Social networks play a major role in today’s society, they have shaped the unfolding of social relationships. To analyze networks dynamics, link prediction i.e., predicting potential new links between actors, is concerned with inspecting networks topology evolution over time. A key issue to be addressed is the imperfection of real world social network data which are usually missing, noisy, or p...

Abstract. Predicting potential links between nodes in a network is a problem of great practical interest. Link prediction is mostly based on graph-based features and, recently, on approaches that consider the semantics of the domain. However, there is uncertainty in these predictions; by modeling it, one can improve prediction results. In this paper, we propose an algorithm for link prediction ...

Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat” file format, but rather involve complex relationships among objects. In this paper, we propose the application of our methodology for...

Inspired by traditional link prediction and to solve the problem of recommending friends in social networks, we introduce the personalized link prediction in this paper, in which each individual will get equal number of diversiform predictions. While the performances of many classical algorithms are not satisfactory under this framework, thus new algorithms are in urgent need. Motivated by prev...

Question-Answering Bulletin Boards (QABB), such as Yahoo! Answers and Windows Live QnA, are gaining popularity recently. Questions are submitted on QABB and let somebody in the internet answer them. Communications on QABB connect users, and the overall connections can be regarded as a social network. If the evolution of the network can be predicted, it is quite useful for encouraging communicat...

Online Social Networks are growing exponentially due to which a lot of researchers are working on Social Network analysis. Link Prediction is a task of predicting new links that may occur in future in the social network. The link prediction problem has generated a lot of interest due its widespread applicability across many domains. We conducted a study on the different methods that have been d...

Many data sets can be represented as a sequence of interactions between entities—for example communications between individuals in a social network, protein-protein interactions or DNA-protein interactions in a biological context, or vehicles’ journeys between cities. In these contexts, there is often interest in making predictions about future interactions, such as who will message whom. A pop...

In the paper, we consider the problem of link prediction in time-evolving graphs. We assume that certain graph features, such as the node degree, follow a vector autoregressive (VAR) model and we propose to use this information to improve the accuracy of prediction. Our strategy involves a joint optimization procedure over the space of adjacency matrices and VAR matrices which takes into accoun...

Many systems of interest are best described using networks that represent binary relationships among their elements. Link prediction aims to infer the link formation process by predicting missed or future relationships based on currently observed connections. Different techniques and measures have been proposed in the literature to solve this problem. Similarity-based local methods achieve high...

Network modeling can be approached using either discriminative or probabilistic models. In the task of link prediction a probabilistic model will give a probability for the existence of a link; while in some scenarios this may be beneficial, in others a hard discriminative boundary needs to be set. Hence the use of a discriminative classifier is preferable. In domains such as image analysis and...