Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
ACM SIGKDD Explorations Newsletter
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
The Time-Series Link Prediction Problem with Applications in Communication Surveillance
INFORMS Journal on Computing
Towards time-aware link prediction in evolving social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
New perspectives and methods in link prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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Link prediction is a well-known task from the Social Network Analysis field that deals with the occurrence of connections in a network. It consists of using the network structure up to a given time in order to predict the appearance of links in a close future. The majority of previous work in link prediction is focused on the application of proximity measures (e.g., path distance, common neighbors) to non-connected pairs of nodes at present time in order to predict new connections in the future. New links can be predicted for instance by ordering the pairs of nodes according to their proximity scores. A limitation usually observed in previous work is that only the current state of the network is used to compute the proximity scores, without taking any temporal information into account (i.e., a static graph representation is adopted). In this work, we propose a new proximity measure for link prediction based on the concept of temporal events. In our work, we defined a temporal event related to a pair of nodes according to the creation, maintenance or interruption of the relationship between the nodes in consecutive periods of time. We proposed an event-based score which is updated along time by rewarding the temporal events observed between the pair of nodes under analysis and their neighborhood. The assigned rewards depend on the type of temporal event observed (e.g., if a link is conserved along time, a positive reward is assigned). Hence, the dynamics of links as the network evolves is used to update representative scores to pairs of nodes, rewarding pairs which formed or preserved a link and penalizing the ones that are no longer connected. In the performed experiments, we evaluated the proposed event-based measure in different scenarios for link prediction using co-authorship networks. Promising results were observed when the proposed measure was compared to both static proximity measures and a time series approach (a more competitive method) that also deploys temporal information for link prediction.