Spreading predictability in complex networks

AbstractMany state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infection probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this report, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future, not just the macro scale of infection. Experimental results on synthetic and real networks demonstrate that the infected individuals predicted by the model have good consistency with the actual infected ones based on simulations.

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Spreading Predictability in Complex Networks

10.21203/rs.3.rs-132928/v1 ◽

2021 ◽

Author(s):

Na Zhao ◽

Jian Wang ◽

Yong Yu ◽

Junyan Zhao ◽

Duanbing Chen

Keyword(s):

Infectious Diseases ◽

Prediction Model ◽

Complex Networks ◽

State Of The Art ◽

Experimental Results ◽

Vaccination Strategies ◽

Model Based ◽

The Future ◽

Rumor Control

Abstract Many state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infected probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this paper, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future. Experimental results on synthetic and real networks demonstrate that the predicted infected individuals have rather consistency with the actual infected ones.

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Spreading predictability in complex networks

10.1101/2020.01.28.922757 ◽

2020 ◽

Cited By ~ 2

Author(s):

Na Zhao ◽

Jian Wang ◽

Yong Yu ◽

Jun-Yan Zhao ◽

Duan-Bing Chen

Keyword(s):

Prediction Model ◽

Complex Networks ◽

State Of The Art ◽

Viral Marketing ◽

Experimental Results ◽

Dynamics Analysis ◽

Interesting Topic ◽

Spreading Dynamics

AbstractSpreading dynamics analysis is an important and interesting topic since it has many applications such as rumor or disease controlling, viral marketing and information recommending. Many state-of-the-art researches focus on predicting infection scale or threshold. Few researchers pay attention to the predicting of infection nodes from a snapshot. With developing of precision marketing, recommending and, controlling, how to predict infection nodes precisely from snapshot becomes a key issue in spreading dynamics analysis. In this paper, a probability based prediction model is presented so as to estimate the infection nodes from a snapshot of spreading. Experimental results on synthetic and real networks demonstrate that the model proposed could predict the infection nodes precisely in the sense of probability.

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Improved Local Search with Momentum for Bayesian Networks Structure Learning

Entropy ◽

10.3390/e23060750 ◽

2021 ◽

Vol 23 (6) ◽

pp. 750

Author(s):

Xiaohan Liu ◽

Xiaoguang Gao ◽

Zidong Wang ◽

Xinxin Ru

Keyword(s):

Local Search ◽

Complex Networks ◽

Bayesian Networks ◽

Structure Learning ◽

Hybrid Methods ◽

State Of The Art ◽

The State ◽

Experimental Results ◽

Local Optimum ◽

Networks Structure

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.

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A new prediction model of infectious diseases with vaccination strategies based on evolutionary game theory

Chaos Solitons & Fractals ◽

10.1016/j.chaos.2017.07.022 ◽

2017 ◽

Vol 104 ◽

pp. 51-60 ◽

Cited By ~ 14

Author(s):

Qiu Li ◽

MingChu Li ◽

Lin Lv ◽

Cheng Guo ◽

Kun Lu

Keyword(s):

Game Theory ◽

Infectious Diseases ◽

Prediction Model ◽

Evolutionary Game Theory ◽

Evolutionary Game ◽

Vaccination Strategies

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A Theoretical Revolution in Corrections

Criminal Justice and Behavior ◽

10.1177/0093854888015001010 ◽

1988 ◽

Vol 15 (1) ◽

pp. 125-136 ◽

Cited By ~ 10

Author(s):

DORIS LAYTON MacKENZIE ◽

C. DALE POSEY ◽

KAREN R. RAPAPORT

Keyword(s):

Prediction Model ◽

Paradigm Shift ◽

State Of The Art ◽

Medical Model ◽

The State ◽

Classification Systems ◽

Special Issue ◽

Current Trends ◽

The Future

The articles in the special issue are reviewed within the context of current trends and the state of the art in prison classification systems. A paradigm shift is noted to be occurring within the field, wherein the medical model is being abandoned for a multipurpose model of classification. The purposes are understanding, prediction, management, and treatment. Each of these purposes is discussed with special emphasis on problems inherent in the prediction model when used in isolation. Implications of the paradigm shift are discussed in terms of the future of psychologists' roles within corrections.

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TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3443687 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1-17

Author(s):

Luyi Bai ◽

Xiangnan Ma ◽

Mingcheng Zhang ◽

Wenting Yu

Keyword(s):

Artificial Intelligence ◽

Prediction Model ◽

State Of The Art ◽

Attention Mechanism ◽

Experimental Results ◽

Knowledge Graph ◽

Modeling Process ◽

Knowledge Graphs

Temporal knowledge graphs (TKGs) have become useful resources for numerous Artificial Intelligence applications, but they are far from completeness. Inferring missing events in temporal knowledge graphs is a fundamental and challenging task. However, most existing methods solely focus on entity features or consider the entities and relations in a disjoint manner. They do not integrate the features of entities and relations in their modeling process. In this paper, we propose TPmod, a tendency-guided prediction model, to predict the missing events for TKGs (extrapolation). Differing from existing works, we propose two definitions for TKGs: the Goodness of relations and the Closeness of entity pairs. More importantly, inspired by the attention mechanism, we propose a novel tendency strategy to guide our aggregated process. It integrates the features of entities and relations, and assigns varying weights to different past events. What is more, we select the Gate Recurrent Unit (GRU) as our sequential encoder to model the temporal dependency in TKGs. Besides, the Softmax function is employed to generate the final decreasing group of candidate entities. We evaluate our model on two TKG datasets: GDELT-5 and ICEWS-250. Experimental results show that our method has a significant and consistent improvement compared to state-of-the-art baselines.

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