[TKDD'23] Demo code of the paper entitled "Towards a Better Trade-Off between Quality and Efficiency of Community Detection: An Inductive Embedding Method across Graphs", which has been accepted by ACM TKDD

Judicious Graph Partitioning

A cutting-edge toolkit engineered for high-precision image segmentation using Boykov-Kolmogorov algorithm and CIDE2000 formula

Assignment for Advanced Software Analysis, KNU (2021)

Kernighan-Lin Algorithm implementation in python. Kernighan-Lin Algorithm is a graph partitioning algorithm that optimizes the cut size between two subsets of nodes.

A coding project aimed at exploring new ways of algorithmic learning using evolutionary techniques such as genetic algorithms and crossover

Custom unembedding techniques for quantum annealers

Designing and implementing my own method to partition a graph into communities.

The Kernighan–Lin algorithm is a heuristic algorithm for finding partitions of graphs. The algorithm has important applications in the layout of digital circuits and components in VLSI.

DRL models for graph partitioning and sparse matrix ordering.

Implementation of Kernighan-Lin graph partitioning algorithm in Python

An implementation of "EdMot: An Edge Enhancement Approach for Motif-aware Community Detection" (KDD 2019)

A NetworkX implementation of Label Propagation from a "Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks" (Physical Review E 2008).