Analyzing Work: One Approach Problem: What’s missing currently? What does the work aim to understand? Approach: How does the work go about addressing the.

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Presentation transcript:

Analyzing Work: One Approach Problem: What’s missing currently? What does the work aim to understand? Approach: How does the work go about addressing the problem? Contribution: What new insights, techniques, or systems does this work provide?

Things To Think About Is the writing easy to comprehend? What could have been improved? Did you learn any lessons about what to do/not do? Do you think the problem is important? Why? What are the merits/drawbacks of the approach? What do you think of the contributions? Is there anything that surprised you? Anything you felt was missing? What did you learn? Do you feel you have new insights because you read this? What did you like? Dislike?

Affiliation Networks (2008) Problem: current social network models don’t match reality – No densification – No diameter shrinkage Approach: analytic, drawing from sociology Contribution: model (better) matching reality – Mathematically tractable – Algorithmically useful

Group Formation in Large Social Networks (2006) Problem: how do communities form and evolve? Approach: – Measure – examine social network structure – Model – features & decision trees, burst analysis Contribution: – Interesting observations: topics move before people – Can do better prediction w/ more structure.

Structure and Evolution of Online Social Networks (2006) Problem: how do communities form and evolve? Approach: – Measure – migration patterns – Model – biased preferential attachment Contribution: – Density of network goes through 3 stages w/ growth – Well connected core, with outlying weakly connected components formed around “stars” – Model reproduces structures of two very different networks

Statistical Properties of Community Structure in Large Soc/Info Nets (2008) Problem: We don’t know much about statistical properties of network communities Approach: Measurement & modeling – Five-part story: interaction graph, group interaction hypothesis, objective function to measure “group-ness”, clustering algorithm, evaluation. – Conductance -> Network Community Profile Plot Contribution: – Learned some very surprising things about real networks (although are they that novel given earlier findings?) – Another generative model (better than previous?)