Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples of absent edges, which creates a difficulty for many existing supervised learning approaches. We develop a new method which treats the observed network as a sample of the true network with different sampling rates f...

Evaluation of link prediction methods is a hard task in very large complex networks because of the inhibitive computational cost. By setting a lower bound of the number of common neighbors (CN), we propose a new framework to efficiently and precisely evaluate the performances of CN-based similarity indices in link prediction for very large heterogeneous networks. Specifically, we propose a fast...

Online social networks like Facebook recommend new friends to users based on an explicit social network that users build by adding each other as friends. The majority of earlier work in link prediction infers new interactions between users by mainly focusing on a single network type. However, users also form several implicit social networks through their daily interactions like commenting on pe...

Online social networks continue to witness a tremendous growth both in terms of the number of registered users and their mutual interactions. In this paper, we focus on online signed social networks where positive interactions among the users signify friendship or approval, whereas negative interactions indicate antagonism or disapproval. We introduce a novel problem which we call the link labe...

Interpersonal ties are responsible for the structure of social networks and the transmission of information through these networks. Different types of social ties have essentially different influence on people. Awareness of the types of social ties can benefit many applications such as recommendation and community detection. For example, our close friends tend to move in the same circles that w...

We propose a nonparametric link prediction algorithm for a sequence of graph snapshots over time. The model predicts links based on the features of its endpoints, as well as those of the local neighborhood around the endpoints. This allows for different types of neighborhoods in a graph, each with its own dynamics (e.g, growing or shrinking communities). We prove the consistency of our estimato...

Online social network services has become one of the most influential and key source of service providing, information/knowledge sharing and many other Internet based activities. The rapid growth of social networks shows the increasing popularity of these services among the users. The growth of social networks occurs as a result of adding new users and new links between users. The emergence of ...

Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks. In the previous work (Xu et al, 2016 [31]), we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight, and propose a weighted prediction index based on ...

The accuracy of link-quality estimators (LQE) is missioncritical in many application scenarios in wireless sensor networks (WSN), since the link-quality metric is used for routing decisions or neighborhood formation. Link-quality estimation must offer validity for different timescales. Existing LQEs describe and approximate the current quality in a single value only. This method leads to a limi...

Protein networks have a great importance in biological activities. ProteinProtein interaction occurs when two or more proteins interact together to carry out some biological activities. For example signals from the exterior of a cell are mediated to the interior through these interactions. Identification of these interaction have a great significance in understanding complex diseases and also f...