Community-Based Event Detection in Temporal Networks

Moriano, Pablo; Finke, Jorge; Ahn, Yong‐Yeol

doi:10.1038/s41598-019-40137-0

Cited by 18 publications

(30 citation statements)

References 26 publications

(18 reference statements)

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“…Moriano and co-authors state in their paper [64] that "Global events trigger viral information cascades that easily cross community boundaries and can thus be detected by monitoring intra-and inter-community communications." ey showed, when a global event (Boston Marathon bombing) occurs, it spreads virally, crossing community boundaries and producing more intercommunity.…”

Section: Resultsmentioning

confidence: 99%

Self‐Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

Dmitriev

Balybin

2019

Complexity

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Recently, there has been an increasing number of empirical evidence supporting the hypothesis that spread of avalanches of microposts on social networks, such as Twitter, is associated with some sociopolitical events. Typical examples of such events are political elections and protest movements. Inspired by this phenomenon, we built a phenomenological model that describes Twitter’s self-organization in a critical state. An external manifestation of this condition is the spread of avalanches of microposts on the network. The model is based on a fractional three-parameter self-organization scheme with stochastic sources. It is shown that the adiabatic mode of self-organization in a critical state is determined by the intensive coordinated action of a relatively small number of network users. To identify the critical states of the network and to verify the model, we have proposed a spectrum of three scaling indicators of the observed time series of microposts.

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Section: Resultsmentioning

confidence: 99%

Self‐Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

Dmitriev

Balybin

2019

Complexity

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“…The suggested method used for event detection is motivated by the fact that viral information spreading has distinct diffusion/spread patterns with respect to the community structures. Specifically, Moriano et al [152] suggest that global events trigger viral information cascades and can thus be detected by monitoring intra-and inter-community communications that occur across the community boundaries. By comparing the expanse of communication patterns among intra-and inter-communities, authors show that it is possible to detect several types of events, even when they do not trigger a significantly larger communication among users.…”

Section: Community-based Event Detection In Temporal Network Via Gramentioning

confidence: 99%

“…By comparing the expanse of communication patterns among intra-and inter-communities, authors show that it is possible to detect several types of events, even when they do not trigger a significantly larger communication among users. A schematic representation of the proposed event detection method taken from the Moriano et al [152] study is shown in Figure 43. From Figure 43, it can be observed that for each network, nodes are belonging to the same communities but different patterns of communication within and across communities appear.…”

Section: Community-based Event Detection In Temporal Network Via Gramentioning

confidence: 99%

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Majeed

Rauf

2020

Inventions

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Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.

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“…• Analysis of the political representativeness of Twitter users [3] • Real-time Twitter analysis [4][5][6] • Democratic elections [7][8][9][10][11][12][13] • The uses of Twitter by populists [14] • Misinformation dissemination and event detection in social networks [15][16][17] • Online public shaming [18] • and many more Furthermore, there exist plenty of public available datasets. E.g., a curated collection [19] is hosted on Zenodo [20] and covers several datasets of political campaigns [8,9], online misinformation networks [21], event detection in temporal networks [22], public shaming [23], Twitter-related word vectors [24], retweeting timeseries [25], and even continuously updated samples of a nations-wide Twitter usage [26].…”

Section: Introductionmentioning

confidence: 99%

Redundancy Reduction in Twitter Event Streams

Kratzke¹

2020

Preprint

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The data from social networks like Twitter is a valuable source for research but full of redundancy, making it hard to provide large-scale, self-contained, and small datasets. The data recording is a common problem in social media-based studies and could be standardized. Sadly, this is hardly done. This paper reports on lessons learned from a long-term evaluation study recording the complete public sample of the German and English Twitter stream. It presents a recording solution proposal that merely chunks a linear stream of events to reduce redundancy. If events are observed multiple times within the time-span of a chunk, only the latest observation is written to the chunk. A 10 Gigabyte Twitter raw dataset covering 1,2 Million Tweets of 120.000 users recorded between June and September 2017 was used to analyze expectable compression rates. It turned out that resulting datasets need only between 10\% and 20\% of the original data size without losing any event, metadata or the relationships between single events. This kind of redundancy reduction recording makes it possible to curate large-scale (even nation-wide), self-contained, and small datasets of social networks for research in a standardized and reproducible manner.

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Community-Based Event Detection in Temporal Networks

Cited by 18 publications

References 26 publications

Self‐Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

Self‐Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Redundancy Reduction in Twitter Event Streams

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