Date and LocationMar 15, 2017 - 2:00pm to 3:00pm
Exposure to ideologically diverse news and opinion on Facebook (presented by Harshitha Chidananda, Computer Science)
Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130-1132.
Exposure to news, opinion and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using de-identified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks, and examine the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed, and further studied users’ choices to click through to ideologically discordant content. Compared to algorithmic ranking, individuals’ choices about what to consume had a stronger effect limiting exposure to cross-cutting content.
Lean Algebraic Multigrid (LAMG): Fast Graph Laplacian Linear Solver (Journal Version) (presented by Steven Munn, Computer Science)
Livne, O. E., & Brandt, A. (2012). Lean algebraic multigrid (LAMG): Fast graph Laplacian linear solver. SIAM Journal on Scientific Computing, 34(4), B499-B522.
Laplacian matrices of graphs arise in large-scale computational applications such as semisupervised machine learning; spectral clustering of images, genetic data, and web pages; transportation network flows; electrical resistor circuits; and elliptic partial differential equations discretized on unstructured grids with finite elements. A lean algebraic multigrid (LAMG) solver of the symmetric linear system $Ax=b$ is presented, where $A$ is a graph Laplacian. LAMG's run time and storage are empirically demonstrated to scale linearly with the number of edges. LAMG consists of a setup phase, during which a sequence of increasingly coarser Laplacian systems is constructed, and an iterative solve phase using multigrid cycles. General graphs pose algorithmic challenges not encountered in traditional multigrid applications. LAMG combines a lean piecewise-constant interpolation, judicious node aggregation based on a new node proximity measure (the affinity), and an energy correction of coarse-level systems. This results in fast convergence and substantial setup and memory savings. A serial LAMG implementation scaled linearly for a diverse set of 3774 real-world graphs with up to 47 million edges, with no parameter tuning. LAMG was more robust than the UMFPACK direct solver and combinatorial multigrid (CMG), although CMG was faster than LAMG on average. Our methodology is extensible to eigenproblems and other graph computations.