mentioning

confidence: 99%

Centrality Measures in Finding Influential Nodes for the Big-Data Network

Gopalakrishnan

Sridharan

Swaminathan

2021

Handbook of Smart Materials, Technologies, and Devices

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Network analysis determines node-to-node connectivity and examines each node's features. Various tools are available in the literature to study networks like graph theoretical, statistical, and many. As the graph plays a vital role in representing networks, the centrality measurements play an essential role in network analysis. The centrality measures use various metrics for evaluating how important the node is. The centrality predicts the characteristics and importance of the nodes in the network. In this review, selected 16 centrality measures presented along with their implementations.

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“…Based upon the types of adjacency matrices (Z p , Z a ), it is possible to analyze a variety of knowledge analytics, such as centrality analysis equations [12], mean rates analysis [2], density measurements [28], centrality measurements [46] [11] [14], and so on, issued from the conventional social network literature. In general, an affiliation network is a bipartite graph, as described in the previous section, in which non-directed lines connect performers aligned on one side of the diagram to the workflow activities aligned on the other side.…”

Section: ) the Formal Representationmentioning

confidence: 99%

“…That is, this paper is basically concerned about organizational centralities [2] [9] [10] [7] [11] in a workflowsupported organization. The typical four out of the conventional centrality analysis techniques [12] [13] [14] are degree, closeness, betweenness, and eigenvector centralities, and the authors are interested in the closeness centrality analysis technique, in particular. It is necessary to quantitatively measure the extents of closeness centralities between performers through activities as well as the extents of the closeness centralities between activities through performers in a specific workflow procedure.…”

Section: Introductionmentioning

confidence: 99%

Organizational Closeness Centralities of Workflow-Supported Performer-to-Activity Affiliation Networks

Ahn

Kim

2021

IEEE Access

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A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. These affiliated relationships exhibit a series of valuable human-centered organizational knowledge and are utilized for exploring human resource's work patterns. This paper focuses not only on a specific type of affiliated relationships between performers and activities, in particular, which forms a performer-to-activity affiliation network, but also on a specific type of analysis techniques, which builds a closeness centrality measurement approach for quantifying the degrees of farnesses between performers as well as between activities. In other words, this paper investigates a series of formal approaches for building organizational closeness centrality measurement techniques on the specific type of affiliation networks. The investigation mainly deploys two types of algorithmic formalisms along with an operational example, which are measuring performercentered organizational closeness centralities and activity-centered organizational closeness centralities, respectively. In order to validate the deployed algorithmic equations, the paper carries out a couple of operational experiments; One is on an ICN-based workflow package and the other is on a discovered workflow model mined from a dataset of workflow event logs. Summarily, this paper devises a series of algorithms and equations for measuring closeness centralities of activities, verify the devised algorithms and their related equations along with operational examples, and discuss the ultimate implications of these analysis techniques of organizational closeness centrality measurements as the performer-to-activity affiliation networking knowledge in workflow-supported organizations.

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Efficient Closeness Centrality Computation for Dynamic Graphs

Cited by 11 publications

References 19 publications

Recent Advances in Fully Dynamic Graph Algorithms

Recent Advances in Fully Dynamic Graph Algorithms

Centrality Measures in Finding Influential Nodes for the Big-Data Network

Organizational Closeness Centralities of Workflow-Supported Performer-to-Activity Affiliation Networks

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