SPECIAL THANKS to Martin Pasquier. an explorer and experimenter of social media, digital, and IRL stuff in Singapore, because of your information, i got this free offer to my email : """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" Hi Wisnu Rahardjo, You’ve just joined a community of hundreds of thousands of students who are taking courses, and changing the face of higher education. you can take courses from the top universities, for free. Take classes taught by brilliant professors, interact with students from around the world, and explore tons of interesting questions through our many course offerings. We are committed to making education available to everyone, and we’ll be constantly working to bring you new classes from the best universities. """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" I enrolled for a class about Social Network Analysis from University of Michigan with Lada Adamic as the guru. Session : Oct 7th 2013 (9 weeks long). This class will give me a new certificates and new stuff to be learn. Also thanks to Jay CL, it was a very good share, i have learn some of new technique about Guirrellas. I will to do the implementation of it very soon. ---------------------------------------------------------------------------------------------- University of Michigan Social Media Course Syllabus Week 1: What are networks and what use is it to study them? Concepts: nodes, edges, adjacency matrix, one and two-mode networks, node degree Activity: Upload a social network (e.g. your Facebook social network into Gephi and visualize it ). Week 2: Random network models: Erdos-Renyi and Barabasi-Albert Concepts: connected components, giant component, average shortest path, diameter, breadth-first search, preferential attachment Activities: Create random networks, calculate component distribution, average shortest path, evaluate impact of structure on ability of information to diffuse Week 3: Network centrality Concepts: betweenness, closeness, eigenvector centrality (+ PageRank), network centralization Activities: calculate and interpret node centrality for real-world networks (your Facebook graph, the Enron corporate email network, Twitter networks, etc.) Week 4: Community Concepts: clustering, community structure, modularity, overlapping communities Activities: detect and interpret disjoint and overlapping communities in a variety of networks (scientific collaborations, political blogs, cooking ingredients, etc.) Week 5: Small world network models, optimization, strategic network formation and search Concepts: small worlds, geographic networks, decentralized search Activity: Evaluate whether several real-world networks exhibit small world properties, simulate decentralized search on different topologies, evaluate effect of small-world topology on information diffusion. Week 6: Contagion, opinion formation, coordination and cooperation Concepts: simple contagion, threshold models, opinion formation Activity: Evaluate via simulation the impact of network structure on the above processes Week 7: Cool and unusual applications of SNA Hidalgo et al. : Predicting economic development using product space networks (which countries produce which products) Ahn et al., and Teng et al.: Learning about cooking from ingredient and flavor networks Lusseau et al.: Social networks of dolphins others TBD Activity: hands-on exploration of these networks using concepts learned earlier in the course Week 8: SNA and online social networks Concepts: how services such as Facebook, LinkedIn, Twitter, CouchSurfing, etc. are using SNA to understand their users and improve their functionality Activity: read recent research by and based on these services and learn how SNA concepts were applied
Posted on: Fri, 21 Jun 2013 05:34:14 +0000
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