The spreading process of rumor is different from that of general messages because two special factors: reason of individual and rumor refuting, affect the process of rumor dissemination besides conventional factor, ie information amount In this paper, we propose a genetics-based rumor diffusion model (GRDM) which regards an individual with multiple rumors in a network as a ‘chromosome’ which is composed by a set of genes The GRDM specifies a rule for interactions between chromosomes to model the rumor interactions between individuals A series of experiments are done on the dynamic social network dataset collected from Sina-Weibo with 9299 users and 215386 pieces of following relationship information between them The experimental results show that the genetic-algorithm-based rumor diffusion model is reasonable and feasible in demonstrating the diffusion of rumor in social networks and some key factors, ie starting node, individual reason and rumor refuting, would affect the propagation process

A new genetic-based rumor diffusion model for social networks

In this work, a new method employs Bioinspired Computational (BIC) optimization from the genetic algorithm, bat algorithm, and flower pollination algorithm. Robust and accurate modeling of the input parameters adjusts the propagation models Stanford University Interim, Electronic Communication Committee, and Floating Interception that consider environments with characteristics specifically of urban regions in the Amazon. The lack of research related to the development of propagation models for Amazonian environments motivated this work. Thus, this application proves the effectiveness of using BIC techniques for modeling the communication channel. Measurement campaigns were carried out in the city of Belem, Brazil, for large-scale channel modeling on the frequencies of 1.8 and 2.6 GHz, belonging to the long-term evolution or fourth-generation mobile communications system (4G). After being adjusted by the optimum values calculated by the BIC techniques used, the models showed better results compared to modeling without optimization. Additionally, it was verified an error reduction of about 80% concerning the metrics root-mean-square error and standard deviation.

Adjusting Large-Scale Propagation Models for the Amazon Region Using Bioinspired Algorithms at 1.8 and 2.6 GHz Frequencies

Currently, the popularity of various social media platforms has made convenient communications and prompt information spreading in Online Social Networks (OSNs). However, it brings a serious threat to social stability and public safety when information is identified as false, misleading or inappropriate. Therefore, it’s valuable that the timely and real-time rumor refutations are respectively studied to promptly and continually block the rumor spreading. Existing works on the rumor refutations are lacking in sufficient decomposition on the trust mechanism and ignore the analysis of continual blocking the rumor spreading. Motivated by these issues, this paper proposes a comprehensive Hybrid Clustered Shuffled Frog-Leaping Algorithm-Particle Swarm Optimization (HCSFLA-PSO) algorithm for promptly and continually blocking the rumor spreading in OSNs. Firstly, by making decomposition of social relations and analyzing degree of intimacy, self-reliability and credibility, a new trust mechanism of rumor refutations and a novel expression of trust degree are proposed. Secondly, a comprehensive HCSFLA-PSO algorithm is proposed by taking advantage of the clustered local search capability of the SFLA and the fast-convergence of the PSO algorithm collaborative for the rumor refutations, which includes CNP-HCSFLA-PSO sub-algorithm for the timely rumor refutation and CP-HCSFLA-PSO sub-algorithm for the real-time rumor refutation during the truth evolution according to different social relations with different trust degrees. Thirdly, a new concept of timeliness of information and a novel energy consumption model are proposed to solve the influence problem of continual updated truths. Finally, numerical simulations and ablation experiments are conducted to verify the effectiveness of our proposed algorithm. 

A Hybrid Clustered SFLA-PSO algorithm for optimizing the timely and real-time rumor refutations in Online Social Networks

Modeling information diffusion for social networks enables analyzing and predicting the impact on a specific topic or event. Most existing information diffusion models only take information influence or diffusion among the network users independently into consideration, which can hardly depict the diffusion in a booming large-scale and complex social network. In this paper, we propose a unified information diffusion model which jointly combines information influence and the diffusion process. Based on the unified model, most existing diffusion models can be reconstructed and implemented easily by setting influence and diffusion functions. In this paper, we use this model reconstructed five traditional models and we evaluate the model with a real collected dataset of 2.2 million blogs and the results show that the unified model can be adopted easily to fulfill classic diffusion models. Furthermore, the model sheds light on designing novel explainable information model in the future.

A Unified Information Diffusion Model for Social Networks

LTE UL has been identified as the coverage and capacity bottleneck compared to DL, according to the comprehensive 3GPP studies and field trials. As an effective and promising solution for UL performance enhancements, BS 8-antenna receiver is being more and more widely deployed in LTE commercial networks. To verify the baseband demodulation ability of commercial BS products, a work item has been started to specify demodulation requirements for BS 8-antenna receiver in 3GPP LTE-Advanced Release 12. This paper firstly analyzes the performance gain of BS 8-antenna receiver in terms of UL capacity improvement, coverage extension as well as UE transmission power reduction, and the importance of specifying global demodulation requirements in 3GPP. Then detailed discussion and proposals on the key aspects of developing the performance requirements are presented, including: 1) the channel modeling methodology and derivation of MIMO spatial correlation matrices for 8-antenna reception; 2) how to determine the test requirements for LTE UL physical channels. It is shown that commercial BS products with 8-antenna receiver can achieve 3.0dB, 3.2dB and 3.2dB average gain respectively for PUSCH, PUCCH and PRACH, with respect to 4-antenna receiver.

Performance verification of uplink 8-antenna receiver in LTE-Advanced Release 12

the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of diffusion: gradually changing of message contents due to the `new' and `comment' mechanisms. A novel genetic-algorithm-based information evolution model is proposed to reproduce both the diffusion and development process of information in social networks. This model firstly proposes a five-tuple to represent three types of topics: independent, competitive and mutually exclusive. Furthermore, it adopts mutation operator and forms new crossover and mutation rules to simulate four typical interactions between individuals, which bring the advantage of reproducing the information evolution process in both popularity and content. A series of experiments tested on public datasets demonstrate that: (1) independent and competitive topics of information rarely affect each other while mutually exclusive topics significantly suppress the diffusion processes of each other; (2) lower mutation probability leads to decreasing of final information amount. The experimental results show that our evolution model is more reasonable and feasible in demonstrating the evolution of information in social networks.

http://ieeexplore.ieee.org/iel7/6245522/7897533/07897547.pdf

A genetic-algorithm-based information evolution model for social networks

Study on diffusion behaviors, such as contagion of virus, adoption of production, and information propagation, is one of the hottest topics of social network research. The conventional models usually focus on a particular factor that influences the contagion process. A novel model of diffusion based on multiple behavior evidence fusion is proposed on the analysis of prevalent social network data set, which can simulate the information propagation process with the fitting goodness of 0.79. In this model, impact factors, i.e. the influence between neighborhood, and the activity of user, are taken into account together and fused into combined evidence mass function, which can be used to infer users' forwarding behavior by D-S theory reasoning. Series of experiments tested on the published Enron email data set show that the multiple evidence fusion based diffusion model (MEFDM) is reasonable and feasible in demonstrating the cascading of information in the social network.

Wanyu Huang

Papers

A genetic-algorithm-based information evolution model for social networks

Multiple evidence fusion based information diffusion model for social network