Social network analysis (SNA) is a core pursuit of analyzing social networks today. In
addition to the usual statistical techniques of data analysis, these networks are investigated …

Social networks are becoming larger and more complex as new ways of collecting social
interaction data arise (namely from online social networks, mobile devices sensors,...) …

Link prediction is an important task in data mining, which has widespread applications in
social network research. Given a social network, the objective of this task is to predict future …

In recommender systems user preferences can be fairly dynamic, as users tend to exploit a
wide range of items and modify their tastes accordingly over time. In this paper, we model …

This study deals with missing link prediction, the problem of predicting the existence of
missing connections between entities of interest. We approach the problem as filling in …

We propose a link prediction method for evolving networks. Our method first computes a
number of different node similarity scores (eg Common Neighbor, Preferential Attachment …

In recent years, context information has been widely used in recommender systems. Tensor
factorization is an effective method to process high-dimensional information. However, data …

In this paper, we present our work on clustering and prediction of temporal evolution of
global congestion configurations in a large-scale urban transportation network. Instead of …

In this paper, we focus on the problem of context-aware recommendation using tensor
factorization. Traditional tensor-based models in context-aware recommendation scenario …

Nowadays we are faced with fast growing and permanently evolving data, including social
networks and sensor data recorded from smart phones or vehicles. Temporally evolving …