An intuitionistic fuzzy method for social network prediction problems

2016 ◽  
Vol 31 (6) ◽  
pp. 3131-3142
Author(s):  
Liming Yang ◽  
Wei Zhang ◽  
Wenqing Liu ◽  
Yunfang Chen
2019 ◽  
Vol 26 (1) ◽  
pp. 103-110
Author(s):  
Hoang Nguyen

Abstract In the safety engineering, the most frequently disadvantage in risk estimation is the lack of data. In such cases, we have to rely on subjective estimations made by persons with practical knowledge in the field of interest, i.e. experts. However, in some realistic situations, they may have uncertainty in the perceiving and evaluation of the problem considered or limited knowledge of the rare events, such as the consequences of the seagoing ship propulsion failures. The probabilistic models of the risk estimation turn out to be insufficient in modelling the subjective uncertainty. The fuzzy methods are viewed to be powerful in dealing with ambiguity and uncertainty that can be used to handle with the subjective estimation. This article addresses the intuitionistic fuzzy method in the subjective estimation of the ship propulsion failure consequences as rare event risk. In the article, a subjective model of the ship propulsion risk is developed as scenarios of the different subsequent consequences of loss of ship propulsion function until a seriously severe accident resulting in loss of seaworthiness. The model proposes an approach combining AHP method and intuitionistic fuzzy method to assess the occurrence probability and severe probability of these rare events based on the expert opinions. In order to show the applicability of the proposed model, a study case of the propulsion risk of the container carrier operating on the North Atlantic lines is conducted.


2018 ◽  
Vol 22 (S4) ◽  
pp. 8099-8108
Author(s):  
Hua Wang ◽  
Maozhu Jin ◽  
Peiyu Ren

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Xiaoliang Chen ◽  
Xiang Lan ◽  
Jihong Wan ◽  
Peng Lu ◽  
Ming Yang

A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N- SEP 2 M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N- SEP 2 M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.


Author(s):  
Bonaventure C. Molokwu ◽  
Ziad Kobti

Social Network Analysis (SNA) has become a very interesting research topic with regard to Artificial Intelligence (AI) because a wide range of activities, comprising animate and inanimate entities, can be examined by means of social graphs. Consequently, classification and prediction tasks in SNA remain open problems with respect to AI. Latent representations about social graphs can be effectively exploited for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. The inherent representations of a social graph are relevant to understanding the nature and dynamics of a given social network. Thus, our research work proposes a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). RLVECN is designed for studying and extracting meaningful representations from social graphs to aid in node classification, community detection, and link prediction problems. RLVECN utilizes an edge sampling approach for exploiting features of the social graph via learning the context of each actor with respect to its neighboring actors.


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