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Introduction to Social Network Methods: Table of Contents

Introduction to Social Network Methods: Table of Contents
Robert A. Hanneman and Mark Riddle Introduction to social network methods Table of contents About this book This on-line textbook introduces many of the basics of formal approaches to the analysis of social networks. You are invited to use and redistribute this text freely -- but please acknowledge the source. Hanneman, Robert A. and Mark Riddle. 2005. Table of contents: Preface1. Related:  Graph theory, graph practice

Overview of Bulbs, a Python Framework for Graph Databases like Neo4j | Bulbflow A Python framework for graph databases. Bulbs is an open-source Python persistence framework for graph databases and the first piece of a larger Web-development toolkit that will be released in the upcoming weeks. It’s like an ORM for graphs, but instead of SQL, you use the graph-traveral language Gremlin to query the database. Bulbs supports pluggable backends, and you can use it to connect to either Neo4j Server or Rexster. Neo4j Server is Neo4j‘s official server. This means your code is portable because you can to plug into different graph database backends without worrying about vendor lock in. Bulbs was developed in the process of building Whybase, a startup that will open for preview later this year. You can use Bulbs from within any Python Web-development framework, including Flask, Pyramid, and Django. Code Example Here’s how you model domain objects: And here’s how you use the models to create and connect domain objects: Why Graphs? Graph are an elegant way of storing relational data.

How to do social network analysis Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes. SNA provides both a visual and a mathematical analysis of human relationships. Management consultants use this methodology with their business clients and call it Organizational Network Analysis [ONA]. ONA allows you to x-ray your organization and reveal the managerial nervous system that connects everything. To understand networks and their participants, we evaluate the location and grouping of actors in the network. We look at a social network -- the "Kite Network" above -- developed by David Krackhardt, a leading researcher in social networks. Degree Centrality Betweenness Centrality Closeness Centrality Network Centralization Network Reach Not all network paths are created equal.

UCINET Software UCINET 6 for Windows is a software package for the analysis of social network data. It was developed by Lin Freeman, Martin Everett and Steve Borgatti. It comes with the NetDraw network visualization tool. If you use the software, please cite it. Here is a sample citation: Borgatti, S.P., Everett, M.G. and Freeman, L.C. 2002. Ucinet for Windows: Software for Social Network Analysis. Personal Learning Networks for Educators: 10 Tips I often begin my workshop on personal learning networks for educators by asking these questions: Who is in your learning network? Who do you learn from on a regular basis? Who do you turn to for your own professional development? Some educators are lucky enough to learn from their coworkers or colleagues at their site. I usually ask these questions at conferences, which are frequently only annual events – and rare treats for many educators. Learning to Network and Networking to Learn1.

Social Network Analysis Social Network Analysis: Introduction and Resources What is Social Network Analysis? Network Data Collection and Representation Network Theories Analysis of Network Data Software Applications Books and Journals Article References Selected Online SNA Portals Ulrike Gretzel November, 2001 What is Social Network Analysis? Social network analysis is based on an assumption of the importance of relationships among interacting units. Actors and their actions are viewed as interdependent rather than independent, autonomous units Relational ties (linkages) between actors are channels for transfer or "flow" of resources (either material or nonmaterial) Network models focusing on individuals view the network structural environment as providing opportunities for or constraints on individual action Network models conceptualize structure (social, economic, political, and so forth) as lasting patterns of relations among actors Wasserman, S. and K. Scott, J., 1992, Social Network Analysis. Index Network Theories

5 Graph Databases to Consider Of the major categories of NoSQL databases - document-oriented databases, key-value stores and graph databases - we've given the least attention to graph databases on this blog. That's a shame, because as many have pointed out it may become the most significant category. Graph databases apply graph theory to the storage of information about the relationships between entries. Google has its own graph computing system called Pregel (you can find the paper on the subject here), but there are several commercial and open source graph databases available. Neo4j This is one of the most popular databases in the category, and one of the only open source options. Neo Technologies cites several customers, though none of them are household names. Here's a fun illustration of how relationship data in graph databases works, from an InfoQ article by Neo Technologies COO Peter Neubauer: FlockDB FlockDB was created by Twitter for relationship related analytics. AllegroGraph GraphDB InfiniteGraph Others

INSNA home page @insna.org Bibliographic coupling Figure visualizing bibliographic coupling between Documents A and B. Bibliographic coupling, like Co-citation, is a similarity measure that uses citation analysis to establish a similarity relationship between documents. Bibliographic coupling occurs when two works reference a common third work in their bibliographies. It is an indication that a probability exists that the two works treat a related subject matter.[1] Two documents are bibliographically coupled if they both cite one or more documents in common. Similarly, two authors are bibliographically coupled if the cumulative reference lists of their respective oeuvres each contain a reference to a common document, and their coupling strength also increases with the citations to other documents that their share. Bibliographic coupling can be useful in a wide variety of fields, since it helps researchers find related research done in the past. History[edit] The concept of bibliographic coupling was introduced by M. Applications[edit]

International Network for Social Network Analysis - INSNA Graduate Programs with Emphasis in Social Network Analysis All data is provided by INSNA Member participation and is maintained by said members. The accuracy of the information is the responsibility of the supplying member. Please contact INSNA administration if there is any offensive or inappropriate material present. Masters in Language and Communication Department of Linguistics, University of Georgetown University Ticknor, Kathryn The M.A. in Language and Communication is a professionally-oriented program within the Linguistics Department at Georgetown University. LINKS Center for organizational social network analysis Department of Management, University of U of Kentucky Borgatti, Steve The Management Department at the University of Kentucky focuses on social networks, both in terms of faculty research and in terms of the Ph.D. program. The CSCS PhD programme is a four year thematic, structured programme commencing in the Academic Year 2009/2010. Social Network Analysis Courses

Graph Graph Description In mathematics and computer science, graph theory studies networks of connected nodes and their properties. A graph can be used to visualize related data, or to find the shortest path from one node to another node for example. Central concepts in graph theory are: Node: a block of information in the network.Edge: a connection between two nodes (can have a direction and a weight).Centrality: determining the relative importance of a node.Clustering: partitioning nodes into groups. The NodeBox Graph library includes algorithms from NetworkX for betweenness centrality and eigenvector centrality, Connelly Barnes' implementation of Dijksta shortest paths (here) and the spring layout for JavaScript by Aslak Hellesoy and Dave Hoover (here). For those of you looking for the old Graph library built on Boost, it can still be found here. Download Documentation The library has a cool example of a visual browser for WordNet. How to get the library up and running graph = ximport("graph")

libSNA: the library for Social Network Analysis

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