ﻣﺤﺎﺳﺒﻪ ﻣﯿﺰان اﻋﺘﻤﺎد در ﺷﺒﮑﻪﻫﺎي اﺟﺘﻤﺎﻋﯽ ﺑﺮﺧﻂ ﺑﺎ اﺳﺘﻔﺎده از اﻟﮕﻮرﯾﺘﻢ ﺑﻬﯿﻨﻪﺳﺎزي ازدﺣﺎم ذرات

calculating trust in online social networks using particale swarm optimization algorithms

ﺑﺎ ﮔﺴﺘﺮش ﺷﺒﮑﻪ اﯾﻨﺘﺮﻧﺖ و ﻓﺮاﮔﯿﺮ ﺷﺪن اﺳﺘﻔﺎده از آن، ﺷﺒﮑﻪﻫﺎي اﺟﺘﻤﺎﻋﯽ ﺑﻄﻮر ﻗﺎﺑﻞ ﺗﻮﺟﻬﯽ ﮔﺴﺘﺮش ﯾﺎﻓﺘﻪاﻧﺪ. ﻃﺒﯿﻌﯽ اﺳﺖ ﮐﻪ ﺗﻤﺎﻣﯽ اﻃﻼﻋﺎت و ﻣﺤﺘﻮاﻫﺎي ﻣﻮﺟﻮد در اﯾﻦ ﺷﺒﮑﻪﻫﺎ ﻣﻮرد اﻋﺘﻤﺎد و دﻟﺨﻮاه ﻫﺮ ﺷﺨﺼﯽ ﻧﯿﺴﺖ و ﺷﺒﮑﻪﻫﺎﯾﯽ از ﻧﻈﺮ ﮐﺎرﺑﺮان ﻣﻄﻠﻮب ﻫﺴﺘﻨﺪ ﮐﻪ ﺑﺘﻮاﻧﻨﺪ اﻃﻼﻋﺎت اراﺋﻪﺷﺪه ﺗﻮﺳﻂ اﻓﺮاد ﻣﻌﺘﻤﺪ را ﺑﻪ آﻧﻬﺎ اراﺋﻪ دﻫﻨﺪ. در ﺷﺒﮑﻪﻫﺎي اﺟﺘﻤﺎﻋﯽ ﻣﺒﺘﻨﯽ ﺑﺮ وب، ﮐﺎرﺑﺮان ﻣﯽﺗﻮاﻧﻨﺪ ﻣﻘﺪار اﻋﺘﻤﺎد را ﺑﺮاي ﮐﺎرﺑﺮاﻧﯽ ﮐﻪ ﺑﺎ آﻧﻬﺎ ارﺗﺒﺎط ﺑﺮﻗﺮار ﮐﺮدهاﻧﺪ، ﻣﺸﺨﺺ ﮐﻨﻨﺪ. ﭘﺲ از ﻣﺸﺨﺺ ﺷﺪن ﻣﯿﺰان اﻋﺘﻤﺎد، ﮐﺎﺑﺮان ﺷﺒﮑﻪﻫﺎي اﺟﺘﻤﺎﻋﯽ ﺗﺼﻤﯿﻤﺎت ﺧﻮد را در راﺑﻄﻪ ﺑﺎ ﮐﺎرﺑﺮان ﻣﺨﺘﻠﻒ ﺑﺮاﺳﺎس ﻣﯿﺰان اﻋﺘﻤﺎد ﻣﺸﺨﺺ ﺷﺪه ﻣﯽﮔﯿﺮﻧﺪ. ﺑﻨﺎﺑﺮاﯾﻦ، ﯾﮑﯽ از ﻣﻔﺎﻫﯿﻢ ﻣﻬﻢ ﻣﻮرد ﺑﺤﺚ در ﺷﺒﮑﻪﻫﺎي اﺟﺘﻤﺎﻋﯽ ﺑﺮﺧﻂ، اﻋﺘﻤﺎد و ﻣﺤﺎﺳﺒﻪ ﻣﺴﯿﺮ اﻋﺘﻤﺎد ﺑﯿﻦ ﮐﺎرﺑﺮان ﺷﺒﮑﻪ اﺟﺘﻤﺎﻋﯽ اﺳﺖ ﮐﻪ ﻧﻘﺶ ﻣﻬﻤﯽ در اﻧﺠﺎم ﺗﻌﺎﻣﻼت ﺑﯿﻦ آﻧﻬﺎ دارد. ﻣﺤﻘﻘﯿﻦ ﺷﺒﮑﻪﻫﺎي اﺟﺘﻤﺎﻋﯽ ﻣﺪلﻫﺎ و روشﻫﺎي ﻣﺨﺘﻠﻔﯽ را ﺑﺮاي ﻣﺤﺎﺳﺒﻪ ﻣﯿﺰان اﻋﺘﻤﺎد در ﺷﺒﮑﻪﻫﺎي اﺟﺘﻤﺎﻋﯽ ﺑﺮﺧﻂ اراﺋﻪ ﮐﺮدهاﻧﺪ. در اﯾﻦ ﺗﺤﻘﯿﻖ ﻧﯿﺰ ﯾﮏ روﯾﮑﺮد ﺟﺪﯾﺪ ﺑﺮاي ﻣﺤﺎﺳﺒﻪ ﺣﺪاﮐﺜﺮاﻋﺘﻤﺎد ﺑﯿﻦ ﮐﺎرﺑﺮان در ﺷﺒﮑﻪ اﺟﺘﻤﺎﻋﯽ ﺑﺎ ﺳﺎﺧﺘﺎر ﻣﺸﺎﺑﻪ ﻓﯿﺴﺒﻮك اراﯾﻪ ﮔﺮدﯾﺪه و ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ NP-Hardﺑﻮدن ﻣﺴﺌﻠﻪ، ﯾﮏ روﯾﮑﺮد ﻓﺮااﺑﺘﮑﺎري ﺑﺎ اﺳﺘﻔﺎده از اﻟﮕﻮرﯾﺘﻢ ﺑﻬﯿﻨﻪﺳﺎزي ازدﺣﺎم ذرات ﺑﺮاي ﺣﻞ آن ﭘﯿﺸﻨﻬﺎد داده ﺷﺪه اﺳﺖ. روش ﭘﯿﺸﻨﻬﺎدي در ﻣﺤﯿﻂ ﻧﺮم اﻓﺰار ﻣﺘﻠﺐ ﺷﺒﯿﻪﺳﺎزي ﮔﺮدﯾﺪه و ﻧﺘﺎﯾﺞ آزﻣﺎﯾﺸﺎت ﺣﺎﮐﯽ از اﯾﻦ اﺳﺖ ﮐﻪ روﯾﮑﺮد ﭘﯿﺸﻨﻬﺎدي ﮐﺎراﯾﯽ ﺑﺎﻻي در ﻣﺤﺎﺳﺒﻪ ﻣﯿﺰان اﻋﺘﻤﺎد در ﺷﺒﮑﻪﻫﺎي اﺟﺘﻤﺎﻋﯽ ﺑﺮﺧﻂ دارد.

Online social networks users interact, play a key role in the analysis social networks. One of the important concepts discussed in social networks, trust and confidence between the calculating social network users that played an important role in the interactions between them. This concept and its calculation method, depend on the structure and social networks parameters, Studies by researchers in this field has been done. In this dissertation, a new method for calculating the maximum trust between users on the social network Facebook with the same structure has been provided, and due to the computational complexity of the issue, a meta-heuristic method has been developed based on genetic algorithm to solve it. The proposed method simulation in MATLAB environment andhas been implemented on the data sample. The computational results show the effectiveness of the proposed method and its high rate of convergence.