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Maximizing Social Influence in Nearly Optimal Time
TLDR
This work addresses the algorithmic problem of finding a set of k initial seed nodes in a network so that the expected size of the resulting cascade is maximized, under the standard independent cascade model of network diffusion.
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
TLDR
This paper proposes a new optimization algorithm called Entropy-SGD for training deep neural networks that is motivated by the local geometry of the energy landscape and compares favorably to state-of-the-art techniques in terms of generalization error and training time.
GRAPH LIMITS AND EXCHANGEABLE RANDOM GRAPHS
We develop a clear connection between de Finetti’s theorem for exchangeable arrays (work of Aldous–Hoover–Kallenberg) and the emerging area of graph limits (work of Lovász and many coauthors). Along
Directed scale-free graphs
TLDR
A model for directed scale-free graphs that grow with preferential attachment depending in a natural way on the in- and out-degrees is introduced, reproducing observed properties of the worldwide web.
Convergent Sequences of Dense Graphs I: Subgraph Frequencies, Metric Properties and Testing
We consider sequences of graphs (Gn) and define various notions of convergence related to these sequences: “left convergence” defined in terms of the densities of homomorphisms from small graphs into
Moments of Two-Variable Functions and the Uniqueness of Graph Limits
AbstractFor a symmetric bounded measurable function W on [0, 1]2 and a simple graph F, the homomorphism density $$t(F,W) = \int _{[0,1]^{V (F)}} \prod_ {i j\in E(F)} W(x_i, x_j)dx .$$ can be thought
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
TLDR
A large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives, and the impact on occupation classification of including explicit gender indicators in different semantic representations of online biographies.
Local Computation of PageRank Contributions
TLDR
This work gives an efficient local algorithm that computes an ε-approximation of the contribution vector for a given vertex by adaptively examining O(1/ε) vertices and gives a local approximation algorithm for the primitive defined above.
Convergent Sequences of Dense Graphs II. Multiway Cuts and Statistical Physics
We consider sequences of graphs (Gn) and dene various notions of convergence related to these sequences including \left-convergence," dened in terms of the densities of homomorphisms from small
Dynamics of bid optimization in online advertisement auctions
We consider the problem of online keyword advertising auctions among multiple bidders with limited budgets, and study a natural bidding heuristic in which advertisers attempt to optimize their
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