The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Towards time-aware link prediction in evolving social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling Information Diffusion in Implicit Networks
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
A MapReduce-Based Maximum-Flow Algorithm for Large Small-World Network Graphs
ICDCS '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems
Predicting a Social Network Structure Once a Node Is Deleted
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
IEEE Transactions on Information Theory
Multi-objective restructuring in social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Social networks are dynamic structures that contain a set of entities and links. In such a dynamic environment, a specific node or a group of nodes can play an important role in the information flow transmission within the network and therefore, its disappearance may lead to a disconnected network or a breakdown in the information flow. The objective of this paper is to extend our previous work on managing a node disappearance to handling the disappearance of a group of nodes. The proposed approach relies on the role played by a group of nodes to conduct network changes and maintain the network connected while restoring the information flow with a similar quality as before the group disappearance. We consider two situations (only one versus many communities) and categorize groups of nodes into three classes (scattered, contiguous and hybrid). Hence, we manage a group disappearance with respect to its class and the network topology by adding new links in a parsimonious way and finding a substitute for a leaving group. Our approach differs from existing link prediction solutions by the fact that it uses the information flow quality as a key performance indicator to identify the potential links to add and/or the possible substitute to a disappearing group. We implement a prototype by using an open source social network analysis library (NetworkX) and we validate our solution through experiments. The results show the benefits of our solution in terms of response time and the number of added links.