Publications

Journal Publications

Vipin Vijayan and Tijana Milenković, “Aligning dynamic networks with DynaWAVE”, Bioinformatics, 34(10):pp. 1795–1798, May 2018. [doi, code]

Vipin Vijayan, Dominic Critchlow, and Tijana Milenković, “Alignment of dynamic networks”, Bioinformatics, 33(14): pp. i80–i89, July 2017. [doi, code]

Vipin Vijayan, Eric Krebs, Lei Meng, and Tijana Milenković, “Pairwise versus multiple network alignment”, Bioinformatics, (Under revision, 2018). [arxiv]

Vipin Vijayan and Tijana Milenković, “Multiple network alignment via multiMAGNA++”, IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), doi: 10.1109/TCBB.2017.2740381, pp. 1–14, 2017. [doi, code]

Vipin Vijayan, Vikram Saraph, and Tijana Milenković, “MAGNA++: Maximizing Accuracy in Global Network Alignment via both node and edge conservation”, Bioinformatics, 31(14): pp. 2409–2411, July 2015. [doi, code]

John H. Simonetti, Michael Kavic, Djordje Minic, Umair Surani and Vipin Vijayan, “A Precision Test for an Extra Spatial Dimension Using Black-hole-Pulsar Binaries”, The Astrophysical Journal Letters, 737(2): pp. 1–9, March 2012. [doi]

Conference Publications

Vipin Vijayan, Dominic Critchlow, and Tijana Milenković, “Alignment of dynamic networks”, International Conference on Intelligent Systems for Molecular Biology and the European Conference on Computational Biology (ISMB/ECCB 2017), Prague, Czech Republic, July 21–25, 2017 (Oral presentation/talk, 2017 ISMB/ECCB Travel Fellowship Award).

Vipin Vijayan and Tijana Milenković, “Multiple network alignment via multiMAGNA++”, 15th International Workshop on Data Mining in Bioinformatics 2016 (BIOKDD 2016), in conjunction with ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD 2016), San Francisco, CA, August 13–17, 2016 (Oral presentation/talk); International Conference on Intelligent Systems for Molecular Biology (ISMB 2016), Orlando, Florida, July 8–12, 2016 (Poster presentation).

Vipin Vijayan and Wesam Sakla, “An empirical comparison of K-SVD and GMRA for dictionary learning”, Proc. SPIE Defense and Commercial Sensing Conference: Optical Pattern Recognition XXVI, 94770J, pp. 1–9, Baltimore, MD, April 20, 2015. [doi]

Vipin Vijayan, Kevin Bowyer, and Patrick Flynn, “3D Twins and Expression Challenge”, in Proc. 13th International Conference on Computer Vision (ICCV 2011), BeFIT Workshop, Barcelona, Spain, pp. 2100–2105, November 13, 2011 (Oral presentation/talk). [doi]

Vipin Vijayan, Kevin Bowyer, Patrick Flynn, Di Huang, Liming Chen, Omar Ocegueda, Shishir K. Shah, and Ioannis A. Kakadiaris, “Twins 3D Face Recognition Challenge”, in Proc. International Joint Conference on Biometrics (IJCB 2011), Washington DC, pp. 1–7, October 11–13, 2011 (Oral presentation/talk, Best Paper Award Nominee). [doi]

Other Research Publications

Vipin Vijayan. “Novel Algorithmic Contributions and Evaluation Frameworks for Network Alignment with Applications in Computational Biology”. Doctoral Thesis, University of Notre Dame, May 2017. [link]

Vipin Vijayan. “Three Dimensional Face Recognition of Identical Twins”. Master’s Thesis, University of Notre Dame, July 2012. [link]

Code

Github

Link

DynaWAVE and WAVE

Dynamic (or temporal) networks are networks whose interactions are time dependent. DynaWAVE aligns two dynamic networks while taking temporal information into account. WAVE aligns two static networks. [code]

IsoRank and PageRank

IsoRank is a network alignment algorithm that produces a node mapping from one network to another by calculating the topological similarity of nodes between the two networks. IsoRank calculates topological similarity by calculating the performing PageRank on the product graph of the two graphs. [code]

Ray casting for deformable triangular meshes

Matlab code that provides an interface to OPCODE. It creates a tree structure of a triangular mesh that allows us to perform ray-mesh intersections efficiently. The tree can be modified efficiently when the mesh is deformed, as opposed to recreating the tree from the mesh. [code]

Fast SVD and PCA

Matlab code that does truncated Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) faster than using Matlab’s svd and svds functions. [code]