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.
- [Quasi-Succinct Indices](https://papers-gamma.link/paper/130)
- [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.
### 2019-2020
- [Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs](https://papers-gamma.link/paper/162)
- [Karp-Sipser based kernels for bipartite graph matching](https://papers-gamma.link/paper/160)
- [Speedup Graph Processing by Graph Ordering](https://papers-gamma.link/paper/159)
- [Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering](https://papers-gamma.link/paper/154)
### 2018-2019:
- [SWeG: Lossless and Lossy Summarization of Web-Scale Graphs](https://papers-gamma.link/paper/139)
- [Smoothed Analysis: An Attempt to Explain the Behavior of Algorithms in Practice](https://papers-gamma.link/paper/129)
- [Are stable instances easy?](https://papers-gamma.link/paper/128)
- [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)

☆

1

I'm in love with this paper!
### Non-worst-case analysis:
In practice, "we are not interested in all problem instances, but only in those which can actually occur in reality."
"The notion of stability [...] is a concrete way to formalize the notion that the only instances of interest are those for which small perturbation in the data (which may reflect e.g. some measurement errors) do not change the optimal partition of the graph."
This Stable Analysis is different from "Smoothed Analysis" where "one shows that the hard instances form a discrete and isolated subset of the input space".
### Open problems:
**Conjecture:** There exists some constant $\gamma^{*}$ such that $\gamma^{*}$-stable instances can be solved in polynomial time.
**Question:** it is shown that $\gamma$-stable instances, with $\gamma>\sqrt{\Delta n}$, can be solved in polynomial time. Can this be improved (without further assumptions such as a lower bound on the minimum degree)? As $\sqrt{\Delta n}$ is usually large, this may not be useful in practice.
**Question:** How does the algorithm "FindMaxCut" (page 6) perform in practice on real-world instances???
**Question:** How about the greedy heuristic?: Start from a random cut and do passes on the nodes moving each node to the other side of the cut if the size of the cut increases until convergence. Does it have some guarantee on $\gamma$-stable instances?
### Extended spectral clustering:
"Let D be a diagonal matrix. Think of W + D as the weighted adjacency matrix of a graph, with loops added. Such loops do not change the weight of any cut, so that regardless of what D we choose, a cut is maximal in W iff it is maximal in W + D. Furthermore, it is not hard to see that W is $\gamma$-stable, iff W + D is. Our approach is to first find a “good” D, and then take the spectral partitioning of W + D as the maximal cut. These observations suggest the following question: Is it true that for every $\gamma$-stable instance W with γ large enough there exists a diagonal D for which extended spectral partitioning solves Max-Cut? If so, can such a D be found efficiently? Below we present certain sufficient conditions for these statements."
I did not fully understand what is presented below that paragraph. Let G be a $\gamma$-stable graph, how do I get $D$?
### Goemans-Williamson algorithm:
The approximation guarantee of the Goemans-Williamson algorithm is better on $\gamma$-stable instances than in general.
### Random model:
With high probability, the extended specral clustering leads to the optimal cut on $\gamma$-stable instances generated from a certain random model for $\gamma\geq 1+\Omega(\sqrt{\frac{\log(n)}{n}})$.
### Typos:
- page 3, Proposition 2.1: " A graph G graph"
- page 4: "this follows from Definition 2.1", should be "Proposition 2.1".
- page 5, Definition 2.2: should be "E" instead of "e" in the equation.
- page 5: "which must to be on the" and "of the optional cut" -> "optimal".
- page 8: "we multiply it be a PSD matrix"

☆

0

Nice paper building on top of [the WebGraph framework](https://papers-gamma.link/paper/31) and [Chierichetti et al.](https://papers-gamma.link/paper/126) to compress graphs.
### Approximation guarantee
I read: "our algorithm is inspired by a theoretical approach
with provable guarantees on the final quality, and it is designed to directly optimize the resulting compression ratio.".
I misunderstood initially, but the proposed algorithm actually does not have any provable approximation guarantee other than the $\log(n)$ one (which is also obtained by a random ordering of the nodes).
Designing an algorithm with (a better) approximation guarantee for minimizing "MLogA", "MLogGapA" or "BiMLogA" seems to be a nice open problem.
### Objectives
Is there any better objective than "MLogA", "MLogGapA" or "BiMLogA" to have a proxy of the compression obtained by the BV-framework?
Is it possible to directly look for an ordering that minimizes the size of the output of BV compression algorithm?

## Comments: