Computing Top-<i>k</i> Closeness Centrality in Fully-dynamic Graphs

Bisenius, Patrick; Bergamini, Elisabetta; Angriman, Eugenio; Meyerhenke, Henning

doi:10.1137/1.9781611975055.3

Cited by 22 publications

(7 citation statements)

References 21 publications

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“…In a retweet network this metric can be interpreted as a user's ability to spread information to other users in the network (Okamoto, Chen, and Li 2008). We compute the top-k closeness centrality to find the 10,000 most central nodes within the detractor and promoter clus- ters (Bisenius et al 2017).…”

Section: Data Enhancementmentioning

confidence: 99%

VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter

Abilov¹,

Hua²,

Matatov³

et al. 2021

Preprint

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The wide spread of unfounded election fraud claims surrounding the U.S. 2020 election had resulted in undermining of trust in the election, culminating in violence inside the U.S. capitol. Under these circumstances, it is critical to understand discussions surrounding these claims on Twitter, a major platform where the claims disseminate. To this end, we collected and release the VoterFraud2020 dataset, a multi-modal dataset with 7.6M tweets and 25.6M retweets from 2.6M users related to voter fraud claims. To make this data immediately useful for a wide area of researchers, we further enhance the data with cluster labels computed from the retweet graph, user suspension status, and perceptual hashes of tweeted images. We also include in the dataset aggregated information for all external links and YouTube videos that appear in the tweets. Preliminary analyses of the data show that Twitter's ban actions mostly affected a specific community of voter fraud claim promoters, and exposes the most common URLs, images and YouTube videos shared in the data.

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Section: Data Enhancementmentioning

confidence: 99%

VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter

Abilov¹,

Hua²,

Matatov³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The problem studied by Bisenius, Bergamini, Angriman, and Meyerhenke (2018) has three differences with the problem studied by Kas et al (2013) and Sariyuce et al (2013). First, it aims to compute approximate closeness scores, rather than exact scores.…”

Section: Dynamical Network Analysis Algorithmsmentioning

confidence: 92%

Dynamical algorithms for data mining and machine learning over dynamic graphs

Chehreghani

2020

WIREs Data Min & Knowl

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In many modern applications, the generated data is a dynamic network. These networks are graphs that change over time by a sequence of update operations (node addition, node deletion, edge addition, edge deletion, and edge weight change). In such networks, it is inefficient to compute from scratch the solution of a data mining/machine learning task, after any update operation. Therefore in recent years, several so‐called dynamical algorithms have been proposed that update the solution, instead of computing it from scratch. In this paper, first we formulate this emerging setting and discuss its high‐level algorithmic aspects. Then, we review state of the art dynamical algorithms proposed for several data mining and machine learning tasks, including frequent pattern discovery, betweenness/closeness/PageRank centralities, clustering, classification, and regression. This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Big Data Mining

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“…A simple adaption of the static closeness algorithm from [3] would be impossible because it does not take the temporal features, like temporal edges with transition times or waiting times at vertices, into account. In [4] Bisenius et al extend the same framework to dynamic graphs in which edges can be added and removed. It allows efficient updates of the static closeness after edge insertions or deletions.…”

Section: Differences To the Conference Versionmentioning

confidence: 99%

Computing top-k temporal closeness in temporal networks

Oettershagen

Mutzel

2022

Knowl Inf Syst

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The closeness centrality of a vertex in a classical static graph is the reciprocal of the sum of the distances to all other vertices. However, networks are often dynamic and change over time. Temporal distances take these dynamics into account. In this work, we consider the harmonic temporal closeness with respect to the shortest duration distance. We introduce an efficient algorithm for computing the exact top-k temporal closeness values and the corresponding vertices. The algorithm can be generalized to the task of computing all closeness values. Furthermore, we derive heuristic modifications that perform well on real-world data sets and drastically reduce the running times. For the case that edge traversal takes an equal amount of time for all edges, we lift two approximation algorithms to the temporal domain. The algorithms approximate the transitive closure of a temporal graph (which is an essential ingredient for the top-k algorithm) and the temporal closeness for all vertices, respectively, with high probability. We experimentally evaluate all our new approaches on real-world data sets and show that they lead to drastically reduced running times while keeping high quality in many cases. Moreover, we demonstrate that the top-k temporal and static closeness vertex sets differ quite largely in the considered temporal networks.

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Computing Top-k Closeness Centrality in Fully-dynamic Graphs

Cited by 22 publications

References 21 publications

VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter

VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter

Dynamical algorithms for data mining and machine learning over dynamic graphs

Computing top-k temporal closeness in temporal networks

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