Papers Collection


Graph Learning


Machine Learning on Graphs: A Model and Comprehensive Taxonomy

Classical Approach of Node Embedding

Combinatorial Optimization using Machine Learning


Combinatorial Optimization and Reasoning with Graph Neural Networks

Machine Learning for Combinatorial Optimization: a Methodological Tour d’Horizon


k-core decoposition (distributed direction)

  1. Distributed k-core Decomposition

  2. A distributed k-core Decomposition on Spark

  3. kcoreTheoriesAndApplications

  4. Parallel and Streaming Algorithms for K-Core Decomposition

  1. A SURVEY: survey of community search over big graph

Number Partition

  1. Optimal Multi-Way Number Partitioning
  2. Bounds on Multiprocessing Timing Anomalies SIAM

Graph Partition

  1. balancedGraphPartition(Classic)
  2. Streaming Graph Partitioning: An Experimental Study
  3. Streaming Balanced Graph Partitioning Algorithms for Random
  4. A Study of Partitioning Policies for Graph Analytics on Large-scale Distributed Platforms
  5. Dynamic Scaling for Parallel Graph Computations
  6. PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs
  7. A survey of partition papers blog
  8. HDRF: Stream-Based Partitioning for Power-Law Graphs
  9. Windows-based Streaming Partitioning Algorithm(A very good example of windows based method)

Algorithm over large graph


  1. Realtime Top-k Personalized PageRank over Large Graphs on GPUs

K-core Decomposition

  1. An Experimental Comparison of Partitioning Strategies in Distributed Graph Processing(metioned in 3.3)
  2. Slides from Uwaterloo about graph partition
  3. Efficient Core Decomposition in Massive Networks


ITAM-24:Intelligent Assistant Medical Diagnosis Based on Online Interactive Information

Gini Methodology Appllied to Macroeconomics Forecasting


Algebra & Basic Maths

Matrix Computation

  1. Matrix Cookbook
  2. Old and New Matrix Algebra Useful for Statistics


  1. Algorithms by Jeff Ericson

  2. Parallel Algorithms CMU

Computer Archs

Prob & Stats

The Elements of Statistical Learning

Programming Languages

Graph & Network Science

  1. Graph Representation Learning