Numerical Computer Methods Part D

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Readings matrices, spectra, random walks : Dan Spielman's spectral graph theory course. Eigenvalues of graphs. Eikmeier and D. KDD, Mathematics of Networks. Random walks and diffusion on networks. Masuda, M. Porter, and R. Physics Reports, Ugander et al. Kwak et al. WWW, Leskovec and E. Leskovec, J. Kleinberg, and C.

Watts and S. Travers and S. Sociometry, Scale-free networks are rare. Broido and A. Nature Communications, Tracing information flow on a global scale using Internet chain-letter data. Liben-Nowell and J. Using selection bias to explain the observed structure of Internet diffusions. Golub and M. The structure of positive interpersonal relations in small groups. Davis and S. Sociological Theories in Progress, Vol. Internet Mathematics, Power-Law Distributions in Empirical Data. Clauset, C. Shalizi, and M. Contemporary Physics, Fosdick, D.

Larremore, J. Nishimura, J. Stochastic blockmodels and community structure in networks. Karrer and M. PRE, Mixed Membership Stochastic Blockmodels. Airoldi, D. Blei, S. Fienberg, and E.

Affiliation Networks. Lattanzi and D. Coin-flipping, ball-dropping, and grass-hopping for generating random graphs from matrices of edge probabilities. Ramani, N. Eikmeier, and D. SIAM Review, to appear. Ulrike von Luxburg. Statistics and Computing, Mining Large Graphs Section 7. Lecture notes, On Clusterings: Good, Bad and Spectral. Kannan, S. Vempala, and A.

Computational Methods for Numerical Analysis with R - CRC Press Book

On Spectral Clustering: Analysis and an Algorithm. Ng, Jordan, and Weiss. Kernel k-means, Spectral Clustering and Normalized Cuts. Dhillon, Guan, and Kulis.

A hierarchical method for generating low‐energy conformers of a protein‐ligand complex

Modularity and community structure in networks. Spectral methods for community detection and graph partitioning. Simple, direct and efficient multi-way spectral clustering. Damle, V. Minden, and L. Information and Inference, Co-clustering documents and words using Bipartite Spectral Graph Partitioning. Fixing two weaknesses of the Spectral Method. Readings more general community detection : Communities in Networks. Porter, J. Onnela, and P. Notices of the AMS, Community detection in networks: A user guide. Fortunato and D. The ground truth about metadata and community detection in networks.

Peel, D. Larremore, and A. Science Advances, Zhu, Z. Ghahraman, and J. Transductive Learning via Spectral Graph Partitioning. Learning with Local and Global Consistency. Zhou et al. Monophily in social networks introduces similarity among friends-of-friends. Altenburger and J.

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Nature Human Behavior, Hoff, Raftery, and Handcock. JASA, K Henderson et al.

Modeling of loops in proteins: a multi-method approach

Perozzi, R. Al-Rfou, and S. Tang et al. Grover and J. Hamilton, R. Ying, and J. Qiu et al. WSDM, Milo et al. Superfamilies of Evolved and Designed Networks. Motifs in temporal networks. Paranjape, A. Benson, and J. Readings applications Higher-order organization of complex networks.

Benson, D. Gleich, and J.

Introduction to Numerical Methods and Errors

Higher-order clustering in networks. Yin, A. Ugander, L. Backstrom, and J. Modeling interactome: scale-free or geometric?

A hierarchical method for generating low‐energy conformers of a protein‐ligand complex

Corneil, and I. Bioinformatics, Structure and function of the feed-forward loop network motif.

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  5. Mangan and U. Mangan, A. Zaslaver, and U. Readings counting algorithms : Counting Triangles.

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    Stanford CS lecture notes, Arboricity and Subgraph Listing Algorithms. Chiba and T. Main-memory triangle computations for very large sparse power-law graphs. Theoretical Computer Science, Seshadhri, A. Pinar, and T. SDM, Jha, C. Seshadhri, and A. Pinar, C. Seshadhri, and V. Sampling Methods for Counting Temporal Motifs. Liu, A. Benson, and M. Psychometrika, Technique for Analyzing Overlapping Memberships. Sociological Methodology, Page, S. Brin, R. Motwani and T. Stanford Technical Report, Authoritative Sources in a Hyperlinked Environment.

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    Numerical Computer Methods Part D Numerical Computer Methods Part D
    Numerical Computer Methods Part D Numerical Computer Methods Part D
    Numerical Computer Methods Part D Numerical Computer Methods Part D
    Numerical Computer Methods Part D Numerical Computer Methods Part D
    Numerical Computer Methods Part D Numerical Computer Methods Part D

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