Wellcome to Maximimi's library,

  You can find here all papers liked or uploaded by Maximimi
  together with brief user bio and description of her/his academic activity.


[Link to my homepage](https://sites.google.com/view/danisch/home) ## I will read the following papers. - [PageRank as a Function of the Damping Factor](https://papers-gamma.link/paper/106) - [Graph Stream Algorithms: A Survey](https://papers-gamma.link/paper/102) - [Network Sampling: From Static to Streaming Graphs](https://papers-gamma.link/paper/122) - [The Protein-Folding Problem, 50 Years On](https://papers-gamma.link/paper/78) - [Computational inference of gene regulatory networks: Approaches, limitations and opportunitie](https://papers-gamma.link/paper/77) - [Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma](https://papers-gamma.link/paper/79) - [Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis](https://papers-gamma.link/paper/76) - [The non-convex Burer–Monteiro approach works on smooth semidefinite programs](https://papers-gamma.link/paper/80) - [Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality](https://papers-gamma.link/paper/81) - [Influence maximization in complex networks through optimal percolation](https://papers-gamma.link/paper/70) - [Motifs in Temporal Networks](https://papers-gamma.link/paper/61) - [Deep Sparse Rectifier Neural Networks](https://papers-gamma.link/paper/69) - [Sparse Convolutional Neural Networks](https://papers-gamma.link/paper/67) - [A fast and simple algorithm for training neural probabilistic language models](https://papers-gamma.link/paper/58) - [Adding One Neuron Can Eliminate All Bad Local Minima](https://papers-gamma.link/paper/71) ## I read the following papers. ### 2018-2019: - [Hierarchical Taxonomy Aware Network Embedding](https://papers-gamma.link/paper/116) - [Billion-scale Network Embedding with Iterative Random Projection](https://papers-gamma.link/paper/110) - [HARP: Hierarchical Representation Learning for Networks](https://papers-gamma.link/paper/109/) - [Layered Label Propagation: A MultiResolution Coordinate-Free Ordering for Compressing Social Networks](https://papers-gamma.link/paper/105) ### 2017-2018: - [Link Prediction in Graph Streams](https://papers-gamma.link/paper/101) - [The Community-search Problem and How to Plan a Successful Cocktail Party](https://papers-gamma.link/paper/74) - [A Nonlinear Programming Algorithm for Solving Semidefinite Programs via Low-rank Factorization](https://papers-gamma.link/paper/55) - [Deep Learning](https://papers-gamma.link/paper/68) - [Reducing the Dimensionality of Data with Neural Networks](https://papers-gamma.link/paper/65) - [Representation Learning on Graphs: Methods and Applications](https://papers-gamma.link/paper/60) - [Improved Approximation Algorithms for MAX k-CUT and MAX BISECTION](https://papers-gamma.link/paper/56) - [Cauchy Graph Embedding](https://papers-gamma.link/paper/53) - [Phase Transitions in Semidefinite Relaxations](https://papers-gamma.link/paper/57) - [Graph Embedding Techniques, Applications, and Performance: A Survey](https://papers-gamma.link/paper/52) - [VERSE: Versatile Graph Embeddings from Similarity Measures](https://papers-gamma.link/paper/48) - [Hierarchical Clustering Beyond the Worst-Case](https://papers-gamma.link/paper/45) - [Scalable Motif-aware Graph Clustering](https://papers-gamma.link/paper/18) - [Practical Algorithms for Linear Boolean-width](https://papers-gamma.link/paper/40) - [New Perspectives and Methods in Link Prediction](https://papers-gamma.link/paper/28/New%20Perspectives%20and%20Methods%20in%20Link%20Prediction) - [In-Core Computation of Geometric Centralities with HyperBall: A Hundred Billion Nodes and Beyond](https://papers-gamma.link/paper/37) - [Diversity is All You Need: Learning Skills without a Reward Function](https://papers-gamma.link/paper/36) - [When Hashes Met Wedges: A Distributed Algorithm for Finding High Similarity Vectors](https://papers-gamma.link/paper/23) - [Fast Approximation of Centrality](https://papers-gamma.link/paper/35/Fast%20Approximation%20of%20Centrality) - [Indexing Public-Private Graphs](https://papers-gamma.link/paper/19/Indexing%20Public-Private%20Graphs) - [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) - [Linear Additive Markov Processes](https://papers-gamma.link/paper/21/Linear%20Additive%20Markov%20Processes) - [ESCAPE: Efficiently Counting All 5-Vertex Subgraphs](https://papers-gamma.link/paper/17/ESCAPE:%20Efficiently%20Counting%20All%205-Vertex%20Subgraphs) - [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) - [A Fast and Provable Method for Estimating Clique Counts Using Turán’s Theorem](https://papers-gamma.link/paper/24)

Comments:

The idea of coarse-graining the graph by merging "similar" nodes (using edge collapsing or star collapsing) before applying a graph embedding method is interesting. Bringing that idea to the next level by building a hierarchy of coarse-grained graphs is also relevant. The hierarchy of coarse-grained graphs allows finding a better initialization for embedding methods relying on deep learning. The two ways of collapsing (edge collapsing or star collapsing) are very simple and more sophisticated ways are not suggested: for instance based on modular decomposition or community detection. Maybe such more advanced ways lead to even better results. The experimental section shows that coarse-graining the input graph before applying a graph embedding methods leads to a "better" embedding. Indeed, using such an embedding improves the performance of the classification task compared to using the embedding obtained without coarse-graining. However, neither a theoretical justification of this fact is provided, nor an intuition. How about other machine learning tasks? For instance, does the suggested method also help for clustering and link prediction? The method does not make the embedding faster even though this might have been expected since the graphs are smaller. Figure 4: I do not understand in what the various level as relevant. To me, no level looks like the wished embedding. The graphs used in the experiments are extremely small (less than 30k nodes). It is not clear how the method would perform on large and sparse graphs, say 1G edges 100M nodes (for instance these: http://snap.stanford.edu/data/#communities).
Read the paper, add your comments…

Comments:

The idea of coarse-graining the graph by merging "similar" nodes (using edge collapsing or star collapsing) before applying a graph embedding method is interesting. Bringing that idea to the next level by building a hierarchy of coarse-grained graphs is also relevant. The hierarchy of coarse-grained graphs allows finding a better initialization for embedding methods relying on deep learning. The two ways of collapsing (edge collapsing or star collapsing) are very simple and more sophisticated ways are not suggested: for instance based on modular decomposition or community detection. Maybe such more advanced ways lead to even better results. The experimental section shows that coarse-graining the input graph before applying a graph embedding methods leads to a "better" embedding. Indeed, using such an embedding improves the performance of the classification task compared to using the embedding obtained without coarse-graining. However, neither a theoretical justification of this fact is provided, nor an intuition. How about other machine learning tasks? For instance, does the suggested method also help for clustering and link prediction? The method does not make the embedding faster even though this might have been expected since the graphs are smaller. Figure 4: I do not understand in what the various level as relevant. To me, no level looks like the wished embedding. The graphs used in the experiments are extremely small (less than 30k nodes). It is not clear how the method would perform on large and sparse graphs, say 1G edges 100M nodes (for instance these: http://snap.stanford.edu/data/#communities).
Read the paper, add your comments…

Comments:

Read the paper, add your comments…
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