Wellcome to Maximimi's library,
You can find here all papers liked or uploaded by Maximimitogether with brief user bio and description of her/his academic activity.
I will read the following papers.
- Computational inference of gene regulatory networks: Approaches, limitations and opportunitie
- Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma
Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis
The non-convex Burer–Monteiro approach works on smooth semidefinite programs
Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality
Influence maximization in complex networks through optimal percolation
- Sparse Convolutional Neural Networks
- A fast and simple algorithm for training neural probabilistic language models
- Adding One Neuron Can Eliminate All Bad Local Minima
I read the following papers.
2019-2020
- Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
- Karp-Sipser based kernels for bipartite graph matching
- Speedup Graph Processing by Graph Ordering
- Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering
2018-2019:
- SWeG: Lossless and Lossy Summarization of Web-Scale Graphs
- Smoothed Analysis: An Attempt to Explain the Behavior of Algorithms in Practice
- Are stable instances easy?
- Hierarchical Taxonomy Aware Network Embedding
- Billion-scale Network Embedding with Iterative Random Projection
- HARP: Hierarchical Representation Learning for Networks
- Layered Label Propagation: A MultiResolution Coordinate-Free Ordering for Compressing Social Networks
2017-2018:
- Link Prediction in Graph Streams
- The Community-search Problem and How to Plan a Successful Cocktail Party
- A Nonlinear Programming Algorithm for Solving Semidefinite Programs via Low-rank Factorization
- Deep Learning
- Reducing the Dimensionality of Data with Neural Networks
- Representation Learning on Graphs: Methods and Applications
- Improved Approximation Algorithms for MAX k-CUT and MAX BISECTION
- Cauchy Graph Embedding
- Phase Transitions in Semidefinite Relaxations
- Graph Embedding Techniques, Applications, and Performance: A Survey
- VERSE: Versatile Graph Embeddings from Similarity Measures
- Hierarchical Clustering Beyond the Worst-Case
- Scalable Motif-aware Graph Clustering
- Practical Algorithms for Linear Boolean-width
- New Perspectives and Methods in Link Prediction
- In-Core Computation of Geometric Centralities with HyperBall: A Hundred Billion Nodes and Beyond
- Diversity is All You Need: Learning Skills without a Reward Function
- When Hashes Met Wedges: A Distributed Algorithm for Finding High Similarity Vectors
- Fast Approximation of Centrality
- Indexing Public-Private Graphs
- On the uniform generation of random graphs with prescribed degree sequences
- Linear Additive Markov Processes
- ESCAPE: Efficiently Counting All 5-Vertex Subgraphs
- The k-peak Decomposition: Mapping the Global Structure of Graphs
- A Fast and Provable Method for Estimating Clique Counts Using Turán’s Theorem
Comments:
Feel free to start an awesome discussion.
Typos:
- "is defined as the the minimum"
Comments:
Dynamic setting:
One of the motivations for the suggested framework is related to the dynamic nature of the reachability graph. However, the suggested framework does not seem to be particularly effective in a dynamic setting (need to recompute the sets "S" from scratch if the reachability graph is modified).
Recent work is dedicated to solving the k-center problem in a dynamic setting: "Fully Dynamic k-Center Clustering, Chan et al., WWW2018". The "asymmetric k-center problem" being a subproblem solved in the suggested framework, a more efficient algorithm in a dynamic setting seems possible.
Effectiveness of greedy:
Nice to see that the greedy heuristic leads to a very good approximation on real-world instances for the considered problem (the exact result is known solving an integer program). On the contrary, an algorithm (Ullman and Yannakakis) having good theoretical guarantees performs poorly on the same real-world instances.
Typos:
- "In Table 6.4, the column labeled...": should be "In Table 2, ..."
Comments: