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)

☆

1

## Interesting paper!
Code is coming soon: http://spcl.inf.ethz.ch/Research/Performance/LogGraph/
The method can reduce the size needed to store the input graph by 35% compared to the adjacency array format, while not making computations slower.
Several compression frameworks are suggested in the paper. All of them use fixed length compression codes contrarily the WebGraph framework.
One of the compression frameworks leads to a space of $\sum_{v\in V} \log(\widehat{N_v})\cdot d_v+\log(\log(\widehat{N_v}))$ bits (cf. section 3.2, $\widehat{N_v}$ is the maximum ID of the neighbors of node $v$) as it uses $\log(\widehat{N_v})$ bits to store the $d_v$ neighbors of each node $v$ and $\log(\log(\widehat{N_v}))$ bits to store the length of the codes used for each node $v$. Note that an additional cost is needed in order to have a direct access to the neighbors of each node (this is done using succinct bit vectors (it seems to lead to a total number of bits close to $n\cdot \log(m)$)). The nodes can be renumbered such that the number of bits is minimized.
### Approaches the compression ratio of WebGraph
I read that the suggested compression method "approaches the compression ratio of the established WebGraph compression library". What does it mean exactly? The Webgraph compression library can compress web graphs using less than 2 bits per link. The suggested compression framework seems to be far from that compression ratio.
So I think that what is meant is that on graphs other than web graphs the suggested method approaches the compression ratio of the Webgraph library. Am I correct?
### Java VS C++
I read "We use the WebGraph original tuned Java implementation for gathering the data on compression ratios but, as Log(Graph) is developed in C++, we use a proof-of-a-concept C++ implementation of WG schemes for a fair C++ based performance analysis.".
I do not understand. This is because the original Java implementation is slower than a C++ implementation? Which C++ implementation was used for WG?
### Cost function in section 3.6
The quantity to minimize should be $\sum_{v\in V} \log(\widehat{N_v})\cdot d_v$ (cf. section 3.2, $\widehat{N_v}$ is the maximum ID of the neighbors of node $v$), that is intuitively minimizing $\widehat{N_v}$ for nodes with high $d_v$.
It is not clear to me why this other counter-intuitive objective is used: $\sum_{v\in V} \widehat{N_v}\cdot \frac{1}{d_v}$.
### Webgraph decompression impacts running time
I agree that referencing nodes can impact the running time (especially if there are chains of references). However, it is possible to use the WebGraph framework without using referencing, that is only using gap encoding with variable length instantaneous codes.
I think that doing that does not impact significantly the running time, does it?
### Typos and minors
- Section 4.2.: "There are exactly $Q$ bit vectors of length with n ones and the storage lower bound is $Q$ bits." -> the storage lower bound is $\log(Q)$ bits.

Some people discuss the paper (and troll the paper authors) on hackernews:
https://news.ycombinator.com/item?id=18081978

## Comments: