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1 - Social Networks Embedding Based on the Employment of Community Recognition and Latent Semantic Feature Extraction Approaches
Mohadeseh Taherparvar Fateme Ahmadi abkenari Peyman bayat

The purpose of embedding social networks, which has recently attracted a lot of attention, is to learn to display in small dimensions for each node in the network while maintaining the structure and characteristics of the network. In this paper, we propose the effect of More

The purpose of embedding social networks, which has recently attracted a lot of attention, is to learn to display in small dimensions for each node in the network while maintaining the structure and characteristics of the network. In this paper, we propose the effect of identifying communities in different situations such as community detection during or before the process of random walking and also the effect of semantic textual information of each node on network embedding. Then two main frameworks have been proposed with community and context aware network embedding and community and semantic feature-oriented network embedding. In this paper, in community and context aware network embedding, the detection of communities before the random walk process, is performed through using the EdMot non-overlapping method and EgoNetSplitter overlapping method. However, in community and semantic feature-oriented network embedding, the recognition of communities during a random walk event is conducted using a Biterm topic model. In all the proposed methods, text analysis is examined and finally, the final display is performed using the Skip-Gram model in the network. Experiments have shown that the methods proposed in this paper work better than the superior network embedding methods such as Deepwalk, CARE, CONE, and COANE and have reached an accuracy of nearly 0.9 and better than other methods in terms of edge prediction criteria in the network. Manuscript profile
Open Access Article
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2 - Friendship Selection Based on Social Features in Social Internet of Things
Mohammad Mahdian S.Mojtaba Matinkhah

The Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right More

The Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right service is demanding. Great number of objects' friends, in classical algorithms, causes increasing the computational time and load of network navigation to find the right service with the help of friendly objects. In this article, in order to reduce the computational load and network navigation, it is proposed, firstly, to select the appropriate object friend from a heuristic approach; secondly, to use an adapted binary cuckoo optimization algorithm (AB-COA) which tries to select the appropriate friendly object to receive the service according to the maximum response capacity of each friendly object, and finally, adopting the Adamic-Adar local index (AA) with the interest degree centrality criterion so that it represents the characteristics of the common neighbors of the objects are involved in the friend selection. Finally, by executing the proposed algorithm on the Web-Stanford dataset, an average of 4.8 steps was obtained for reaching a service in the network, indicating the superiority of this algorithm over other algorithms. Manuscript profile

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List of Articles social network