Burstiness Scale

Alves, Rodrigo; Assunção, Renato M.; Melo, Pedro O. S. Vaz de

doi:10.1145/2939672.2939852

Cited by 8 publications

(1 citation statement)

References 23 publications

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“…connections is subject to burstiness, which yields temporal clusters of connections separated by latency periods [3,8,27,37,40,46,85]. These two dynamics are often intertwined through memory and self-excitement mechanisms, such that the temporal evolution of the system depends on the evolution of the characteristics of the single individuals, and vice versa [87].…”

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confidence: 99%

Modeling Memory Effects in Activity-Driven Networks

Zino¹,

Rizzo²,

Porfiri³

2018

SIAM J. Appl. Dyn. Syst.

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confidence: 99%

Modeling Memory Effects in Activity-Driven Networks

Zino¹,

Rizzo²,

Porfiri³

2018

SIAM J. Appl. Dyn. Syst.

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Social Network Monitoring for Bursty Cascade Detection

Xie

Zhu

Xiao

et al. 2018

ACM Trans. Knowl. Discov. Data

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Social network services have become important and efficient platforms for users to share all kinds of information. The capability to monitor user-generated information and detect bursts from information diffusions in these social networks brings value to a wide range of real-life applications, such as viral marketing. However, in reality, as a third party, there is always a cost for gathering information from each user or so-called social network sensor. The question then arises how to select a budgeted set of social network sensors to form the data stream for burst detection without compromising the detection performance. In this article, we present a general sensor selection solution for different burst detection approaches. We formulate this problem as a constraint satisfaction problem that has high computational complexity. To reduce the computational cost, we first reduce most of the constraints by making use of the fact that bursty cascades are rare among the whole population. We then transform the problem into an Linear Programming (LP) problem. Furthermore, we use the sub-gradient method instead of the standard simplex method or interior-point method to solve the LP problem, which makes it possible for our solution to scale up to large social networks. Evaluating our solution on millions of real information cascades, we demonstrate both the effectiveness and efficiency of our approach.

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Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction

Yang

Zhang

et al. 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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This paper targets a general popularity prediction problem for event sequence, which has recently gained great attention due to its extensive applications in various domains. Feature driven method and point process method are two basic thinking paradigms to tackle the prediction problem, but both of them suffer from limitations. In this paper, we propose PreNets unifying the two thinking paradigms in an adversarial manner. On one side, feature driven model acts like a 'critic' who aims to discriminate the predicted popularity from the real one based on a set of temporal features from the sequence. On the other side, point process model acts like an 'interpreter' who recognizes the dynamic patterns in sequence to generate a predicted popularity that can fool the 'critic'. Through a Wasserstein learning based two-player game, the training loss of the 'critic' guides the 'interpreter' to better exploit the sequence patterns and enhance prediction, while the 'interpreter' pushes the 'critic' to select effective early features that helps discrimination. This mechanism enables the framework to absorb the advantages of both feature driven and point process methods. Empirical results show that PreNets achieves significant MAPE improvement for both Twitter cascade and Amazon review prediction.

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Burstiness Scale

Cited by 8 publications

References 23 publications

Modeling Memory Effects in Activity-Driven Networks

Modeling Memory Effects in Activity-Driven Networks

Social Network Monitoring for Bursty Cascade Detection

Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction

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