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

Author : leventiser | Published Date : 2020-08-26

Stephen J Hardiman Capital Fund Management France Liran Katzir Advanced Technology Labs Microsoft Research Israel Research was conducted while the author was

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Estimating Clustering Coefficients and Size of Social Networks via Random Walk: Transcript

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. Presented by Changqing Li. Mathematics. Probability. Statistics. What. . is a Random Walk?. An Intuitive understanding. : . A series of movement which direction and size are randomly decided (e.g., . CS648. . Lecture 9. Random Sampling. part-I. (Approximating a parameter). 1. Overview. of the Lecture. Randomization Framework for . estimation. of . a parameter. Number of balls . from a bag. Size of transitive closure . . the. Cluster . Structure. . of. Graphs. Christian . Sohler. joint. . work. . with. Artur . Czumaj. . and. . Pan Peng. Very. Large Networks. Examples. Social. . networks. The World Wide Web. First half based on slides by . Kentaro Toyama,. Microsoft Research, India. And their applications to Web. Networks—Physical & Cyber. Typhoid Mary. (Mary Mallon). Patient Zero. (Gaetan Dugas). Applications of Network Theory. 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 . TJTSD66: Advanced Topics in Social Media. (Social . Media . Mining). Dr. WANG, Shuaiqiang @ CS & IS, JYU. Email: . shuaiqiang.wang@jyu.fi. Homepage: . http://users.jyu.fi/~swang/. Why should I use network models?. 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 . David . Harel. and . Yehuda. . Koren. KDD 2001. Introduction. Advances in database technologies resulted in huge amounts of spatial data. The characteristics of spatial data pose several difficulties for clustering algorithms.. 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. (SIGIR2010). IBM Research Lab. Ido. . Guy,Naama. . Zwerdling. Inbal. . Ronen,David. . Carmel,Erel. . Uziel. Social Networks and Discovery(. SaND. ). Direct entity-entity relations. Recommendation Algorithm. Random Graphs. Random graphs. Erdös-Renyi. model . One of several models …. Presents a theory of how social webs are formed.. Start with a set of isolated nodes. Connect each pair of nodes with a probability. Absorbing Random Walks. Link Prediction. Why does the Power Method work?. If a matrix R is real and symmetric, it has real eigenvalues and eigenvectors: . r is the rank of the matrix. The vector space of R is the set of vectors that can be written as a linear combination of its rows (or columns). Social Networks. What is a social network?. A graph metaphor for studying the relationships/interactions among a group of people. People. : vertices/nodes. Relationship. : edges . System. : network, graph . 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 .

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