Information spreading in context
Proceedings of the 20th international conference on World wide web
Benefits of bias: towards better characterization of network sampling
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
It's who you know: graph mining using recursive structural features
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning diffusion probability based on node attributes in social networks
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Propagation and immunization in large networks
XRDS: Crossroads, The ACM Magazine for Students - Big Data
Rise and fall patterns of information diffusion: model and implications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Interacting viruses in networks: can both survive?
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding and managing cascades on large graphs
Proceedings of the VLDB Endowment
Gelling, and melting, large graphs by edge manipulation
Proceedings of the 21st ACM international conference on Information and knowledge management
Effective immunization of online networks: a self-similar selection approach
Information Technology and Management
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Given a large graph, like a computer network, which k nodes should we immunize (or monitor, or remove), to make it as robust as possible against a computer virus attack? We need (a) a measure of the ‘Vulnerability’ of a given network, b) a measure of the ‘Shield-value’ of a specific set of k nodes and (c) a fast algorithm to choose the best such k nodes. We answer all these three questions: we give the justification behind our choices, we show that they agree with intuition as well as recent results in immunology. Moreover, we propose Net Shield, a fast and scalable algorithm. Finally, we give experiments on large real graphs, where Net Shield achieves tremendous speed savings exceeding 7 orders of magnitude, against straightforward competitors.