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Discovering Colored Network Motifs.

, and . CompleNet, volume 549 of Studies in Computational Intelligence, page 107-118. Springer, (2014)

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A Scalable Parallel Approach for Subgraph Census Computation., , and . Euro-Par Workshops (2), volume 8806 of Lecture Notes in Computer Science, page 194-205. Springer, (2014)Parallel Calculation of Subgraph Census in Biological Networks., , and . BIOINFORMATICS, page 56-65. INSTICC Press, (2010)g-tries: an efficient data structure for discovering network motifs., and . SAC, page 1559-1566. ACM, (2010)Network Node Label Acquisition and Tracking., , and . EPIA, volume 7026 of Lecture Notes in Computer Science, page 418-430. Springer, (2011)Towards a faster network-centric subgraph census., and . ASONAM, page 264-271. ACM, (2013)TensorCast: Forecasting Time-Evolving Networks with Contextual Information., , and . IJCAI, page 5199-5203. ijcai.org, (2018)G-Tries: a data structure for storing and finding subgraphs., and . Data Min. Knowl. Discov., 28 (2): 337-377 (2014)Event detection in evolving networks., , and . CASoN, page 26-32. IEEE, (2012)Scalable subgraph counting using MapReduce., and . SAC, page 1574-1581. ACM, (2017)TensorCast: Forecasting with Context Using Coupled Tensors (Best Paper Award)., , and . ICDM, page 71-80. IEEE Computer Society, (2017)