PPT-Estimating Clustering Coefficients and Size of Social Networks via Random Walk

Stephen J Hardiman Capital Fund Management France Liran Katzir Advanced Technology Labs Microsoft Research Israel Research was conducted while the author was unaffiliated Motivation Social Networks. For a downward travelling P wave, for the most general case:. Where the first term on the RHS is the P-wave displacement component and the second term is . the shear-wave . displacement component. Reflection Coefficients. Dmitri Krioukov. CAIDA/UCSD. M. . . Á. . Serrano, M. . Bogu. ñá. . UNT, March 2011. Percolation. Percolation is one of the most fundamental and best-studied critical phenomena in nature. In networks: the critical parameter is often average degree . Draft slides. Background. Consider a social graph G=(V, E), where |V|= n and |E|= m . Girvan and Newman’s algorithm for community detection runs . in O(m. 2. n) time. , and . O(n. 2. ) space. .. The . Christian Sohler. joint work with Artur Czumaj and Pan Peng. Very. Large Networks. Examples. Social. . networks. The World Wide Web. Cocitation. . graphs. Coauthorship. . graphs. Data . size. GigaByte. Where the first term on the RHS is the P-wave displacement component and the second term is . the shear-wave . displacement component. Reflection Coefficients. and where both shear stress,. and as well as normal stress is continuous across the boundary:. What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Important in evaluating the health of ecosystem:. Is organism threatened or endangered….need protection? Wolves. Over-running habitat….. Should Population be thinned ? Deer. Count Individuals. Best for stationary organisms. ). Prof. . Ralucca Gera, . Applied Mathematics Dept.. Naval Postgraduate School. Monterey, California. rgera@nps.edu. Excellence Through Knowledge. Learning Outcomes. I. dentify . network models and explain their structures. 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values.. What is clustering?. Grouping set of documents into subsets or clusters.. The Goal of clustering algorithm is:. To create clusters that are coherent internally, but clearly different from each other. Authors: . Kexiang. Wang, . Zhifang. Sui, et al.. Organization: Peking University. Speaker: . Kexiang. Wang. E-mail: wkx@pku.edu.cn. Outline. Overview of Our Paper. Aim. We propose the adjustable affinity-preserving random walk method for generic and query-focused multi-document summarization to enforce the .