Open Reading Group — Papersᵞ

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### Upcoming readings: No upcoming readings for now... ### Past Readings: - 21/05/2018 [Graph Embedding Techniques, Applications, and Performance: A Survey](https://papers-gamma.link/paper/52) - 14/05/2018 [A Fast and Provable Method for Estimating Clique Counts Using Turán’s Theorem](https://papers-gamma.link/paper/24) - 07/05/2018 [VERSE: Versatile Graph Embeddings from Similarity Measures](https://papers-gamma.link/paper/48) - 30/04/2018 [Hierarchical Clustering Beyond the Worst-Case](https://papers-gamma.link/paper/45) - 16/04/2018 [Scalable Motif-aware Graph Clustering](https://papers-gamma.link/paper/18) - 02/04/2018 [Practical Algorithms for Linear Boolean-width](https://papers-gamma.link/paper/40) - 26/03/2018 [New Perspectives and Methods in Link Prediction](https://papers-gamma.link/paper/28/New%20Perspectives%20and%20Methods%20in%20Link%20Prediction) - 19/03/2018 [In-Core Computation of Geometric Centralities with HyperBall: A Hundred Billion Nodes and Beyond](https://papers-gamma.link/paper/37) - 12/03/2018 [Diversity is All You Need: Learning Skills without a Reward Function](https://papers-gamma.link/paper/36) - 05/03/2018 [When Hashes Met Wedges: A Distributed Algorithm for Finding High Similarity Vectors](https://papers-gamma.link/paper/23) - 26/02/2018 [Fast Approximation of Centrality](https://papers-gamma.link/paper/35/Fast%20Approximation%20of%20Centrality) - 19/02/2018 [Indexing Public-Private Graphs](https://papers-gamma.link/paper/19/Indexing%20Public-Private%20Graphs) - 12/02/2018 [On the uniform generation of random graphs with prescribed degree sequences](https://papers-gamma.link/paper/26/On%20the%20uniform%20generation%20of%20random%20graphs%20with%20prescribed%20d%20egree%20sequences) - 05/02/2018 [Linear Additive Markov Processes](https://papers-gamma.link/paper/21/Linear%20Additive%20Markov%20Processes) - 29/01/2018 [ESCAPE: Efficiently Counting All 5-Vertex Subgraphs](https://papers-gamma.link/paper/17/ESCAPE:%20Efficiently%20Counting%20All%205-Vertex%20Subgraphs) - 22/01/2018 [The k-peak Decomposition: Mapping the Global Structure of Graphs](https://papers-gamma.link/paper/16/The%20k-peak%20Decomposition:%20Mapping%20the%20Global%20Structure%20of%20Graphs)

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The paper is about a scalable alternative to $k$-order Markov process called k-LAMP. k-LAMP only needs $nnz(T)+k$ (where $T$ is the transition matrix and $nnz(T)$ is the number of nonzero entries in $T$) parameters, while $k$-order Markov process needs as many parameters as the number of paths of length $k+1$ in $T$. A generalized version called $k$-GLAMP is also suggested, it needs $k*nnz(T)+k$ parameters. An experimental comparison to Markov process and LSTM (Long-Short-Term-Memory) seems convincing. ### Typos: - page 5, Theorem 11: "\n this version.)"

Open Reading Group at 2018-05-10 16:51:28

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