PPT-Computing Classic Closeness Centrality, at Scale

Edith Cohen Joint with Thomas Pajor Daniel Delling Renato Werneck Microsoft Research Very Large Graphs Model many types of relations and interactions edges between entities nodes. Uni processor computing can be called centralized computing brPage 3br mainframe computer workstation network host network link terminal centralized computing distributed computing A distributed system is a collection of independent computers interc C57528 ataly urek Depts Biomedical Informatics Computer Science and Engineering Electrical and Computer Engineering The Ohio State University Email sariyuce1osuedu kamerbmiosuedu esauleunccedu umitbmiosuedu Abstract Centrality metrics have shown to Centrality measures. Centrality measures. Centrality is related to the potential importance of a node. Some. nodes have greater “influence” over others compared to the rest,. or are more easily accessible to other, or act as a go-between in. Hexmoor. Department of Computer Science. Southern Illinois University Carbondale. Network Theory:. Computational Phenomena and Processes. Social Network Analysis . Degree, . Indegree. , . Outdegree. Centrality. Betweenness. and Graph partitioning. Chapter 3, from D. Easley and J. Kleinberg book. Section 10.2.4, from A. . Rajaraman. , J. Ullman, J. . Leskovec. Centrality Measures. Not all nodes are equally important. Microdata. with a Robust Privacy Guarantee. Jianneng. Cao,. . National University of Singapore, now at I. 2. R. Panagiotis. . Karras. ,. Rutgers University. Table 2. . Voter registration list. Quasi-identifier (QI):. Classic Code Colour Classic Code Colour Classic Code Colour Classic Code 1244 Charles L. Cartledge. Michael L. Nelson. Old Dominion University. Department of Computer Science. Norfolk, VA 23529 USA. Why the problem is of interest. Picking apart the title. Preservation. Graph. Suitability. TJTSD66: Advanced Topics in Social Media. Dr. WANG, Shuaiqiang @ CS & IS, JYU. Email: . shuaiqiang.wang@jyu.fi. Homepage: . http://users.jyu.fi/~swang/. (Social . Media . Mining). Klout. Why Do We Need Measures?. (Gephi and Python). By: Ralucca Gera, NPS. Excellence Through Knowledge. Python with Networkx. Excellence Through Knowledge. Python code . Use . Metrics.py . from dropbox or . my . website. 3. NetworkX Reference. Centrality. , Similarity, and . Influence. Edith Cohen . Tel Aviv University. Graph Datasets:. Represent relations between “things”. Bowtie structure of the Web . Broder. et. al. 2001. Dolphin interactions. Excellence Through Knowledge. A periodic table of centralities. 2. An interactive periodic table of centralities: . http://. schochastics.net/sna/periodic.html. Different types of centralities:. 3. Source: Discovering Sets of Key Players in Social Networks – Daniel Ortiz-Arroyo – Springer 2010/. Some pages are adapted. from Dan Ryan, . Mills College. B. etweenness. Centrality. I. ntuition. : how many pairs of individuals would have to go through you in order to reach one another in the minimum number of hops. Quality:. what makes a node important (central). Mathematical. Description. Appropriate Usage. Identification. Lots of one-hop. connections from . The number of vertices that . influences directly.