Social networks provide a wealth of data to study relationship dynamics among people. Most social networks such as Epinions and Facebook allow users to declare trusts or friendships with other users. Some of them also allow users to declare distrusts or negative relationships. When both positive and negative links co-exist in a network, some interesting community structures can be studied. In t...

today, online social networks are very popular due to the possibility of creating relationships between people all over the world. these social networks with possibilities such as friend recommendation generally use local features derived from social graph structure. for friend recommendation, there are different algorithms with local and global approaches. in this paper, we proposed an algorit...

With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these applications to user-specific needs or preferences. Despite their increasing popularity, in general recommender systems suffer from the data sparsity and the cold-start problems. To alleviate these issues, in re...

Many real-world person-person or person-product relationships can be modeled graphically. Specifically, bipartite graphs are especially useful when modeling scenarios involving two disjoint groups. As a result, existing papers have utilized to address the classical link recommendation problem. Applying principle of graphs, this research presents modified approach problem which employs two-step ...

Introduction: The Iranian Brain Imaging Database (IBID) was initiated in 2017, with 5 major goals: provide researchers easy access to a neuroimaging database, provide normative quantitative measures of the brain for clinical research purposes, study the aging profile of the brain, examine the association of brain structure and function, and join the ENIGMA consortium. Many prestigious databases...

Collaborative filtering (CF) recommender systems are typically unable to generate adequate recommendations in sparse datasets. Empirical evidence suggests that incorporation of a trust network among the users of a recommender system can significantly help to alleviate this problem. For this reason, some studies have been done on combining CF with trust-enhanced recommender system. In this study...

In the age of information overload, collaborative filtering and recommender systems have become essential tools for content discovery. The advent of online social networks has added another approach to recommendation whereby the social network itself is used as a source for recommendations i.e. users are recommended items that are preferred by their friends. In this paper we develop a new model...

Since more and more customers get interested in ecommerce, marketing strategy on the virtual community becomes a major issue. It is important to help companies to determine which potential customers to market to. In this paper, we study the trust relationships between customers on the virtual community and detect influential target groups of interest. We analyze Epinions, a large and popular on...

The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users' purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendati...