Universal and Distinct Properties of Communication Dynamics

Melo, Pedro O. S. Vaz de; Faloutsos, Christos; Assunção, Renato M.; Alves, Rodrigo; Loureiro, Antônio A. F.

doi:10.1145/2700399

Cited by 5 publications

(3 citation statements)

References 34 publications

(41 reference statements)

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“…This distribution seems to appear due to various actions of normal users in shopping such as finding items, examining items, and comparing their prices. Indeed, we confirmed that IATs in CSS follow the log-logistic distribution, well modeled by odds ratio power law, which also coincides with existing theories found in human communication dynamics such as e-mail and SMS communications [7].…”

Section: Inter-arrival Time Differencesupporting

confidence: 90%

Fraud Detection in Comparison-Shopping Services: Patterns and Anomalies in User Click Behaviors

Lee

Faloutsos

Chae

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper deals with a novel, interesting problem of detecting frauds in comparison-shopping services (CSS). In CSS, there exist frauds who perform excessive clicks on a target item. They aim at making the item look very popular and subsequently ranked high in the search and recommendation results. As a result, frauds may distort the quality of recommendations and searches. We propose an approach of detecting such frauds by analyzing click behaviors of users in CSS. We evaluate the effectiveness of the proposed approach on a real-world clickstream dataset. key words: fraud detection, comparison-shopping services, user behavior analysis

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Section: Inter-arrival Time Differencesupporting

confidence: 90%

Fraud Detection in Comparison-Shopping Services: Patterns and Anomalies in User Click Behaviors

Lee

Faloutsos

Chae

et al. 2017

IEICE Trans. Inf. & Syst.

Self Cite

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show abstract

“…Due to the generic nature of the problem, several statistical tools have been developed for estimating IET distributions for renewal processes [34][35][36][37][38][39][40]. Additionally, some techniques based on survival analysis and event-history analysis have been used to analyze temporal network data [41][42][43]. Similar problems have also been encountered when analyzing geological data [44,45] and estimating inter-spike intervals of firing neurons [46].…”

Section: Introductionmentioning

confidence: 99%

Estimating interevent time distributions from finite observation periods in communication networks

Kivelä

Porter

2015

Phys. Rev. E

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A diverse variety of processes-including recurrent disease episodes, neuron firing, and communication patterns among humans-can be described using interevent time (IET) distributions. Many such processes are ongoing, although event sequences are only available during a finite observation window. Because the observation time window is more likely to begin or end during long IETs than during short ones, the analysis of such data is susceptible to a bias induced by the finite observation period. In this paper, we illustrate how this length bias is born and how it can be corrected without assuming any particular shape for the IET distribution. To do this, we model event sequences using stationary renewal processes, and we formulate simple heuristics for determining the severity of the bias. To illustrate our results, we focus on the example of empirical communication networks, which are temporal networks that are constructed from communication events. The IET distributions of such systems guide efforts to build models of human behavior, and the variance of IETs is very important for estimating the spreading rate of information in networks of temporal interactions. We analyze several well-known data sets from the literature, and we find that the resulting bias can lead to systematic underestimates of the variance in the IET distributions and that correcting for the bias can lead to qualitatively different results for the tails of the IET distributions.

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“…Note that the odds ratio is particularly good at capturing differences between distributions at small values of τ [45].…”

Section: Fitting Of the Emm To The Individual With The Largest Number Of Events And Comparison With Power-law Distributionsmentioning

confidence: 99%

Long-tailed distributions of inter-event times as mixtures of exponential distributions

2020

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Inter-event times of various human behavior are apparently non-Poissonian and obey long-tailed distributions as opposed to exponential distributions, which correspond to Poisson processes. It has been suggested that human individuals may switch between different states in each of which they are regarded to generate events obeying a Poisson process. If this is the case, distributions of inter-event times should approximately obey a mixture of exponential distributions with different parameter values. In the present study, we introduce the minimum description length principle to compare mixtures of exponential distributions with different numbers of components (i.e., constituent exponential distributions). Because these distributions violate the identifiability property, one is mathematically not allowed to apply the Akaike or Bayes information criteria to their maximum likelihood estimator to carry out model selection. We overcome this theoretical barrier by applying a minimum description principle to joint likelihoods of the data and latent variables. We show that mixtures of exponential distributions with a few components are selected as opposed to more complex mixtures in various data sets and that the fitting accuracy is comparable to that of state-of-the-art algorithms to fit powerlaw distributions to data. Our results lend support to Poissonian explanations of apparently non-Poissonian human behavior.1

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Universal and Distinct Properties of Communication Dynamics

Cited by 5 publications

References 34 publications

Fraud Detection in Comparison-Shopping Services: Patterns and Anomalies in User Click Behaviors

Fraud Detection in Comparison-Shopping Services: Patterns and Anomalies in User Click Behaviors

Estimating interevent time distributions from finite observation periods in communication networks

Long-tailed distributions of inter-event times as mixtures of exponential distributions

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