信息网络安全
信息網絡安全
신식망락안전
NETINFO SECURITY
2014年
1期
5-9
,共5页
社交网络%谣言发现%谣言传播%控制策略
社交網絡%謠言髮現%謠言傳播%控製策略
사교망락%요언발현%요언전파%공제책략
social networks%rumor identiifcation%rumor propagation%rumor control
社交网络中谣言的肆意传播给网络安全以及社会稳定带来了全新的挑战,如何科学地认识和掌握谣言传播、扩散的内在规律,并对谣言进行有效地控制具有非常重要的学术意义和社会意义。文章首先在充分分析目前谣言传播模型的基础上,引入谣言正向感染及负向感染两个感染状态,提出更适用于描述谣言传播的SPNR模型;其次,基于SPNR模型,设计谣言传播SPNR算法,实现谣言传播演化过程的仿真;另外,利用数值仿真的方法,分析了模型主要参数对谣言传播关键指标的影响效果,为制定有效的谣言控制策略提供了可靠的依据;最后,从定性和定量两个角度验证了SPNR模型基本假设的准确性,同时通过将SPNR模型模拟效果与新浪微博实证结果进行对比试验的方式,验证了SPNR谣言传播模型的适用性。
社交網絡中謠言的肆意傳播給網絡安全以及社會穩定帶來瞭全新的挑戰,如何科學地認識和掌握謠言傳播、擴散的內在規律,併對謠言進行有效地控製具有非常重要的學術意義和社會意義。文章首先在充分分析目前謠言傳播模型的基礎上,引入謠言正嚮感染及負嚮感染兩箇感染狀態,提齣更適用于描述謠言傳播的SPNR模型;其次,基于SPNR模型,設計謠言傳播SPNR算法,實現謠言傳播縯化過程的倣真;另外,利用數值倣真的方法,分析瞭模型主要參數對謠言傳播關鍵指標的影響效果,為製定有效的謠言控製策略提供瞭可靠的依據;最後,從定性和定量兩箇角度驗證瞭SPNR模型基本假設的準確性,同時通過將SPNR模型模擬效果與新浪微博實證結果進行對比試驗的方式,驗證瞭SPNR謠言傳播模型的適用性。
사교망락중요언적사의전파급망락안전이급사회은정대래료전신적도전,여하과학지인식화장악요언전파、확산적내재규률,병대요언진행유효지공제구유비상중요적학술의의화사회의의。문장수선재충분분석목전요언전파모형적기출상,인입요언정향감염급부향감염량개감염상태,제출경괄용우묘술요언전파적SPNR모형;기차,기우SPNR모형,설계요언전파SPNR산법,실현요언전파연화과정적방진;령외,이용수치방진적방법,분석료모형주요삼수대요언전파관건지표적영향효과,위제정유효적요언공제책략제공료가고적의거;최후,종정성화정량량개각도험증료SPNR모형기본가설적준학성,동시통과장SPNR모형모의효과여신랑미박실증결과진행대비시험적방식,험증료SPNR요언전파모형적괄용성。
The malevolent spreading of rumors on social networks put forward a new challenge to the security of network and society. How to grasp the inherent laws of rumor propagation and propose effective control strategies for rumors has important practical signiifcant. Firstly, based on the detailed analysis of epidemic spreading model, this paper proposes a more suitable rumor propagation model-SPNR by dividing infected states (I) into positive infected (P) and negative infected (N). Secondly, based on the SPNR model, designs the algorithm for SPNR model and accomplishes the simulation of rumor propagation process. Thirdly, further analyzes the key factors of affecting the maximum value of steady state, the point of decline, and the life cycle of a rumor. These results have important signiifcant in developing new rumor control strategies. Lastly, after evaluating the proposed model with simulations and comparing the simulation results with real data on Sina Weibo, the experimental results shows that the new model is effective for capturing the rumor spreading in real social networks.